WO2021201384A1

WO2021201384A1 - Apparatus and method for processing point cloud data

Info

Publication number: WO2021201384A1
Application number: PCT/KR2020/019483
Authority: WO
Inventors: 박유선; 오세진
Original assignee: 엘지전자 주식회사
Priority date: 2020-04-03
Filing date: 2020-12-31
Publication date: 2021-10-07

Abstract

A method for processing point cloud data according to embodiments comprises the steps of: encoding point cloud data into a geometry representing the positions of one or more points; encoding attributes of the one or more points; and transmitting a bitstream including the encoded point cloud data. The point cloud data can be encoded and transmitted.

Description

Point cloud data processing apparatus and method

Embodiments provide point cloud content to provide users with various services such as VR (Virtual Reality), AR (Augmented Reality, Augmented Reality), MR (Mixed Reality), and autonomous driving service. provide a way

The point cloud content is content expressed as a point cloud, which is a set of points (points) belonging to a coordinate system representing a three-dimensional space. Point cloud content can express three-dimensional media, and provides various services such as VR (Virtual Reality), AR (Augmented Reality), MR (Mixed Reality), and autonomous driving service. used to provide However, tens of thousands to hundreds of thousands of point data are needed to express point cloud content. Therefore, a method for efficiently processing a large amount of point data is required.

Embodiments provide an apparatus and method for efficiently processing point cloud data. Embodiments provide a point cloud data processing method and apparatus for solving latency and encoding/decoding complexity.

However, it is not limited only to the above-described technical problems, and the scope of rights of the embodiments may be extended to other technical problems that can be inferred by those skilled in the art based on the entire description.

Accordingly, in order to efficiently process point cloud data, a method for transmitting point cloud data according to embodiments includes encoding the point cloud data and transmitting a bitstream including the encoded point cloud data.

A method for receiving point cloud data according to embodiments includes receiving a bitstream including point cloud data and decoding the point cloud data.

The apparatus and method according to the embodiments may process point cloud data with high efficiency.

The apparatus and method according to the embodiments may provide a high quality point cloud service.

The apparatus and method according to the embodiments may provide point cloud content for providing universal services such as a VR service and an autonomous driving service.

BRIEF DESCRIPTION OF THE DRAWINGS The drawings are included to further understand the embodiments, and the drawings, together with a description related to the embodiments, represent the embodiments. For a better understanding of the various embodiments described below, reference should be made to the following description of the embodiments in connection with the following drawings in which like reference numerals include corresponding parts throughout the drawings.

1 shows an example of a point cloud content providing system according to embodiments.

2 is a block diagram illustrating an operation of providing point cloud content according to embodiments.

3 shows an example of a point cloud video capture process according to embodiments.

4 shows an example of a point cloud encoder according to embodiments.

5 illustrates an example of a voxel according to embodiments.

6 shows an example of an octree and an occupancy code according to embodiments.

7 shows an example of a neighbor node pattern according to embodiments.

8 shows an example of a point configuration for each LOD according to embodiments.

9 shows an example of a point configuration for each LOD according to embodiments.

10 shows an example of a point cloud decoder according to embodiments.

11 shows an example of a point cloud decoder according to embodiments.

12 is an example of a transmission apparatus according to embodiments.

13 is an example of a receiving apparatus according to embodiments.

14 shows an example of a structure capable of interworking with a method/device for transmitting and receiving point cloud data according to embodiments.

15 is a block diagram illustrating an apparatus for transmitting point cloud data according to embodiments.

16 shows a prediction tree structure according to embodiments.

17 shows an example of a method of calculating a modified predicted value according to embodiments.

18 shows an example of a method of changing a prediction tree structure according to embodiments.

19 shows the structure of a bitstream according to embodiments.

20 illustrates a SPS (Sequential Parameter Set) structure of point cloud data according to embodiments.

21 illustrates a TPS (Tile Parameter Set) structure of point cloud data according to embodiments.

22 illustrates a Geometry Parameter Set (GPS) structure of point cloud data according to embodiments.

23 illustrates an Attribute Parameter Set (APS) structure of point cloud data according to embodiments.

24 illustrates a Geometry Slice Header (GSH) structure of point cloud data according to embodiments.

25 is a block diagram illustrating an apparatus for receiving point cloud data according to embodiments.

26 is an example of a flowchart illustrating a method of transmitting point cloud data according to embodiments.

27 is an example of a flowchart illustrating a method for receiving point cloud data according to embodiments.

Preferred embodiments of the embodiments will be specifically described, examples of which are shown in the accompanying drawings. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The detailed description below with reference to the accompanying drawings is intended to describe preferred embodiments of the embodiments rather than merely representing embodiments that may be implemented according to the embodiments of the embodiments. The following detailed description includes details to provide a thorough understanding of the embodiments. However, it will be apparent to one skilled in the art that embodiments may be practiced without these details.

described in the Examples. Therefore, the embodiments are not the mere name or meaning of the term, but most of the terms intended and used are selected from general ones widely used in the field, but some terms are arbitrarily selected by the applicant, and their meanings are It should be understood based on the semantics in detail in the description.

The point cloud content providing system shown in FIG. 1 may include a transmission device 10000 and a reception device 10004 . The transmitting device 10000 and the receiving device 10004 are capable of wired/wireless communication in order to transmit/receive point cloud data.

The transmission device 10000 according to embodiments may secure, process, and transmit a point cloud video (or point cloud content). According to embodiments, the transmission device 10000 may be a fixed station, a base transceiver system (BTS), a network, an Ariticial Intelligence (AI) device and/or system, a robot, an AR/VR/XR device and/or a server and the like. In addition, according to embodiments, the transmission device 10000 uses a radio access technology (eg, 5G NR (New RAT), LTE (Long Term Evolution)) to perform communication with a base station and/or other wireless devices; It may include robots, vehicles, AR/VR/XR devices, mobile devices, home appliances, Internet of Things (IoT) devices, AI devices/servers, and the like.

Transmission device 10000 according to embodiments is a point cloud video acquisition unit (Point Cloud Video Acquisition, 10001), a point cloud video encoder (Point Cloud Video Encoder, 10002) and / or a transmitter (Transmitter (or Communication module), 10003) ) contains

The point cloud video acquisition unit 10001 according to embodiments acquires the point cloud video through processing such as capturing, synthesizing, or generating. The point cloud video is point cloud content expressed as a point cloud that is a set of points located in a three-dimensional space, and may be referred to as point cloud video data or the like. A point cloud video according to embodiments may include one or more frames. One frame represents a still image/picture. Accordingly, the point cloud video may include a point cloud image/frame/picture, and may be referred to as any one of a point cloud image, a frame, and a picture.

The point cloud video encoder 10002 according to embodiments encodes the obtained point cloud video data. The point cloud video encoder 10002 may encode point cloud video data based on point cloud compression coding. Point cloud compression coding according to embodiments may include Geometry-based Point Cloud Compression (G-PCC) coding and/or Video based Point Cloud Compression (V-PCC) coding or next-generation coding. In addition, the point cloud compression coding according to the embodiments is not limited to the above-described embodiments. The point cloud video encoder 10002 may output a bitstream including encoded point cloud video data. The bitstream may include not only the encoded point cloud video data, but also signaling information related to encoding of the point cloud video data.

The transmitter 10003 according to embodiments transmits a bitstream including encoded point cloud video data. The bitstream according to the embodiments is encapsulated into a file or segment (eg, a streaming segment) and transmitted through various networks such as a broadcasting network and/or a broadband network. Although not shown in the drawing, the transmission device 10000 may include an encapsulation unit (or encapsulation module) that performs an encapsulation operation. Also, according to embodiments, the encapsulation unit may be included in the transmitter 10003 . According to embodiments, the file or segment may be transmitted to the receiving device 10004 through a network or stored in a digital storage medium (eg, USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.). The transmitter 10003 according to the embodiments may communicate with the receiving device 10004 (or a receiver 10005) through wired/wireless communication through networks such as 4G, 5G, and 6G. Also, the transmitter 10003 may perform a necessary data processing operation according to a network system (eg, a communication network system such as 4G, 5G, or 6G). Also, the transmission device 10000 may transmit encapsulated data according to an on demand method.

The receiving apparatus 10004 according to the embodiments includes a receiver (Receiver, 10005), a point cloud video decoder (Point Cloud Decoder, 10006), and/or a renderer (Renderer, 10007). According to embodiments, the receiving device 10004 uses a radio access technology (eg, 5G NR (New RAT), LTE (Long Term Evolution)) to communicate with a base station and/or other wireless devices, a device, a robot , vehicles, AR/VR/XR devices, mobile devices, home appliances, Internet of Things (IoT) devices, AI devices/servers, and the like.

The receiver 10005 according to embodiments receives a bitstream including point cloud video data or a file/segment in which the bitstream is encapsulated from a network or a storage medium. The receiver 10005 may perform a necessary data processing operation according to a network system (eg, a communication network system such as 4G, 5G, or 6G). The receiver 10005 according to embodiments may output a bitstream by decapsulating the received file/segment. Also, according to embodiments, the receiver 10005 may include a decapsulation unit (or a decapsulation module) for performing a decapsulation operation. Also, the decapsulation unit may be implemented as an element (or component) separate from the receiver 10005 .

The point cloud video decoder 10006 decodes a bitstream including point cloud video data. The point cloud video decoder 10006 may decode the point cloud video data according to an encoded manner (eg, a reverse process of the operation of the point cloud video encoder 10002 ). Accordingly, the point cloud video decoder 10006 may decode the point cloud video data by performing point cloud decompression coding, which is a reverse process of the point cloud compression. Point cloud decompression coding includes G-PCC coding.

The renderer 10007 renders the decoded point cloud video data. The renderer 10007 may output point cloud content by rendering audio data as well as point cloud video data. According to embodiments, the renderer 10007 may include a display for displaying the point cloud content. According to embodiments, the display may not be included in the renderer 10007 and may be implemented as a separate device or component.

An arrow indicated by a dotted line in the drawing indicates a transmission path of the feedback information obtained by the receiving device 10004 . The feedback information is information for reflecting the interactivity with the user who consumes the point cloud content, and includes user information (eg, head orientation information, viewport information, etc.). In particular, when the point cloud content is content for a service that requires interaction with a user (eg, autonomous driving service, etc.), the feedback information is provided by the content transmitting side (eg, the transmission device 10000) and/or the service provider can be passed on to According to embodiments, the feedback information may be used by the receiving device 10004 as well as the transmitting device 10000 or may not be provided.

The head orientation information according to the embodiments is information about the user's head position, direction, angle, movement, and the like. The receiving apparatus 10004 according to embodiments may calculate viewport information based on head orientation information. The viewport information is information about the area of the point cloud video that the user is looking at. A viewpoint is a point at which a user is watching a point cloud video, and may mean a central point of the viewport area. That is, the viewport is an area centered on a viewpoint, and the size and shape of the area may be determined by a Field Of View (FOV). Accordingly, the reception device 10004 may extract viewport information based on a vertical or horizontal FOV supported by the device in addition to the head orientation information. In addition, the receiving device 10004 checks the user's point cloud consumption method, the point cloud video area the user gazes at, the gaze time, and the like by performing a gaze analysis or the like. According to embodiments, the receiving device 10004 may transmit feedback information including the result of the gaze analysis to the transmitting device 10000 . Feedback information according to embodiments may be obtained during rendering and/or display. Feedback information according to embodiments may be secured by one or more sensors included in the receiving device 10004 . Also, according to embodiments, the feedback information may be secured by the renderer 10007 or a separate external element (or device, component, etc.). A dotted line in FIG. 1 shows a process of transferring feedback information secured by the renderer 10007 . The point cloud content providing system may process (encode/decode) the point cloud data based on the feedback information. Accordingly, the point cloud video data decoder 10006 may perform a decoding operation based on the feedback information. Also, the receiving device 10004 may transmit feedback information to the transmitting device 10000 . The transmitting device 10000 (or the point cloud video data encoder 10002 ) may perform an encoding operation based on the feedback information. Therefore, the point cloud content providing system does not process (encode / decode) all point cloud data, but efficiently processes necessary data (for example, point cloud data corresponding to the user's head position) based on the feedback information, and the user can provide point cloud content to

According to embodiments, the transmitting apparatus 10000 may be referred to as an encoder, a transmitting device, a transmitter, or the like, and the receiving apparatus 10004 may be referred to as a decoder, a receiving device, a receiver, or the like.

Point cloud data (processed in a series of acquisition/encoding/transmission/decoding/rendering) processed in the point cloud content providing system of FIG. 1 according to embodiments may be referred to as point cloud content data or point cloud video data. can According to embodiments, the point cloud content data may be used as a concept including metadata or signaling information related to the point cloud data.

The elements of the point cloud content providing system shown in FIG. 1 may be implemented by hardware, software, a processor and/or a combination thereof.

The block diagram of FIG. 2 shows the operation of the point cloud content providing system described in FIG. 1 . As described above, the point cloud content providing system may process point cloud data based on point cloud compression coding (eg, G-PCC).

The point cloud content providing system (for example, the point cloud transmission apparatus 10000 or the point cloud video acquisition unit 10001) according to embodiments may acquire a point cloud video (20000). A point cloud video is expressed as a point cloud belonging to a coordinate system representing a three-dimensional space. A point cloud video according to embodiments may include a Ply (Polygon File format or the Stanford Triangle format) file. When the point cloud video has one or more frames, the acquired point cloud video may include one or more Ply files. The Ply file contains point cloud data such as the point's geometry and/or attributes. Geometry includes positions of points. The position of each point may be expressed by parameters (eg, values of each of the X-axis, Y-axis, and Z-axis) representing a three-dimensional coordinate system (eg, a coordinate system including XYZ axes). The attribute includes attributes of points (eg, texture information of each point, color (YCbCr or RGB), reflectance (r), transparency, etc.). A point has one or more attributes (or properties). For example, one point may have one attribute of color, or two attributes of color and reflectance. According to embodiments, the geometry may be referred to as positions, geometry information, geometry data, and the like, and the attribute may be referred to as attributes, attribute information, attribute data, and the like. In addition, the point cloud content providing system (for example, the point cloud transmission device 10000 or the point cloud video acquisition unit 10001) receives points from information (eg, depth information, color information, etc.) related to the point cloud video acquisition process. Cloud data can be obtained.

The point cloud content providing system (for example, the transmission device 10000 or the point cloud video encoder 10002) according to embodiments may encode the point cloud data (20001). The point cloud content providing system may encode point cloud data based on point cloud compression coding. As described above, the point cloud data may include the geometry and attributes of the point. Accordingly, the point cloud content providing system may output a geometry bitstream by performing geometry encoding for encoding the geometry. The point cloud content providing system may output an attribute bitstream by performing attribute encoding for encoding the attribute. According to embodiments, the point cloud content providing system may perform attribute encoding based on geometry encoding. The geometry bitstream and the attribute bitstream according to the embodiments may be multiplexed and output as one bitstream. The bitstream according to embodiments may further include signaling information related to geometry encoding and attribute encoding.

The point cloud content providing system (for example, the transmission device 10000 or the transmitter 10003) according to the embodiments may transmit the encoded point cloud data (20002). As described with reference to FIG. 1 , the encoded point cloud data may be expressed as a geometry bitstream and an attribute bitstream. Also, the encoded point cloud data may be transmitted in the form of a bitstream together with signaling information related to encoding of the point cloud data (eg, signaling information related to geometry encoding and attribute encoding). In addition, the point cloud content providing system may encapsulate the bitstream for transmitting the encoded point cloud data and transmit it in the form of a file or segment.

The point cloud content providing system (eg, the receiving device 10004 or the receiver 10005) according to the embodiments may receive a bitstream including the encoded point cloud data. Also, the point cloud content providing system (eg, the receiving device 10004 or the receiver 10005) may demultiplex the bitstream.

The point cloud content providing system (for example, the receiving device 10004 or the point cloud video decoder 10005) may decode the encoded point cloud data (for example, a geometry bitstream, an attribute bitstream) transmitted as a bitstream. have. The point cloud content providing system (for example, the receiving device 10004 or the point cloud video decoder 10005) may decode the point cloud video data based on signaling information related to encoding of the point cloud video data included in the bitstream. have. The point cloud content providing system (eg, the receiving device 10004 or the point cloud video decoder 10005) may decode the geometry bitstream to restore positions (geometry) of the points. The point cloud content providing system may restore attributes of points by decoding an attribute bitstream based on the restored geometry. The point cloud content providing system (eg, the receiving device 10004 or the point cloud video decoder 10005) may reconstruct the point cloud video based on positions and decoded attributes according to the reconstructed geometry.

The point cloud content providing system (for example, the receiving device 10004 or the renderer 10007) according to the embodiments may render the decoded point cloud data (20004). The point cloud content providing system (for example, the receiving device 10004 or the renderer 10007) may render the geometry and attributes decoded through the decoding process according to various rendering methods according to the rendering method. Points of the point cloud content may be rendered as a vertex having a certain thickness, a cube having a specific minimum size centered at the vertex position, or a circle centered at the vertex position. All or part of the rendered point cloud content is provided to the user through a display (eg, VR/AR display, general display, etc.).

The point cloud content providing system (eg, the receiving device 10004) according to embodiments may secure feedback information (20005). The point cloud content providing system may encode and/or decode the point cloud data based on the feedback information. Since the operation of the feedback information and point cloud content providing system according to the embodiments is the same as the feedback information and operation described with reference to FIG. 1 , a detailed description thereof will be omitted.

3 shows an example of a point cloud video capture process of the point cloud content providing system described with reference to FIGS. 1 to 2 .

The point cloud content is an object located in various three-dimensional spaces (eg, a three-dimensional space representing a real environment, a three-dimensional space representing a virtual environment, etc.) and/or a point cloud video representing the environment (images and/or videos) are included. Accordingly, in the point cloud content providing system according to the embodiments, one or more cameras (eg, an infrared camera capable of securing depth information, color information corresponding to depth information) in order to generate point cloud content Point cloud video can be captured using an RGB camera that can extract The point cloud content providing system according to the embodiments may extract a shape of a geometry composed of points in a three-dimensional space from depth information, and extract an attribute of each point from color information to secure point cloud data. An image and/or an image according to embodiments may be captured based on at least one of an inward-facing method and an outward-facing method.

The left side of FIG. 3 shows an inward-pacing scheme. The inward-pacing method refers to a method in which one or more cameras (or camera sensors) located surrounding the central object capture the central object. The inward-facing method provides a 360-degree image of a point cloud content that provides a 360-degree image of a core object to the user (for example, a 360-degree image of an object (e.g., a core object such as a character, player, object, actor, etc.) to the user. VR/AR content).

The right side of FIG. 3 shows an outward-pacing scheme. The outward-pacing method refers to a method in which one or more cameras (or camera sensors) positioned surrounding the central object capture the environment of the central object rather than the central object. The outward-pacing method may be used to generate point cloud content (eg, content representing an external environment that may be provided to a user of an autonomous vehicle) for providing a surrounding environment that appears from the user's point of view.

As shown in the figure, the point cloud content may be generated based on a capture operation of one or more cameras. In this case, since the coordinate system of each camera may be different, the point cloud content providing system may perform calibration of one or more cameras in order to set a global coordinate system before a capture operation. In addition, the point cloud content providing system may generate the point cloud content by synthesizing the image and/or image captured by the above-described capture method and an arbitrary image and/or image. In addition, the point cloud content providing system may not perform the capture operation described with reference to FIG. 3 when generating point cloud content representing a virtual space. The point cloud content providing system according to the embodiments may perform post-processing on the captured image and/or the image. That is, the point cloud content providing system removes an unwanted area (for example, a background), recognizes a space where captured images and/or images are connected, and fills in a spatial hole if there is one. can

In addition, the point cloud content providing system may generate one point cloud content by performing coordinate system transformation on points of the point cloud video obtained from each camera. The point cloud content providing system may perform coordinate system transformation of points based on the position coordinates of each camera. Accordingly, the point cloud content providing system may generate content representing one wide range and may generate point cloud content having a high density of points.

4 shows an example of a point cloud encoder according to embodiments.

4 shows an example of the point cloud video encoder 10002 of FIG. 1 . The point cloud encoder controls point cloud data (eg, positions of points and/or attributes) and perform an encoding operation. When the total size of the point cloud content is large (for example, point cloud content of 60 Gbps in the case of 30 fps), the point cloud content providing system may not be able to stream the corresponding content in real time. Accordingly, the point cloud content providing system may reconfigure the point cloud content based on a maximum target bitrate in order to provide it according to a network environment.

As described with reference to FIGS. 1 and 2 , the point cloud encoder may perform geometry encoding and attribute encoding. Geometry encoding is performed before attribute encoding.

The point cloud encoder according to the embodiments may include a coordinate system transformation unit (Transformation Coordinates, 40000), a quantization unit (Quantize and Remove Points (Voxelize), 40001), an octree analysis unit (Analyze Octree, 40002), and a surface appropriation analysis unit ( Analyze Surface Approximation (40003), Arithmetic Encode (40004), Reconstruct Geometry (40005), Color Transformer (Transform Colors, 40006), Attribute Transformer (Transfer Attributes, 40007), RAHT Transform a unit 40008, an LOD generator (Generated LOD, 40009), a lifting transform unit (Lifting) 40010, a coefficient quantization unit (Quantize Coefficients, 40011) and/or an arithmetic encoder (Arithmetic Encode, 40012).

The coordinate system transformation unit 40000, the quantization unit 40001, the octree analysis unit 40002, the surface approximation analysis unit 40003, the arithmetic encoder 40004, and the geometry reconstruction unit 40005 perform geometry encoding. can do. Geometry encoding according to embodiments may include octree geometry coding, direct coding, trisoup geometry encoding, and entropy encoding. Direct coding and trisup geometry encoding are applied selectively or in combination. Also, the geometry encoding is not limited to the above example.

As shown in the figure, the coordinate system conversion unit 40000 according to the embodiments receives the positions and converts them into a coordinate system. For example, the positions may be converted into position information in a three-dimensional space (eg, a three-dimensional space expressed in an XYZ coordinate system, etc.). Location information in a 3D space according to embodiments may be referred to as geometry information.

The quantizer 40001 according to embodiments quantizes the geometry. For example, the quantizer 40001 may quantize the points based on the minimum position values of all points (eg, the minimum values on each axis with respect to the X-axis, Y-axis, and Z-axis). The quantization unit 40001 multiplies the difference between the minimum position value and the position value of each point by a preset quatization scale value, and then performs a quantization operation to find the nearest integer value by rounding or rounding it down. Accordingly, one or more points may have the same quantized position (or position value). The quantizer 40001 according to embodiments performs voxelization based on quantized positions to reconstruct quantized points. A minimum unit including 2D image/video information is a pixel, and points of point cloud content (or 3D point cloud video) according to embodiments may be included in one or more voxels. have. A voxel is a combination of volume and pixel, and is a unit (unit=1.0) unit of a three-dimensional space based on the axes (eg, X-axis, Y-axis, and Z-axis) representing the three-dimensional space. It means a three-dimensional cubic space that occurs when divided by . The quantizer 40001 may match groups of points in a 3D space to voxels. According to embodiments, one voxel may include only one point. According to embodiments, one voxel may include one or more points. Also, in order to express one voxel as one point, a position of a ceter of a corresponding voxel may be set based on positions of one or more points included in one voxel. In this case, attributes of all positions included in one voxel may be combined and assigned to a corresponding voxel.

The octree analyzer 40002 according to embodiments performs octree geometry coding (or octree coding) to represent voxels in an octree structure. The octree structure represents points matched to voxels based on the octal tree structure.

The surface approximation analyzer 40003 according to embodiments may analyze and approximate the octree. Octree analysis and approximation according to embodiments is a process of analyzing to voxelize a region including a plurality of points in order to efficiently provide octree and voxelization.

The arithmetic encoder 40004 according to embodiments entropy encodes the octree and/or the approximated octree. For example, the encoding method includes an arithmetic encoding method. The encoding results in a geometry bitstream.

Color transform unit 40006, attribute transform unit 40007, RAHT transform unit 40008, LOD generating unit 40009, lifting transform unit 40010, coefficient quantization unit 40011 and/or arithmetic encoder 40012 performs attribute encoding. As described above, one point may have one or more attributes. Attribute encoding according to embodiments is equally applied to attributes of one point. However, when one attribute (eg, color) includes one or more elements, independent attribute encoding is applied to each element. Attribute encoding according to embodiments may include color transform coding, attribute transform coding, region adaptive hierarchical transform (RAHT) coding, interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform coding, and interpolation-based hierarchical nearest -neighbor prediction with an update/lifting step (Lifting Transform)) may include coding. According to the point cloud content, the above-described RAHT coding, predictive transform coding, and lifting transform coding may be selectively used, or a combination of one or more codings may be used. Also, attribute encoding according to embodiments is not limited to the above-described example.

The color conversion unit 40006 according to embodiments performs color conversion coding for converting color values (or textures) included in attributes. For example, the color conversion unit 40006 may convert the format of color information (eg, convert from RGB to YCbCr). The operation of the color converter 40006 according to embodiments may be optionally applied according to color values included in the attributes.

The geometry reconstruction unit 40005 according to embodiments reconstructs (decompresses) an octree and/or an approximated octree. The geometry reconstruction unit 40005 reconstructs an octree/voxel based on a result of analyzing the distribution of points. The reconstructed octree/voxel may be referred to as a reconstructed geometry (or a reconstructed geometry).

The attribute transform unit 40007 according to embodiments performs an attribute transform that transforms attributes based on positions where geometry encoding has not been performed and/or a reconstructed geometry. As described above, since the attributes are dependent on the geometry, the attribute conversion unit 40007 may transform the attributes based on the reconstructed geometry information. For example, the attribute conversion unit 40007 may convert an attribute of a point at the position based on the position value of the point included in the voxel. As described above, when the position of the center point of a corresponding voxel is set based on the positions of one or more points included in one voxel, the attribute conversion unit 40007 converts attributes of the one or more points. When the tri-soup geometry encoding is performed, the attribute conversion unit 40007 may convert the attributes based on the tri-soup geometry encoding.

The attribute conversion unit 40007 is an average value of attributes or attribute values (eg, color or reflectance of each point) of neighboring points within a specific position/radius from the position (or position value) of the center point of each voxel. can be calculated to perform attribute transformation. The attribute conversion unit 40007 may apply a weight according to the distance from the center point to each point when calculating the average value. Thus, each voxel has a position and a computed attribute (or attribute value).

The attribute transform unit 40007 may search for neighboring points existing within a specific position/radius from the position of the center point of each voxel based on the K-D tree or the Morton code. The K-D tree is a binary search tree and supports a data structure that can manage points based on location so that Nearest Neighbor Search-NNS is possible quickly. The Molton code represents a coordinate value (eg (x, y, z)) representing the three-dimensional position of all points as a bit value, and is generated by mixing the bits. For example, if the coordinate value indicating the position of the point is (5, 9, 1), the bit value of the coordinate value is (0101, 1001, 0001). If the bit value is mixed according to the bit index in the order of z, y, x, it is 010001000111. If this value is expressed in decimal, it becomes 1095. That is, the molton code value of the point whose coordinate values are (5, 9, 1) is 1095. The attribute transform unit 40007 may align the points based on the Molton code value and perform a shortest neighbor search (NNS) through a depth-first traversal process. After the attribute transform operation, when the nearest neighbor search (NNS) is required in another transform process for attribute coding, a K-D tree or a Molton code is used.

As shown in the figure, the converted attributes are input to the RAHT conversion unit 40008 and/or the LOD generation unit 40009.

The RAHT converter 40008 according to embodiments performs RAHT coding for predicting attribute information based on the reconstructed geometry information. For example, the RAHT transform unit 40008 may predict attribute information of a node at an upper level of the octree based on attribute information associated with a node at a lower level of the octree.

The LOD generator 40009 according to embodiments generates a level of detail (LOD) to perform predictive transform coding. The LOD according to the embodiments indicates the detail of the point cloud content, and the smaller the LOD value, the lower the detail of the point cloud content, and the larger the LOD value, the higher the detail of the point cloud content. Points may be classified according to LOD.

The lifting transform unit 40010 according to embodiments performs lifting transform coding that transforms the attributes of the point cloud based on weights. As described above, lifting transform coding may be selectively applied.

The coefficient quantizer 40011 according to embodiments quantizes the attribute-coded attributes based on the coefficients.

The arithmetic encoder 40012 according to embodiments encodes the quantized attributes based on arithmetic coding.

Although not shown in the figure, the elements of the point cloud encoder of FIG. 4 are hardware including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing device. , software, firmware, or a combination thereof. The one or more processors may perform at least any one or more of the operations and/or functions of the elements of the point cloud encoder of FIG. 4 described above. The one or more processors may also operate or execute a set of software programs and/or instructions for performing the operations and/or functions of the elements of the point cloud encoder of FIG. 4 . One or more memories according to embodiments may include high speed random access memory, non-volatile memory (eg, one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid state memory). memory devices (such as solid-state memory devices).

5 shows an example of a voxel according to embodiments.

5 shows voxels located in a three-dimensional space represented by a coordinate system composed of three axes: an X-axis, a Y-axis, and a Z-axis. As described with reference to FIG. 4 , the point cloud encoder (eg, the quantization unit 40001, etc.) may perform voxelization. A voxel refers to a three-dimensional cubic space generated when a three-dimensional space is divided into units (unit=1.0) based on the axes (eg, X-axis, Y-axis, and Z-axis) representing the three-dimensional space. 5 is generated through an octree structure that recursively subdivides a bounding box (cubical axis-aligned bounding box) defined by two poles (0,0,0) and (2d, 2d, 2d) An example of a voxel is shown. One voxel includes at least one or more points. A voxel may estimate spatial coordinates from a positional relationship with a voxel group. As described above, voxels have attributes (such as color or reflectance) like pixels of a 2D image/image. A detailed description of the voxel is the same as that described with reference to FIG. 4 and thus will be omitted.

6 shows an example of an octree and an occupancy code according to embodiments.

As described with reference to FIGS. 1 to 4 , the point cloud content providing system (point cloud video encoder 10002) or point cloud encoder (eg, octree analysis unit 40002) efficiently manages the area and/or position of voxels. To do this, octree geometry coding (or octree coding) based on octree structure is performed.

The upper part of FIG. 6 shows the octree structure. The three-dimensional space of the point cloud content according to the embodiments is expressed by axes (eg, X-axis, Y-axis, and Z-axis) of the coordinate system. The octree structure is created by recursive subdividing a cubic axis-aligned bounding box defined by two poles (0,0,0) and (2 ^d , 2 ^d , 2 ^{d ).} . 2d may be set to a value constituting the smallest bounding box surrounding all points of the point cloud content (or point cloud video). d represents the depth of the octree. The value of d is determined according to the following equation. In the following equation (x ^int _n , y ^int _n , z ^int _n ) represents positions (or position values) of quantized points.

As shown in the middle of the upper part of FIG. 6 , the entire 3D space may be divided into eight spaces according to the division. Each divided space is represented by a cube with six faces. As shown in the upper right of FIG. 6 , each of the eight spaces is again divided based on the axes of the coordinate system (eg, X-axis, Y-axis, and Z-axis). Therefore, each space is further divided into 8 small spaces. The divided small space is also expressed as a cube with six faces. This division method is applied until a leaf node of the octree becomes a voxel.

The lower part of FIG. 6 shows the occupanci code of the octree. An occupancy code of an octree is generated to indicate whether each of eight divided spaces generated by dividing one space includes at least one point. Accordingly, one occupanci code is expressed by eight child nodes. Each child node represents the occupancies of the divided space, and each child node has a value of 1 bit. Therefore, the occupanci code is expressed as an 8-bit code. That is, if at least one point is included in the space corresponding to the child node, the corresponding node has a value of 1. If the space corresponding to the child node does not contain a point (empty), the node has a value of 0. Since the occupanci code shown in FIG. 6 is 00100001, spaces corresponding to the third child node and the eighth child node among the eight child nodes each include at least one point. As shown in the figure, the third child node and the eighth child node each have 8 child nodes, and each child node is expressed by an 8-bit occupanci code. The figure shows that the occupancies code of the 3rd child node is 10000111, and the occupancies code of the 8th child node is 01001111. A point cloud encoder (eg, arithmetic encoder 40004 ) according to embodiments may entropy encode the occupanci code. In addition, to increase the compression efficiency, the point cloud encoder can intra/inter-code the occupanci code. The receiving apparatus (eg, the receiving apparatus 10004 or the point cloud video decoder 10006) according to embodiments reconstructs an octree based on the occupanci code.

The point cloud encoder (eg, the point cloud encoder of FIG. 4 , or the octree analyzer 40002) according to embodiments may perform voxelization and octree coding to store positions of points. However, since the points in the 3D space are not always evenly distributed, there may be a specific area where there are not many points. Therefore, it is inefficient to perform voxelization on the entire 3D space. For example, if there are few points in a specific area, it is not necessary to perform voxelization up to the corresponding area.

Accordingly, the point cloud encoder according to the embodiments does not perform voxelization on the above-described specific region (or a node other than a leaf node of an octree), but directly codes positions of points included in the specific region. ) can be done. Coordinates of direct coding points according to embodiments are called direct coding mode (DCM). In addition, the point cloud encoder according to the embodiments may perform trisoup geometry encoding for reconstructing positions of points in a specific region (or node) based on a voxel based on a surface model. Tri-Soop geometry encoding is a geometry encoding that expresses the representation of an object as a series of triangle meshes. Thus, the point cloud decoder can generate a point cloud from the mesh surface. Direct coding and trisup geometry encoding according to embodiments may be selectively performed. Also, direct coding and trisup geometry encoding according to embodiments may be performed in combination with octree geometry coding (or octree coding).

In order to perform direct coding, the option to use a direct mode for applying direct coding must be activated, and a node to which direct coding is to be applied is not a leaf node, but is below a threshold within a specific node. points must exist. In addition, the number of whole points to be subjected to direct coding should not exceed a preset limit value. If the above condition is satisfied, the point cloud encoder (or the arithmetic encoder 40004 ) according to embodiments may entropy-code positions (or position values) of points.

The point cloud encoder (for example, the surface appropriation analyzer 40003) according to the embodiments determines a specific level of the octree (when the level is smaller than the depth d of the octree), and from that level, a node using the surface model It is possible to perform tri-soup geometry encoding that reconstructs the position of a point in a region based on voxels (tri-soup mode). The point cloud encoder according to the embodiments may designate a level to which tri-soup geometry encoding is to be applied. For example, if the specified level is equal to the depth of the octree, the point cloud encoder will not operate in trichop mode. That is, the point cloud encoder according to the embodiments may operate in the tri-soup mode only when the designated level is smaller than the depth value of the octree. A three-dimensional cube region of nodes of a specified level according to embodiments is called a block. One block may include one or more voxels. A block or voxel may correspond to a brick. Within each block, the geometry is represented as a surface. A surface according to embodiments may intersect each edge of the block at most once.

Since one block has 12 edges, there are at least 12 intersections in one block. Each intersection is called a vertex (vertex or vertex). A vertex existing along an edge is detected when there is at least one occupied voxel adjacent to the edge among all blocks sharing the edge. An ocupided voxel according to embodiments means a voxel including a point. The position of the vertex detected along the edge is the average position along the edge of all voxels of all voxels adjacent to the edge among all blocks sharing the edge.

When a vertex is detected, the point cloud encoder according to the embodiments entropy-codes the starting point (x, y, z) of the edge, the direction vectors (Δx, Δy, Δz) of the edge, and the vertex position values (relative position values within the edge). can When the tri-soap geometry encoding is applied, the point cloud encoder (eg, the geometry reconstruction unit 40005 ) according to the embodiments performs triangle reconstruction, up-sampling, and voxelization processes. to create a reconstructed geometry (reconstructed geometry). Vertices located at the edge of a block determine the surface that passes through the block. The surface according to embodiments is a non-planar polygon. The triangle reconstruction process reconstructs the surface represented by a triangle based on the starting point of the edge, the direction vector of the edge, and the position value of the vertex. The triangle reconstruction process is as follows. ① Calculate the centroid value of each vertex, ② perform a square on the values obtained by subtracting the center value from each vertex value, and obtain the sum of all the values.

The minimum value of the added values is obtained, and the projection process is performed along the axis with the minimum value. For example, if the x element is the minimum, each vertex is projected on the x-axis with respect to the center of the block and projected on the (y, z) plane. If the projected value on the (y, z) plane is (ai, bi), the θ value is obtained through atan2(bi, ai), and the vertices are aligned based on the θ value. The table below shows combinations of vertices for generating a triangle according to the number of vertices. Vertices are sorted in order from 1 to n. The table below shows that for four vertices, two triangles can be formed according to a combination of vertices. The first triangle may be composed of 1st, 2nd, and 3rd vertices among aligned vertices, and the second triangle may be composed of 3rd, 4th, and 1st vertices among aligned vertices.

graph. Triangles formed from vertices ordered 1

nn	TrianglesTriangles
33	(1,2,3)(1,2,3)
44	(1,2,3), (3,4,1)(1,2,3), (3,4,1)
55	(1,2,3), (3,4,5), (5,1,3)(1,2,3), (3,4,5), (5,1,3)
66	(1,2,3), (3,4,5), (5,6,1), (1,3,5)(1,2,3), (3,4,5), (5,6,1), (1,3,5)
77	(1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)(1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)
88	(1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1)(1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1)
99	(1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7,9,3)(1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7 ,9,3)
1010	(1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,1), (1,3,5), (5,7,9), (9,1,5)(1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,1), (1,3,5), (5 ,7,9), (9,1,5)
1111	(1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,1,3), (3,5,7), (7,9,11), (11,3,7)(1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,1,3), (3 ,5,7), (7,9,11), (11,3,7)
1212	(1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,12,1), (1,3,5), (5,7,9), (9,11,1), (1,5,9)(1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,12,1), (1 ,3,5), (5,7,9), (9,11,1), (1,5,9)

The upsampling process is performed to voxelize the triangle by adding points along the edge of the triangle. Create additional points based on the upsampling factor and the width of the block. The additional points are called refined vertices. The point cloud encoder according to embodiments may voxel the refined vertices. Also, the point cloud encoder may perform attribute encoding based on the voxelized position (or position value). FIG. 7 shows an example of a neighbor node pattern according to embodiments.

In order to increase the compression efficiency of the point cloud video, the point cloud encoder according to the embodiments may perform entropy coding based on context adaptive arithmetic coding.

1 to 6, the point cloud content providing system or the point cloud encoder (for example, the point cloud video encoder 10002, the point cloud encoder or the arithmetic encoder 40004 of FIG. 4) directly transmits the occupanci code. Entropy coding is possible. In addition, the point cloud content providing system or point cloud encoder performs entropy encoding (intra-encoding) based on the occupancies of the current node and the occupancies of neighboring nodes, or entropy encoding (inter-encoding) based on the occupancies of the previous frame. ) can be done. A frame according to embodiments means a set of point cloud videos generated at the same time. Compression efficiency of intra encoding/inter encoding according to embodiments may vary according to the number of referenced neighboring nodes. The larger the bit, the more complicated it becomes, but it can be biased to one side, which can increase the compression efficiency. For example, if you have a 3-bit context, you have to code in 2 to 3 ok = 8 ways. The division of coding affects the complexity of implementation. Therefore, it is necessary to match the appropriate level of compression efficiency and complexity.

7 illustrates a process of obtaining an occupancies pattern based on the occupancies of neighboring nodes. A point cloud encoder according to embodiments determines occupancy of neighboring nodes of each node of an octree and obtains a value of a neighbor pattern. The neighbor node pattern is used to infer the occupancies pattern of the corresponding node. The left side of FIG. 7 shows a cube corresponding to a node (a cube located in the center) and six cubes (neighbor nodes) sharing at least one face with the cube. The nodes shown in the figure are nodes of the same depth (depth). The numbers shown in the figure represent the weights (1, 2, 4, 8, 16, 32, etc.) associated with each of the six nodes. Each weight is sequentially assigned according to the positions of neighboring nodes.

The right side of FIG. 7 shows the neighboring node pattern values. The neighbor node pattern value is the sum of values multiplied by the weights of the ocupided neighbor nodes (neighbor nodes with points). Therefore, the neighbor node pattern values range from 0 to 63. When the value of the neighbor node pattern is 0, it indicates that there is no node (ocupid node) having a point among the neighboring nodes of the corresponding node. When the neighbor node pattern value is 63, it indicates that all of the neighbor nodes are ocupid nodes. As shown in the figure, since neighboring nodes to which

weights

1, 2, 4, and 8 are assigned are ocupided nodes, the neighboring node pattern value is 15, which is the sum of 1, 2, 4, and 8. The point cloud encoder may perform coding according to the value of the neighboring node pattern (for example, if the value of the neighboring node pattern is 63, 64 types of coding are performed). According to embodiments, the point cloud encoder may reduce coding complexity by changing the neighbor node pattern value (eg, based on a table that changes 64 to 10 or 6).

1 to 7 , before attribute encoding is performed, the encoded geometry is reconstructed (decompressed). When direct coding is applied, the geometry reconstruction operation may include changing the arrangement of the direct coded points (eg, placing the direct coded points in front of the point cloud data). In the case of tri-soap geometry encoding, the geometry reconstruction process is triangular reconstruction, upsampling, and voxelization. Since the attribute is dependent on the geometry, the attribute encoding is performed based on the reconstructed geometry.

The point cloud encoder (eg, the LOD generator 40009) may reorganize the points by LOD. The figure shows the point cloud content corresponding to the LOD. The left side of the drawing shows the original point cloud content. The second figure from the left of the figure shows the distribution of the points of the lowest LOD, and the rightmost figure of the figure shows the distribution of the points of the highest LOD. That is, the points of the lowest LOD are sparsely distributed, and the points of the highest LOD are densely distributed. That is, as the LOD increases according to the direction of the arrow indicated at the bottom of the drawing, the interval (or distance) between the points becomes shorter.

1 to 8, the point cloud content providing system or the point cloud encoder (for example, the point cloud video encoder 10002, the point cloud encoder of FIG. 4, or the LOD generator 40009) generates an LOD. can do. The LOD is created by reorganizing the points into a set of refinement levels according to a set LOD distance value (or set of Euclidean Distance). The LOD generation process is performed not only in the point cloud encoder but also in the point cloud decoder.

The upper part of FIG. 9 shows examples (P0 to P9) of points of point cloud content distributed in a three-dimensional space. The original order of FIG. 9 indicates the order of points P0 to P9 before LOD generation. The LOD based order of FIG. 9 indicates the order of points according to the LOD generation. Points are rearranged by LOD. Also, the high LOD includes points belonging to the low LOD. As shown in FIG. 9 , LOD0 includes P0, P5, P4 and P2. LOD1 includes the points of LOD0 and P1, P6 and P3. LOD2 includes points of LOD0, points of LOD1, and P9, P8 and P7.

As described in FIG. 4 , the point cloud encoder according to the embodiments may perform predictive transform coding, lifting transform coding, and RAHT transform coding selectively or in combination.

A point cloud encoder according to embodiments may generate predictors for points and perform predictive transform coding to set a predictive attribute (or predictive attribute value) of each point. That is, N predictors may be generated for N points. The predictor according to the embodiments calculates a weight (=1/distance) value based on the LOD value of each point and the indexing information for neighboring points existing within the distance set for each LOD and the distance value to the neighboring points. can

The prediction attribute (or attribute value) according to the embodiments is a weight calculated based on the distance to each neighboring point in the attributes (or attribute values, for example, color, reflectance, etc.) of neighboring points set in the predictor of each point (or a weight value) is set as the average value of the multiplied value. The point cloud encoder (for example, the coefficient quantization unit 40011) according to the embodiments subtracts the predicted attribute (attribute value) from the attribute (attribute value) of each point (residuals, residual attribute, residual attribute value, attribute quantization and inverse quantization can be performed on the prediction residual value, etc. The quantization process is shown in the following table.

Attribute prediction residuals quantization pseudo code

int PCCQuantization(int value, int quantStep) {

if( value >=0) {

return floor(value / quantStep + 1.0 / 3.0);

} else {

return -floor(-value / quantStep + 1.0 / 3.0);

}

Attribute prediction residuals inverse quantization pseudo code

int PCCInverseQuantization(int value, int quantStep) {

if( quantStep ==0) {

return value;

} else {

return value * quantStep;

}

The point cloud encoder (eg, the arithmetic encoder 40012 ) according to embodiments may entropy-code the quantized and dequantized residual values as described above when there are neighboring points to the predictor of each point. The point cloud encoder (eg, the arithmetic encoder 40012 ) according to embodiments may entropy-code attributes of a corresponding point without performing the above-described process if there are no neighboring points in the predictor of each point. The point cloud encoder (for example, the lifting transform unit 40010) according to the embodiments generates a predictor of each point, sets the LOD calculated in the predictor and registers neighboring points, and according to the distance to the neighboring points Lifting transform coding can be performed by setting weights. Lifting transform coding according to embodiments is similar to the aforementioned predictive transform coding, but is different in that a weight is accumulated and applied to an attribute value. A process of accumulatively applying a weight to an attribute value according to the embodiments is as follows.

1) Create an array QW (QuantizationWieght) that stores the weight value of each point. The initial value of all elements of QW is 1.0. A value obtained by multiplying the weight of the predictor of the current point by the QW value of the predictor index of the neighboring node registered in the predictor is added.

2) Lift prediction process: In order to calculate the predicted attribute value, a value obtained by multiplying a point's attribute value by a weight is subtracted from the existing attribute value.

3) Create temporary arrays called updateweight and update, and initialize the temporary arrays to 0.

4) The weights calculated for all predictors are additionally multiplied by the weights stored in the QW corresponding to the predictor index, and the calculated weights are cumulatively added to the update weight array as the indexes of neighboring nodes. In the update array, the value obtained by multiplying the calculated weight by the attribute value of the index of the neighbor node is accumulated and summed.

5) Lift update process: For all predictors, the attribute value of the update array is divided by the weight value of the update weight array of the predictor index, and the existing attribute value is added back to the divided value.

6) For all predictors, predictive attribute values are calculated by additionally multiplying the attribute values updated through the lift update process by the weights updated through the lift prediction process (stored in QW). A point cloud encoder (eg, the coefficient quantization unit 40011) according to embodiments quantizes a prediction attribute value. A point cloud encoder (eg, arithmetic encoder 40012) entropy codes the quantized attribute values.

The point cloud encoder (for example, the RAHT transform unit 40008) according to the embodiments may perform RAHT transform coding for predicting the attributes of the nodes of the higher level by using the attributes associated with the nodes at the lower level of the octree. . RAHT transform coding is an example of attribute intra coding with octree backward scan. The point cloud encoder according to the embodiments scans the entire area from the voxel, and repeats the merging process up to the root node while merging the voxels into a larger block at each step. The merging process according to the embodiments is performed only for the ocupid node. A merging process is not performed on an empty node, and a merging process is performed on a node immediately above the empty node.

The following equation represents the RAHT transformation matrix.

denotes the average attribute value of voxels in level l.

Is

Wow

can be calculated from

Wow

the weight of

class

am.

is a low-pass value and is used in the merging process at the next higher level.

is high-pass coefficients, and the high-pass coefficients in each step are quantized and entropy coded (eg, encoding of the arithmetic encoder 400012 ). weight is

is calculated as The root node is the last

class

is created as follows,

The gDC value is also quantized and entropy-coded like the high-pass coefficient.

10 shows an example of a point cloud decoder according to embodiments.

The point cloud decoder shown in FIG. 10 is an example of the point cloud video decoder 10006 described in FIG. 1 , and may perform the same or similar operations to the operation of the point cloud video decoder 10006 described in FIG. 1 . As shown in the figure, the point cloud decoder may receive a geometry bitstream and an attribute bitstream included in one or more bitstreams. The point cloud decoder includes a geometry decoder and an attribute decoder. The geometry decoder outputs decoded geometry by performing geometry decoding on the geometry bitstream. The attribute decoder outputs decoded attributes by performing attribute decoding based on the decoded geometry and the attribute bitstream. The decoded geometry and decoded attributes are used to reconstruct the point cloud content (decoded point cloud).

11 shows an example of a point cloud decoder according to embodiments.

The point cloud decoder shown in FIG. 11 is an example of the point cloud decoder described with reference to FIG. 10 , and may perform a decoding operation that is a reverse process of the encoding operation of the point cloud encoder described with reference to FIGS. 1 to 9 .

1 and 10 , the point cloud decoder may perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

A point cloud decoder according to embodiments may include an arithmetic decoder 11000, a synthesize octree 11001, a synthesize surface approximation 11002, and a reconstruct geometry , 11003), inverse transform coordinates (11004), arithmetic decoder (11005), inverse quantize (11006), RAHT transform unit (11007), LOD generator (generate LOD, 11008) ), an inverse lifting unit (Inverse lifting, 11009), and / or color inverse transform unit (inverse transform colors, 11010).

The arithmetic decoder 11000 , the octree synthesizer 11001 , the surface opproximation synthesizer 11002 , the geometry reconstruction unit 11003 , and the coordinate system inverse transformation unit 11004 may perform geometry decoding. Geometry decoding according to embodiments may include direct coding and trisoup geometry decoding. Direct coding and trisup geometry decoding are optionally applied. Also, the geometry decoding is not limited to the above example, and is performed as a reverse process of the geometry encoding described with reference to FIGS. 1 to 9 .

The arithmetic decoder 11000 according to embodiments decodes the received geometry bitstream based on arithmetic coding. The operation of the arithmetic decoder 11000 corresponds to the reverse process of the arithmetic encoder 40004 .

The octree synthesizing unit 11001 according to embodiments may generate an octree by obtaining an ocupancy code from a decoded geometry bitstream (or information about a geometry secured as a result of decoding). A detailed description of the occupanci code is the same as described with reference to FIGS. 1 to 9 .

The surface op-proximation synthesizing unit 11002 according to the embodiments may synthesize a surface based on the decoded geometry and/or the generated octree when the tri-top geometry encoding is applied.

The geometry reconstruction unit 11003 according to embodiments may reconstruct a geometry based on the surface and/or the decoded geometry. As described with reference to FIGS. 1 to 9 , direct coding and tri-soup geometry encoding are selectively applied. Accordingly, the geometry reconstruction unit 11003 directly brings and adds position information of points to which direct coding is applied. In addition, when tri-soup geometry encoding is applied, the geometry reconstruction unit 11003 may perform a reconstruction operation of the geometry reconstruction unit 40005, for example, triangle reconstruction, up-sampling, and voxelization to restore the geometry. have. Specific details are the same as those described with reference to FIG. 6 and thus will be omitted. The reconstructed geometry may include a point cloud picture or frame that does not include attributes.

The coordinate system inverse transform unit 11004 according to embodiments may obtain positions of points by transforming the coordinate system based on the restored geometry.

The arithmetic decoder 11005, the inverse quantization unit 11006, the RAHT transform unit 11007, the LOD generation unit 11008, the inverse lifting unit 11009, and/or the color inverse transform unit 11010 are the attributes described with reference to FIG. decoding can be performed. Attribute decoding according to embodiments includes Region Adaptive Hierarchical Transform (RAHT) decoding, Interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform decoding, and interpolation-based hierarchical nearest-neighbor prediction with an update/lifting step (Lifting Transform)) decoding may be included. The above three decodings may be used selectively, or a combination of one or more decodings may be used. Also, attribute decoding according to the embodiments is not limited to the above-described example.

The arithmetic decoder 11005 according to embodiments decodes an attribute bitstream by arithmetic coding.

The inverse quantization unit 11006 according to the embodiments inverse quantizes the decoded attribute bitstream or information about the attribute secured as a result of decoding, and outputs inverse quantized attributes (or attribute values). Inverse quantization may be selectively applied based on attribute encoding of the point cloud encoder.

According to embodiments, the RAHT transformation unit 11007, the LOD generation unit 11008, and/or the inverse lifting unit 11009 may process the reconstructed geometry and dequantized attributes. As described above, the RAHT converting unit 11007, the LOD generating unit 11008 and/or the inverse lifting unit 11009 may selectively perform a corresponding decoding operation according to the encoding of the point cloud encoder.

The color inverse transform unit 11010 according to embodiments performs inverse transform coding for inverse transforming color values (or textures) included in decoded attributes. The operation of the color inverse transform unit 11010 may be selectively performed based on the operation of the color transform unit 40006 of the point cloud encoder.

Although the elements of the point cloud decoder of FIG. 11 are not shown in the figure, hardware including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing apparatus , software, firmware, or a combination thereof. One or more processors may perform at least any one or more of the operations and/or functions of the elements of the point cloud decoder of FIG. 11 described above. Also, the one or more processors may operate or execute a set of software programs and/or instructions for performing operations and/or functions of the elements of the point cloud decoder of FIG. 11 .

12 is an example of a transmission apparatus according to embodiments.

The transmission device shown in FIG. 12 is an example of the transmission device 10000 of FIG. 1 (or the point cloud encoder of FIG. 4 ). The transmitting apparatus shown in FIG. 12 may perform at least any one or more of the same or similar operations and methods to the operations and encoding methods of the point cloud encoder described with reference to FIGS. 1 to 9 . The transmission apparatus according to the embodiments includes a data input unit 12000 , a quantization processing unit 12001 , a voxelization processing unit 12002 , an occupancy code generation unit 12003 , a surface model processing unit 12004 , and an intra/ Inter-coding processing unit 12005, arithmetic coder 12006, metadata processing unit 12007, color conversion processing unit 12008, attribute conversion processing unit (or attribute conversion processing unit) 12009, prediction/lifting/RAHT conversion It may include a processing unit 12010 , an arithmetic coder 12011 , and/or a transmission processing unit 12012 .

The data input unit 12000 according to embodiments receives or acquires point cloud data. The data input unit 12000 may perform the same or similar operation and/or acquisition method to the operation and/or acquisition method of the point cloud video acquisition unit 10001 (or the acquisition process 20000 described in FIG. 2 ).

Data input unit 12000 , quantization processing unit 12001 , voxelization processing unit 12002 , octree occupancy code generation unit 12003 , surface model processing unit 12004 , intra/inter coding processing unit 12005 , Arithmetic The coder 12006 performs geometry encoding. Since the geometry encoding according to the embodiments is the same as or similar to the geometry encoding described with reference to FIGS. 1 to 9 , a detailed description thereof will be omitted.

The quantization processing unit 12001 according to embodiments quantizes a geometry (eg, a position value of points or a position value). The operation and/or quantization of the quantization processing unit 12001 is the same as or similar to the operation and/or quantization of the quantization unit 40001 described with reference to FIG. 4 . A detailed description is the same as that described with reference to FIGS. 1 to 9 .

The voxelization processing unit 12002 according to embodiments voxelizes position values of quantized points. The voxelization processing unit 12002 may perform the same or similar operations and/or processes as those of the quantization unit 40001 described with reference to FIG. 4 and/or the voxelization process. A detailed description is the same as that described with reference to FIGS. 1 to 9 .

The octree occupancy code generator 12003 according to embodiments performs octree coding on the positions of voxelized points based on the octree structure. The octree occupancy code generator 12003 may generate an occupanci code. The octree occupancy code generator 12003 may perform the same or similar operations and/or methods to those of the point cloud encoder (or the octree analyzer 40002) described with reference to FIGS. 4 and 6 . A detailed description is the same as that described with reference to FIGS. 1 to 9 .

The surface model processing unit 12004 according to the embodiments may perform tri-supply geometry encoding for reconstructing positions of points in a specific region (or node) based on a voxel based on a surface model. The fore surface model processing unit 12004 may perform the same or similar operations and/or methods to those of the point cloud encoder (eg, the surface appropriation analyzer 40003) described with reference to FIG. 4 . A detailed description is the same as that described with reference to FIGS. 1 to 9 .

The intra/inter coding processing unit 12005 according to embodiments may perform intra/inter coding of point cloud data. The intra/inter coding processing unit 12005 may perform the same or similar coding to the intra/inter coding described with reference to FIG. 7 . A detailed description is the same as that described with reference to FIG. 7 . In some embodiments, the intra/inter coding processing unit 12005 may be included in the arithmetic coder 12006 .

The arithmetic coder 12006 according to embodiments entropy encodes an octree and/or an approximated octree of point cloud data. For example, the encoding method includes an arithmetic encoding method. . The arithmetic coder 12006 performs the same or similar operations and/or methods as the operations and/or methods of the arithmetic encoder 40004 .

The metadata processing unit 12007 according to the embodiments processes metadata related to point cloud data, for example, a setting value, and provides it to necessary processing such as geometry encoding and/or attribute encoding. Also, the metadata processing unit 12007 according to embodiments may generate and/or process signaling information related to geometry encoding and/or attribute encoding. Signaling information according to embodiments may be encoded separately from geometry encoding and/or attribute encoding. Also, signaling information according to embodiments may be interleaved.

The color conversion processing unit 12008, the attribute conversion processing unit 12009, the prediction/lifting/RAHT conversion processing unit 12010, and the arithmetic coder 12011 perform attribute encoding. Since the attribute encoding according to the embodiments is the same as or similar to the attribute encoding described with reference to FIGS. 1 to 9 , a detailed description thereof will be omitted.

The color conversion processing unit 12008 according to embodiments performs color conversion coding for converting color values included in the attributes. The color conversion processing unit 12008 may perform color conversion coding based on the reconstructed geometry. The description of the reconstructed geometry is the same as that described with reference to FIGS. 1 to 9 . In addition, the same or similar operation and/or method to the operation and/or method of the color conversion unit 40006 described with reference to FIG. 4 is performed. A detailed description will be omitted.

The attribute transformation processing unit 12009 according to embodiments performs an attribute transformation for transforming attributes based on positions where geometry encoding has not been performed and/or a reconstructed geometry. The attribute transformation processing unit 12009 performs the same or similar operations and/or methods as those of the attribute transformation unit 40007 described in FIG. 4 . A detailed description will be omitted. The prediction/lifting/RAHT transform processing unit 12010 according to embodiments may code the transformed attributes by any one or a combination of RAHT coding, predictive transform coding, and lifting transform coding. The prediction/lifting/RAHT transformation processing unit 12010 performs at least one or more of the same or similar operations to the operations of the RAHT transformation unit 40008, the LOD generation unit 40009, and the lifting transformation unit 40010 described with reference to FIG. 4 . do. In addition, since the descriptions of predictive transform coding, lifting transform coding, and RAHT transform coding are the same as those described with reference to FIGS. 1 to 9 , a detailed description thereof will be omitted.

The arithmetic coder 12011 according to embodiments may encode coded attributes based on arithmetic coding. The arithmetic coder 12011 performs the same or similar operations and/or methods to the operations and/or methods of the arithmetic encoder 400012 .

The transmission processing unit 12012 according to the embodiments transmits each bitstream including the encoded geometry and/or encoded attribute and metadata information, or converts the encoded geometry and/or the encoded attribute and metadata information into one It can be transmitted by composing it as a bitstream. When the encoded geometry and/or the encoded attribute and metadata information according to the embodiments are configured as one bitstream, the bitstream may include one or more sub-bitstreams. The bitstream according to the embodiments is a sequence parameter set (SPS) for signaling of a sequence level, a geometry parameter set (GPS) for signaling of a geometry information coding, an attribute parameter set (APS) for signaling of an attribute information coding, a tile Signaling information including a Tile Parameter Set (TPS) for level signaling and slice data may be included. Slice data may include information about one or more slices. One slice according to embodiments may include one geometry bitstream (Geom0 ⁰ ) and one or more attribute bitstreams (Attr0 ⁰ , Attr1 ⁰ ).

A slice refers to a series of syntax elements representing all or a part of a coded point cloud frame.

The TPS according to embodiments may include information about each tile (eg, coordinate value information and height/size information of a bounding box, etc.) for one or more tiles. A geometry bitstream may include a header and a payload. The header of the geometry bitstream according to the embodiments may include identification information (geom_ parameter_set_id), a tile identifier (geom_tile_id), a slice identifier (geom_slice_id) of a parameter set included in GPS, and information about data included in a payload, etc. can As described above, the metadata processing unit 12007 according to the embodiments may generate and/or process signaling information and transmit it to the transmission processing unit 12012 . According to embodiments, elements performing geometry encoding and elements performing attribute encoding may share data/information with each other as dotted lines are processed. The transmission processing unit 12012 according to embodiments may perform the same or similar operation and/or transmission method to the operation and/or transmission method of the transmitter 10003 . Since the detailed description is the same as that described with reference to FIGS. 1 to 2 , a detailed description thereof will be omitted.

13 is an example of a receiving apparatus according to embodiments.

The reception apparatus shown in FIG. 13 is an example of the reception apparatus 10004 of FIG. 1 (or the point cloud decoder of FIGS. 10 and 11 ). The receiving apparatus shown in FIG. 13 may perform at least any one or more of the same or similar operations and methods to the operations and decoding methods of the point cloud decoder described with reference to FIGS. 1 to 11 .

The reception apparatus according to the embodiments includes a reception unit 13000 , a reception processing unit 13001 , an arithmetic decoder 13002 , an Occupancy code-based octree reconstruction processing unit 13003 , and a surface model processing unit (triangle reconstruction). , up-sampling, voxelization) 13004, inverse quantization processing unit 13005, metadata parser 13006, arithmetic decoder 13007, inverse quantization processing unit 13008, prediction It may include a /lifting/RAHT inverse transformation processing unit 13009 , an inverse color transformation processing unit 13010 , and/or a renderer 13011 . Each component of decoding according to embodiments may perform a reverse process of a component of encoding according to embodiments.

The receiver 13000 according to embodiments receives point cloud data. The receiver 13000 may perform the same or similar operation and/or reception method as the operation and/or reception method of the receiver 10005 of FIG. 1 . A detailed description will be omitted.

The reception processing unit 13001 according to embodiments may acquire a geometry bitstream and/or an attribute bitstream from the received data. The reception processing unit 13001 may be included in the reception unit 13000 .

The arithmetic decoder 13002, the occupancy code-based octree reconstruction processing unit 13003, the surface model processing unit 13004, and the inverse quantization processing unit 13005 may perform geometry decoding. Since the geometry decoding according to the embodiments is the same as or similar to the geometry decoding described with reference to FIGS. 1 to 10 , a detailed description thereof will be omitted.

The arithmetic decoder 13002 according to embodiments may decode a geometry bitstream based on arithmetic coding. The arithmetic decoder 13002 performs the same or similar operations and/or coding to the operations and/or coding of the arithmetic decoder 11000 .

The ocupancy code-based octree reconstruction processing unit 13003 according to the embodiments may reconstruct the octopus by obtaining an occupanci code from a decoded geometry bitstream (or information about a geometry secured as a result of decoding). The occupancy code-based octree reconstruction processing unit 13003 performs the same or similar operations and/or methods as those of the octree synthesis unit 11001 and/or the octree generation method. The surface model processing unit 13004 according to the embodiments may decode a trichop geometry based on the surface model method and reconstruct the geometry related thereto (eg, triangle reconstruction, up-sampling, voxelization) based on the surface model method when trisuple geometry encoding is applied. can be performed. The surface model processing unit 13004 performs the same or similar operations to the operations of the surface op-proximation synthesis unit 11002 and/or the geometry reconstruction unit 11003 .

The inverse quantization processing unit 13005 according to embodiments may inverse quantize the decoded geometry.

The metadata parser 13006 according to embodiments may parse metadata included in the received point cloud data, for example, a setting value. The metadata parser 13006 may pass the metadata to geometry decoding and/or attribute decoding. A detailed description of the metadata is the same as that described with reference to FIG. 12 , and thus will be omitted.

The arithmetic decoder 13007 , the inverse quantization processing unit 13008 , the prediction/lifting/RAHT inverse transformation processing unit 13009 , and the color inverse transformation processing unit 13010 perform attribute decoding. Since the attribute decoding is the same as or similar to the attribute decoding described with reference to FIGS. 1 to 10 , a detailed description thereof will be omitted.

The arithmetic decoder 13007 according to embodiments may decode the attribute bitstream by arithmetic coding. The arithmetic decoder 13007 may perform decoding of the attribute bitstream based on the reconstructed geometry. The arithmetic decoder 13007 performs the same or similar operation and/or coding to the operation and/or coding of the arithmetic decoder 11005 .

The inverse quantization processing unit 13008 according to embodiments may inverse quantize the decoded attribute bitstream. The inverse quantization processing unit 13008 performs the same or similar operations and/or methods to those of the inverse quantization unit 11006 and/or the inverse quantization method.

The prediction/lifting/RAHT inverse transform processing unit 13009 according to embodiments may process the reconstructed geometry and inverse quantized attributes. The prediction/lifting/RAHT inverse transform processing unit 13009 performs the same or similar operations and/or decodings as the operations and/or decodings of the RAHT transform unit 11007, the LOD generation unit 11008 and/or the inverse lifting unit 11009 and/or At least any one or more of the decodings are performed. The color inverse transform processing unit 13010 according to embodiments performs inverse transform coding for inverse transforming color values (or textures) included in decoded attributes. The color inverse transform processing unit 13010 performs the same or similar operation and/or inverse transform coding to the operation and/or inverse transform coding of the color inverse transform unit 11010 . The renderer 13011 according to embodiments may render point cloud data.

14 illustrates an example of a structure capable of interworking with a method/device for transmitting and receiving point cloud data according to embodiments.

The structure of FIG. 14 includes at least one or more of a server 1460 , a robot 1410 , an autonomous vehicle 1420 , an XR device 1430 , a smartphone 1440 , a home appliance 1450 , and/or an HMD 1470 . A configuration connected to the cloud network 1410 is shown. The robot 1410 , the autonomous vehicle 1420 , the XR device 1430 , the smart phone 1440 , or the home appliance 1450 are referred to as devices. Also, the XR device 1430 may correspond to a point cloud data (PCC) device according to embodiments or may be linked with the PCC device.

The cloud network 1400 may constitute a part of the cloud computing infrastructure or may refer to a network existing in the cloud computing infrastructure. Here, the cloud network 1400 may be configured using a 3G network, a 4G or Long Term Evolution (LTE) network, or a 5G network.

The server 1460 includes at least one of a robot 1410 , an autonomous vehicle 1420 , an XR device 1430 , a smartphone 1440 , a home appliance 1450 and/or an HMD 1470 , and a cloud network 1400 . It is connected through and may help at least a part of the processing of the connected devices 1410 to 1470 .

A Head-Mount Display (HMD) 1470 represents one of the types in which an XR device and/or a PCC device according to embodiments may be implemented. The HMD-type device according to the embodiments includes a communication unit, a control unit, a memory unit, an I/O unit, a sensor unit, and a power supply unit.

Hereinafter, various embodiments of the devices 1410 to 1450 to which the above-described technology is applied will be described. Here, the devices 1410 to 1450 shown in FIG. 14 may be linked/coupled with the point cloud data transmission/reception device according to the above-described embodiments.

<PCC+XR>

XR / PCC device 1430 is PCC and / or XR (AR + VR) technology is applied, HMD (Head-Mount Display), HUD (Head-Up Display) provided in the vehicle, television, mobile phone, smart phone, It may be implemented as a computer, a wearable device, a home appliance, a digital signage, a vehicle, a stationary robot, or a mobile robot.

The XR/PCC device 1430 analyzes three-dimensional point cloud data or image data acquired through various sensors or from an external device to generate position data and attribute data for three-dimensional points in the surrounding space or real objects. Information can be obtained and the XR object to be output can be rendered and output. For example, the XR/PCC apparatus 1430 may output an XR object including additional information on the recognized object to correspond to the recognized object.

<PCC+XR+mobile phone>

The XR/PCC device 1430 may be implemented as a mobile phone 1440 or the like to which PCC technology is applied.

The mobile phone 1440 may decode and display the point cloud content based on the PCC technology.

<PCC+Autonomous Driving+XR>

The autonomous vehicle 1420 may be implemented as a mobile robot, a vehicle, an unmanned aerial vehicle, etc. by applying PCC technology and XR technology.

The autonomous driving vehicle 1420 to which the XR/PCC technology is applied may mean an autonomous driving vehicle equipped with a means for providing an XR image or an autonomous driving vehicle subject to control/interaction within the XR image. In particular, the autonomous driving vehicle 1420 that is the target of control/interaction within the XR image may be distinguished from the XR device 1430 and may be interlocked with each other.

The autonomous vehicle 1420 having means for providing an XR/PCC image may obtain sensor information from sensors including a camera, and output an XR/PCC image generated based on the acquired sensor information. For example, the autonomous vehicle 1420 may provide an XR/PCC object corresponding to a real object or an object in a screen to a passenger by outputting an XR/PCC image with a HUD.

In this case, when the XR/PCC object is output to the HUD, at least a portion of the XR/PCC object may be output to overlap the real object to which the passenger's gaze is directed. On the other hand, when the XR/PCC object is output to a display provided inside the autonomous vehicle, at least a part of the XR/PCC object may be output to overlap the object in the screen. For example, the autonomous vehicle 1220 may output XR/PCC objects corresponding to objects such as a lane, other vehicles, traffic lights, traffic signs, two-wheeled vehicles, pedestrians, and buildings.

VR (Virtual Reality) technology, AR (Augmented Reality) technology, MR (Mixed Reality) technology, and/or PCC (Point Cloud Compression) technology according to the embodiments may be applied to various devices.

In other words, VR technology is a display technology that provides objects or backgrounds in the real world only as CG images. On the other hand, AR technology refers to a technology that shows a virtual CG image on top of an actual object image. Furthermore, the MR technology is similar to the AR technology described above in that it shows the virtual objects by mixing and combining them in the real world. However, in AR technology, the distinction between real objects and virtual objects made of CG images is clear, and virtual objects are used in a form that complements real objects, whereas in MR technology, virtual objects are regarded as having the same characteristics as real objects. distinct from technology. More specifically, for example, a hologram service to which the aforementioned MR technology is applied.

However, recently, VR, AR, and MR technologies are sometimes called XR (extended reality) technologies rather than clearly distinguishing them. Accordingly, embodiments of the present invention are applicable to all of VR, AR, MR, and XR technologies. For these technologies, encoding/decoding based on PCC, V-PCC, and G-PCC technologies may be applied.

The PCC method/apparatus according to the embodiments may be applied to a vehicle providing an autonomous driving service.

A vehicle providing an autonomous driving service is connected to a PCC device for wired/wireless communication.

The point cloud data (PCC) transceiver according to the embodiments receives/processes AR/VR/PCC service-related content data that can be provided together with the autonomous driving service when connected to a vehicle to enable wired/wireless communication, can be sent to In addition, when the point cloud data transceiver is mounted on a vehicle, the point cloud transceiver may receive/process AR/VR/PCC service-related content data according to a user input signal input through the user interface device and provide it to the user. A vehicle or a user interface device according to embodiments may receive a user input signal. A user input signal according to embodiments may include a signal indicating an autonomous driving service.

15 is a point cloud data transmission apparatus (eg, the transmission apparatus 10000 of FIG. 1 , the point cloud video encoder 10002 of FIG. 1 , the point cloud encoder of FIG. 4 , and the transmission apparatus of FIG. 12 according to the embodiments. , a block diagram illustrating the encoder of FIG. 12 and the XR device 1430 of FIG. 14 ). The transmitting apparatus 1500 according to embodiments may perform the same or similar operation to the encoding operation described with reference to FIGS. 1 to 14 . The transmitting apparatus according to the embodiments may include a data input unit 1501 , a coordinate transformation unit 1502 , a quantization/voxelization processing unit 1503 , a prediction tree structure generation unit 1504 , and/or an attribute encoding unit 1505 . can Although not shown in FIG. 15 , the transmitting apparatus according to the embodiments may further include one or more elements for performing the same or similar operation to the encoding operation described with reference to FIGS. 1 to 14 .

The data input unit according to embodiments may receive point cloud data (eg, point cloud data of FIGS. 1 to 4 ). Point cloud data according to embodiments may include position values (eg, geometry information of FIG. 1 ) and attribute values (eg, attribute information of FIG. 1 ) of points. The data input unit according to embodiments may perform the same or similar operation as that of the point cloud video obtainer 10001 of FIG. 1 .

The coordinate system transforming unit according to embodiments may transform a coordinate system for geometry information of point cloud data. The coordinate system transformation unit according to embodiments may perform the same or similar operation to the operation of the coordinate system transformation unit 40000 of FIG. 4 .

The quantization/voxelization processing unit according to embodiments may adjust the distribution of points of the point cloud data according to a scale value (or quantum value) and voxelize the geometry data of the point cloud data. The quantization/voxelization processing unit according to embodiments may perform the same or similar operations to those of the quantizer 40001 of FIG. 4 and the voxelizer 40001 of FIG. 4 .

The prediction tree structure generator according to embodiments may perform a process of encoding the geometry of points using a predictive geometry encoding scheme. The process of encoding the geometry of points with a predictive geometry encoding scheme may include a process of reordering one or more points based on the geometry and a process of generating a prediction tree based on the reordered points. can The prediction tree structure according to the embodiments may be referred to as a prediction tree, a prediction structure, a prediction tree, a prediction structure, or a prediction tree structure. A prediction tree according to embodiments may represent a geometric structure of points of point cloud data instead of the octree structure described above with reference to FIGS. 1 to 14 . A prediction tree according to embodiments may indicate a connection relationship based on a geometry of point cloud data. Accordingly, the prediction tree structure generator according to the embodiments may predict the geometry of points based on the prediction tree. A prediction tree according to embodiments may include one or more vertices corresponding to each point of one or more points of point cloud data. That is, one vertex of the one or more vertices according to embodiments corresponds to one of the one or more points. A vertex according to embodiments may be referred to as a prediction tree node (or node). A vertex according to embodiments has a prediction tree depth (or depth). The transmitting apparatus according to the embodiments may encode signaling information (or signaling information) regarding the prediction geometry encoding scheme for each vertex of the prediction tree. The reception apparatus according to the embodiments may predict the geometry of points based on signaling information about the prediction geometry encoding scheme generated for each vertex.

The prediction tree structure generator according to embodiments may include a data aligning unit 1504a, a prediction value calculation unit 1504b, a prediction tree structure transformation unit 1504c, and/or a prediction value calculation and transmission method determination unit 1504d. The arrangement order of the data aligning unit, the prediction unit calculation unit, the prediction tree structure transformation unit, and/or the prediction value calculation and transmission method determining unit according to the embodiments may be changed. For example, the prediction tree structure transformation unit may be located between the data aligning unit and the prediction value calculating unit. Although not shown in FIG. 15 , the prediction tree structure generator according to embodiments may further include one or more elements for generating the aforementioned prediction tree.

The data arranging unit according to embodiments may rearrange points of point cloud data based on geometry. The data sorting unit according to the embodiments may rearrange the points based on an arbitrary sorting order in order to sort the points in the same or similar order to the order in which the points were obtained before generating the prediction tree. The sorting order according to embodiments may include a morton order, an azimuth angle order, or a radial distance order. For example, the data sorting unit may rearrange the points in an ascending order of the molton order (the molton code order of FIG. 4 ). As described above with reference to FIG. 3 , the point cloud data may be acquired through an out-word pacing method through LiDAR. LiDAR according to embodiments obtains point cloud data based on reflection of light emitted from one or more lasers arranged in a rotating LiDAR header. That is, the points of the point cloud data obtained through the lidar may be rearranged in ascending or descending order of the azimuth of the rotating lidar header. Accordingly, the data alignment unit may rearrange the points in an ascending order of the azimuth of the lidar header. In addition, the points of the point cloud data obtained through the lidar may be rearranged in ascending or descending order of the distance from the lidar to the point. Accordingly, the data aligning unit may rearrange the points in an ascending order of a distance (or a radial distance) spaced apart from the LiDAR to the point. The method of the data aligning unit according to the embodiments of realigning the points is limited to the above-described example. doesn't happen

One point among the points rearranged by the data aligning unit according to embodiments corresponds to one vertex. Also, a depth of a vertex corresponding to a point may be determined based on the order in which the points are rearranged. For example, a depth of a vertex corresponding to a point having the smallest molton code among points rearranged in the molton order has the smallest value (eg, 0) and is referred to as a root vertex.

The prediction value calculator according to embodiments may calculate a prediction value and a prediction residual value based on a prediction mode for a point corresponding to each vertex of the vertices. A prediction mode according to embodiments may indicate a mode of a method of calculating a prediction value of a point corresponding to a vertex. The predicted value according to embodiments may be a predicted value of a geometric value (or a position value) of a point. That is, the predicted value of the point according to the embodiments may be different from the coordinate value indicating the actual position of the point. The prediction value according to the embodiments may be calculated based on the prediction mode in the transmitting apparatus and the receiving apparatus. The predicted value according to the embodiments may include an x-coordinate value, a y-coordinate value, and a z-coordinate value (eg, parameters representing the 3D coordinate system described in FIG. 2 ). A prediction residual value according to embodiments may indicate a difference between a predicted value of a point and a coordinate value indicating an actual position of the point. That is, the prediction residual value according to the embodiments may indicate the accuracy of the calculated prediction value. The prediction residual value according to embodiments may include an x-coordinate value, a y-coordinate value, and a z-coordinate value. The transmitting apparatus according to the embodiments may transmit the calculated prediction residual value to the receiving apparatus, and the receiving apparatus may reconstruct the geometry of points based on the received prediction residual value and the prediction value calculated based on the prediction mode.

The prediction tree structure transforming unit according to the embodiments may change the structure of the prediction tree. A prediction tree according to embodiments may be generated based on geometrical proximity of points. The aforementioned geometric adjacency may be based on a Euclidean distance between points. For example, the prediction tree structure generator may search a point corresponding to the root vertex and a point closest to the point corresponding to the root vertex and register it as a child vertex of the root vertex. The child vertices of the root vertex have a depth that is one greater than the depth of the root vertex. That is, a prediction tree generated based on geometric adjacency may include only one vertex at one depth. A prediction tree including only one vertex in one depth may not reflect geometrical proximity of points as the depth increases. A prediction tree including only one vertex in one depth searches for points only in a direction in which the depth of the prediction tree increases, and as the depth increases, the geometrical proximity of the points may not be reflected. For example, the point corresponding to the vertex of depth 10 may be located closer to the point corresponding to the root vertex than the point corresponding to the vertex of depth 2 . The prediction tree structure transforming unit according to the embodiments may re-search a point that has not been searched in the existing prediction tree and add it to the prediction tree. For example, the prediction tree structure transforming unit searches up to a point corresponding to the vertex of depth 2, and then recalculates the unsearched point among the points located between the point corresponding to the root vertex and the point corresponding to the vertex of depth 2 By searching, it can be added as a child vertex of any one vertex from the root vertex to the vertex of depth 1. That is, the changed prediction tree (or the changed prediction tree) generated by the prediction tree structure transforming unit according to the embodiments may include two or more vertices in one depth. That is, the number of total depths of the prediction tree having the changed structure according to the embodiments may be smaller than the total number of depths of the prediction tree having the structure before the change. The structure before the change described above may be referred to as a first structure, and the modified structure may be referred to as a second structure. The method of the prediction tree structure transforming unit changing the structure of the prediction tree according to the embodiments is not limited to the above-described example.

The prediction value calculation and delivery method determining unit according to the embodiments may transmit the x-coordinate value, the y-coordinate value, and the z-coordinate value of the above-described prediction residual value by substituting a single value. For example, the prediction value calculation and delivery method determining unit according to the embodiments mixes the bit values of the x-coordinate value, the y-coordinate value, and the z-coordinate value of the prediction residual value to a molton code (the molton code described above in FIG. 4) can be replaced with The method of the prediction value calculation and transmission method determining unit replacing the x-coordinate value, the y-coordinate value, and the z-coordinate value of the prediction residual value with one value is not limited to the above-described example.

The above-described prediction value calculation unit, prediction tree structure transformation unit, and/or prediction value calculation and transmission method determination unit may be included in the prediction tree generation unit (not shown in this figure). That is, the prediction tree structure generator according to the embodiments may include a data aligner and/or a prediction tree generator.

The attribute encoder according to embodiments may perform attribute encoding (eg, attribute encoding described with reference to FIGS. 1 to 14 ). The attribute information encoder according to embodiments may include one or more elements for performing the attribute encoding described with reference to FIGS. 1 to 14 .

The transmitting apparatus according to the embodiments may transmit a bitstream in which a geometry bitstream and/or an attribute bitstream are multiplexed by the method described with reference to FIG. 15 . In addition, the bitstream according to the embodiments may further include signaling information on the aforementioned prediction tree (or signaling information on the prediction geometry encoding scheme). The transmitting apparatus according to the embodiments generates the above-described prediction tree, and transmits the prediction residual value and signaling information regarding the prediction geometry encoding scheme to the receiving apparatus, thereby reducing the latency of geometry decoding and burdening the information stored in the bitstream. can be reduced to provide real-time point cloud content. The point cloud data transmission apparatus according to the embodiments may encapsulate the bitstream and transmit it in the form of segments and/or files.

16 shows a prediction tree structure according to embodiments.

16 illustrates a structure of a prediction tree (or prediction tree) according to embodiments. The prediction tree according to the embodiments represents the prediction tree described with reference to FIG. 15 .

Reference numeral 1600 denotes a Ply file (eg, the Ply file described in FIG. 2 ) included in the point cloud data acquired through the acquisition unit (eg, the point cloud video acquisition unit of FIG. 1 ). A Ply file may contain one or more points.

Point cloud data according to embodiments may be divided into category 1 data and category 3 data according to characteristics of the point cloud data. Category 1 data according to embodiments may be static data, and category 3 data according to embodiments may be dynamic data. Accordingly, category 1 data according to embodiments may include only one frame (the frame described with reference to FIGS. 1 to 14 ), and category 3 data according to embodiments may include one or more frames. Also, points corresponding to one frame may be distributed more sparsely in category 3 data than in category 1 data. As described above with reference to FIGS. 1 to 14 , point cloud data may be geometrically encoded/decoded based on an octree structure. As described above with reference to FIGS. 4 to 6 , in order to perform geometry encoding/decoding based on an octree structure, a voxelization process of recursively dividing a bounding box must be performed. However, if octree-based geometry encoding/decoding is performed on point cloud data corresponding to category 3 data, voxelization is performed for each frame, and thus point cloud content may not be provided in real time. Therefore, the transmitting apparatus / receiving apparatus according to the embodiments reduces the latency in the geometry encoding / decoding process by performing a process of predicting the geometry of the points based on the prediction tree with respect to the point cloud data corresponding to the category 3 data, Real-time point cloud content can be provided.

1601 indicates reordered points according to embodiments. The rearranged points according to the exemplary embodiments represent points rearranged by the data aligning unit 1504a of FIG. 15 . A process of rearranging points according to embodiments is the same as or similar to the process described with reference to FIG. 15 . As described above in FIG. 15 , the points may be rearranged based on the molton order. For example, the points of the acquired point cloud data may be rearranged in ascending order of Morton code size.

1602 indicates a prediction tree structure (or prediction tree) generated based on rearranged points according to embodiments. A prediction tree according to embodiments may be generated by the prediction tree structure generator 1504 of FIG. 15 . The process of generating the prediction tree according to the embodiments is the same as or similar to the process described with reference to FIG. 15 . As described above with reference to FIG. 15 , vertices according to embodiments have a prediction tree depth (or depth). Also, a vertex corresponding to the lowest depth among vertices according to embodiments may correspond to a root vertex. A vertex corresponding to the highest depth among vertices according to embodiments may correspond to a leaf vertex. The depth of the current vertex may be expressed as the number of hops from the root vertex to the current vertex. A child vertex of the current vertex according to embodiments may have a depth greater than the depth of the current vertex by 1, and a parent vertex of the current vertex may have a depth smaller than the depth of the current vertex by 1 can As described above with reference to FIG. 15 , a point corresponding to a root vertex having a depth of 0 may be determined based on an order in which the points are rearranged according to embodiments. For example, a depth of a vertex corresponding to a point having the smallest molton code among points rearranged in molton order has the smallest value (eg, 0) and may be referred to as a root vertex. . 1602a indicates a point corresponding to the root vertex. A point corresponding to a child vertex of the root vertex according to embodiments may be searched based on the Euclidean distance between the points described above with reference to FIG. 15 . A point corresponding to a child vertex of a root vertex according to embodiments may be a point having a coordinate value indicating a position closest to a coordinate value indicating a position of a point corresponding to the root vertex among points of point cloud data. That is, a point corresponding to a child vertex of the current vertex according to embodiments is a point corresponding to the current vertex among points of point cloud data excluding points corresponding to vertices having a depth greater than the depth of the current vertex. It may be a point having a coordinate value indicating the position of , and a coordinate value indicating the closest position. A point having a coordinate value indicating a position closest to a coordinate value indicating a position of a point corresponding to the current vertex according to embodiments may be any one of the points selected based on the order in which the above-described points are rearranged. have. For example, with respect to three points having a Morton code value close to the Morton code value of the point corresponding to the current vertex, the Euclidean separation distance from the point corresponding to the current vertex is compared to the point having the smallest separation distance. can be searched for as a point corresponding to a child vertex. A current vertex according to embodiments may have two or more child vertices. For example, if there are two or more points having a separation distance equal to or less than a predetermined distance from a point corresponding to the current vertex, the points having a separation distance equal to or less than a predetermined distance value are points corresponding to child vertices of the current vertex. can be added as The preset distance value according to embodiments may be preset by a user. Information indicating a preset distance value according to embodiments may be included in signaling information regarding a prediction geometry encoding scheme included in a bitstream and delivered to the receiving device. 1602b indicates a point corresponding to a leaf vertex. As described above with reference to FIG. 15 , signaling information (or signaling information) regarding a prediction geometry encoding scheme included in a bitstream may be generated for each vertex and delivered to the receiving device. The signaling information about the prediction geometry encoding scheme generated for each vertex according to embodiments may include parent vertex index information, child vertex number information, and/or child vertex index information of the current vertex. Parent vertex index information according to embodiments is information indicating a parent vertex of a current vertex. Child vertex recount information according to embodiments is information indicating the number of child vertices of a current vertex. Child vertex index information according to embodiments is information indicating a child vertex of a current vertex.

1603 indicates an order of performing geometry encoding based on the prediction tree. As described above with reference to FIG. 15 , a prediction tree according to embodiments may be generated in a direction in which a depth of a vertex increases. The prediction tree-based geometry encoding according to the embodiments may be performed according to the order in which the prediction tree is generated. That is, prediction tree-based geometry encoding according to embodiments may be performed according to an increasing order of depths of vertices. For example, a point 1603a corresponding to a root vertex may be geometrically encoded before a point 1603b corresponding to a leaf vertex.

As described above in FIG. 15 , the prediction tree-based geometry encoding according to embodiments is a prediction value (in FIG. 15 ) based on a prediction mode (prediction mode described in FIG. 15 ) for a point corresponding to each vertex of the vertices of the prediction tree. The process of calculating the predicted value) and the prediction residual value (the prediction residual value described with reference to FIG. 15 ) may be included. Descriptions of the prediction mode, the prediction value, and the prediction residual value according to the embodiments are the same as or similar to those described with reference to FIG. 15 . As described above with reference to FIG. 15 , the prediction mode according to the embodiments represents a mode of a method of calculating a prediction value of a point corresponding to a vertex. The prediction value according to embodiments may indicate a prediction value of a point corresponding to a current vertex. A predicted value of a point corresponding to a current vertex according to embodiments may be calculated from a coordinate value indicating positions of one or more points corresponding to one or more vertices having a depth smaller than a depth of the current vertex. Also, the prediction residual value according to embodiments may represent a difference between a predicted value of a point corresponding to a current vertex and a coordinate value indicating an actual position of a point corresponding to the current vertex. The prediction value and the prediction residual value according to embodiments may include an x-coordinate value, a y-coordinate value, and a z-coordinate value.

Prediction mode according to embodiments is No prediction mode (or first mode), Delta Prediction mode (or second mode), Linear Prediction mode (or third mode) and / or Parallelogrm predictor mode (or fourth mode) may include A coordinate value indicating a position of a point corresponding to a current vertex according to embodiments is p, a coordinate value indicating a position of a point corresponding to a vertex (or parent of the current vertex) having a depth smaller by 1 than the depth of the current vertex is p0, a coordinate value indicating the location of a point corresponding to a vertex (or grandparent of the current vertex) having a depth smaller than the depth of the current vertex by 2, p1, a vertex having a depth smaller than the depth of the current vertex by 3 (or It is assumed that the coordinate value indicating the position of the point corresponding to the grand-grandparent of the current vertex is p2. A prediction value of a point corresponding to a current vertex according to embodiments may be calculated based on a prediction mode. A prediction mode according to embodiments may indicate a mode of a method of calculating a prediction value based on at least one of 0, p0, p1, and p2.

The first mode according to the embodiments may indicate a mode in which the above-described geometry prediction is not performed. That is, a prediction value of a point corresponding to a current vertex based on a first mode (No prediction mode) according to embodiments may represent (0, 0, 0) (or 0). Accordingly, a prediction residual value of a point corresponding to a current vertex corresponding to the first mode according to embodiments may represent -p. A prediction value of a point corresponding to a current vertex based on a second mode (Delta prediction mode) according to embodiments may indicate p0. Accordingly, the prediction residual value of the point corresponding to the current vertex corresponding to the second mode according to the embodiments may represent p0-p. A prediction value of a point corresponding to a current vertex based on a third mode (linear prediction mode) according to embodiments may represent 2p0-p1. Accordingly, a prediction residual value of a point corresponding to a current vertex corresponding to the third mode according to embodiments may represent 2p0-p1-p. A predicted value of a point corresponding to a current vertex based on a fourth mode (parallelogram predictor mode) according to embodiments may represent 2p0+p1-p2. Accordingly, a prediction residual value of a point corresponding to a current vertex corresponding to the third mode according to embodiments may represent 2p0+p1-p2-p. The information on the prediction mode according to the embodiments is included in the signaling information on the prediction geometry encoding scheme included in the bitstream (eg, the signaling information on the prediction geometry encoding scheme described in FIG. 15) to the receiving device. can be transmitted. The information on the prediction mode according to the embodiments may indicate information indicating any one of the first to fourth modes described above.

The prediction mode used in the transmission apparatus (eg, the prediction tree structure generator 1504 of FIG. 15 ) according to the embodiments may be used in the reception apparatus as well. For example, the transmitting device may indicate any one of the first to fourth modes to the receiving device through the information about the prediction mode. The receiving device may determine the prediction mode and calculate the prediction value based on the information about the prediction mode. The transmitting apparatus according to the embodiments may transmit information about the prediction mode and the prediction residual value to the receiving apparatus, and the receiving apparatus may reconstruct the geometry of points based on the calculated prediction value and the received prediction residual value.

17 illustrates an example of a method of calculating a modified prediction value according to embodiments.

17 shows an example of a method for calculating a revised prediction value (or a revised prediction value). The modified prediction value may be calculated by modifying the method of calculating the prediction value described above in FIG. 16 . The method of calculating the predicted value described above in FIG. 16 may be referred to as a first method, and the method of calculating the corrected prediction value may be referred to as a second method. The first method according to the embodiments may be modified as the second method based on the order in which the points are rearranged. The first method according to the embodiments may be modified as the second method based on a prediction value calculated based on the first method and a threshold value.

The modified prediction value according to the embodiments may mean a prediction value calculated by a method different from the method of calculating the prediction value described above with reference to FIGS. 15 to 16 . The modified prediction value according to the embodiments may be calculated based on the modified prediction mode (or the modified prediction mode). A method of calculating a prediction value according to embodiments may be modified based on the order in which points are rearranged. For example, the rearranged points may be arranged in an ascending order of coordinate values indicating positions of the points. That is, as the depth of the vertex of the prediction tree increases, the size of the coordinate value indicating the position of the point corresponding to the vertex may increase. Also, the rearranged points may be arranged in descending order of coordinate values indicating the positions of the points. That is, as the depth of the vertex of the prediction tree increases, the size of the coordinate value indicating the position of the point corresponding to the vertex may decrease. The predicted value calculated based on the arrangement in descending order of the coordinate values indicating the positions of the points is compared to the predicted values calculated based on the arrangement in the ascending order of the coordinate values indicating the positions of the points, the actual position of the point corresponding to the current vertex The difference from the displayed coordinate value may be greater. The transmitting apparatus (eg, the prediction tree structure generator 1504 of FIG. 15 ) according to embodiments may modify a method of calculating a prediction value based on the order in which the points are rearranged. For example, the transmitting device may modify a method of calculating the predicted value in response to the coordinate values indicating the positions of the rearranged points being arranged in descending order. The transmitting apparatus according to the embodiments modifies a method of calculating a prediction value based on the order in which the points are rearranged, reduces the prediction residual value, reduces the size of a bitstream transmitted to the receiving apparatus, and reduces latency in the encoding/decoding process can be adjusted

1700 indicates rearranged points 1700a of point cloud data according to embodiments and a prediction tree 1700b generated based on the rearranged points.

As described above with reference to FIGS. 15 to 16 , the points of the point cloud data may be rearranged according to an arbitrary sorting order for the geometry of the points. In addition, as described above with reference to FIG. 16 , the predicted value of a point corresponding to a current vertex according to embodiments indicates the location of one or more points corresponding to one or more vertices having a depth smaller than the depth of the current vertex. It can be calculated from the indicated coordinate values. For example, a prediction value of a point corresponding to a current vertex based on a third mode (Linear prediction mode) may be expressed as 2p0-p1, and a prediction residual value may be expressed as 2p0-p1-p. Based on the fourth mode (parallelogram predictor mode), the predicted value of the point corresponding to the current vertex may be 2p0+p1-p2, and the prediction residual value may represent 2p0+p1-p2-p. Descriptions of the third mode, the fourth mode, p, p0, p1, and p2 are the same as those described with reference to FIG. 16 .

The second method according to embodiments may be in a reverse order of the first method. That is, the modified prediction mode according to the embodiments may represent a reverse order of the method of calculating the existing prediction value. Accordingly, the predicted value of the point corresponding to the current vertex based on the modified first mode (or the modified first mode) according to embodiments may represent (0, 0, 0). The predicted value of the point corresponding to the current vertex based on the modified second mode (or the modified second mode) according to embodiments may indicate -p0. The predicted value of the point corresponding to the current vertex based on the modified third mode (or the modified third mode) according to embodiments may indicate p1-2p0. The predicted value of the point corresponding to the current vertex based on the fourth modified mode (or the fourth modified mode) according to embodiments may indicate p2-p1-2p0. Accordingly, the prediction residual value of the point corresponding to the current vertex based on the modified first mode according to the embodiments may represent -p. A prediction residual value of a point corresponding to a current vertex based on the modified second mode according to embodiments may represent -p0-p. A prediction residual value of a point corresponding to a current vertex based on the modified third mode according to embodiments may represent p1-2p0-p. A prediction residual value of a point corresponding to a current vertex based on the modified fourth mode according to embodiments may indicate p2-p1-2p0-p. The information about the above-described second method may be included in signaling information about the prediction geometry encoding scheme included in the bitstream and delivered to the receiving device. The second method according to the embodiments is not limited to the above-described example. That is, the second method according to the embodiments may be expressed as a*p0+b*p1+c*p2. The a, b, and c values of a*p0+b*p1+c*p2 may have values representing the reverse order of the first method, and may have values other than the values representing the reverse order of the first method. can also

The coordinate value indicating the position of the point corresponding to the current vertex is 1, the coordinate value indicating the position of the point corresponding to the vertex having a depth that is 1 less than the depth of the current vertex is 2, and the depth is 2 less than the depth of the current vertex. It is assumed that a coordinate value indicating a position of a point corresponding to a branch vertex is 5, and a coordinate value indicating a position of a point corresponding to a vertex having a depth smaller by 3 than the depth of the current vertex is 12. A prediction residual value of a point corresponding to a current vertex according to the third mode according to embodiments may be calculated as 2*2-5-1 = -2. Also, a prediction residual value of a point corresponding to a current vertex according to the fourth mode according to embodiments may be calculated as 2*2+5-12-1 = -4. However, the prediction residual value of the point corresponding to the current vertex according to the modified third mode according to the embodiments may be calculated as 5-2*2-1 = 0. Also, a prediction residual value of a point corresponding to a current vertex according to the modified fourth mode according to embodiments may be calculated as 12-5-2*2-1 = 2. That is, the prediction residual value based on the second method according to embodiments may have a smaller value than the prediction residual value based on the first method. Accordingly, the transmitting apparatus according to the embodiments may transmit the prediction residual value based on the second method to the receiving apparatus, thereby increasing the compression efficiency of the bitstream and adjusting the latency in the encoding/decoding process.

The rearranged points according to embodiments may simultaneously include points arranged in an ascending order of coordinate values indicating the positions of the points and points arranged in a descending order of coordinate values indicating the positions of the points. Accordingly, the second method according to the embodiments may be used only for points arranged in descending order of coordinate values indicating the positions of the points. That is, the second method according to the embodiments may be used adaptively for each point of the points. The transmitting apparatus (eg, the prediction tree structure generator 1504 of FIG. 15 ) according to embodiments may use a threshold value to adaptively apply the second method to each point. For example, if the prediction value based on the first method for the point corresponding to the current vertex is equal to or greater than the threshold value, the second method may be applied. If the prediction value based on the first method for the point corresponding to the current vertex is smaller than the threshold value, the first method may be applied. That is, when the first method is modified as the second method based on the threshold, both the prediction value based on the first method and the prediction value based on the second method for a point corresponding to the current vertex may be calculated. The threshold according to embodiments may be a preset value. Information about the threshold indicating the threshold according to the embodiments may be included in signaling information about the prediction geometry encoding scheme included in the bitstream and delivered to the receiving device.

1701 indicates that the first method and/or the second method according to the embodiments are modified based on a coefficient value. For example, the transmitter may multiply the calculated predicted value and/or the calculated corrected predicted value by a coefficient value. The predicted value multiplied by the coefficient value and/or the modified prediction value multiplied by the coefficient value according to the embodiments is the actual value of the point corresponding to the current vertex compared to the predicted value not multiplied by the coefficient value and/or the corrected prediction value not multiplied by the coefficient value. It is possible to represent a value closer to a coordinate value representing a location. The coefficient value according to the embodiments may be multiplied by the x-coordinate value, the y-coordinate value, and the z-coordinate value of the predicted value and/or the modified predicted value, respectively. Accordingly, the coefficient values according to the embodiments may include three values (eg, a value multiplied by an x-coordinate value, a b-value multiplied by a y-coordinate value, and a c value multiplied by a z-coordinate value). . Values included in the coefficient values according to embodiments may be the same as or different from each other. For example, a prediction value based on a third mode multiplied by a coefficient value (Linear Prediction mode) may include a(2p0x-p1x), b(2p0y-p1y), and c(2p0z-p1z). 2p0x-p1x represents the x-coordinate value of 2p0-p1, 2p0y-p1y represents the y-coordinate value of 2p0-p1, and 2p0z-p1z represents the z-coordinate value of 2p0-p1. The prediction value based on the fourth mode multiplied by the coefficient value (Parallelogram Predictor mode) may include a(2p0x+p1x-p2x), b(2p0y+p1y-p2y), and c(2p0z+p1z-p2z). 2p0x+p1x-p2x represents the x-coordinate value of 2p0+p1-p2, 2p0y+p1y-p2y represents the y-coordinate value of 2p0+p1-p2, and 2p0z+p1z-p2z represents the z-coordinate value of 2p0+p1-p2 indicates The coefficient value according to embodiments may include three or more values. For example, a value multiplied by the x-coordinate value of p0, a' value multiplied by the x-coordinate value of p1, b value multiplied by the y-coordinate value of p0, b' value multiplied by the y-coordinate value of p1, It may include a c value multiplied by the z-coordinate value of p0 and a c' value multiplied by the z-coordinate value of p1. That is, coefficient values according to embodiments may have different values with respect to p0, p1, and p2. For example, the prediction value based on the modified third mode multiplied by the coefficient value may include a*p1x-a'*2p0x, b*p1y-b'*2p0y, and c*p1z-c'*2p0z. The predicted values based on the fourth mode multiplied by the coefficient values are a*p2x-a'*2p0x-a''p1x, b*p2y-b'*2p0y-b''*p1y and c*p2z-c'*2p0z- It may contain c''*p1z. The coefficient value according to embodiments may be a preset value. Information about a coefficient value indicating a coefficient value according to embodiments may be included in signaling information about a prediction geometry encoding scheme included in a bitstream and delivered to the receiving device. The first method and/or the second method modified based on the above-described coefficient value may be referred to as a third method.

The transmission apparatus according to the embodiments (eg, the prediction tree structure generator 1504 of FIG. 15 ) predicts included in the bitstream by using the above-described modified prediction mode, modified prediction value, threshold value, and/or coefficient value. By reducing the size of the residual value, it is possible to increase the compression efficiency of the bitstream and to adjust the latency in the encoding/decoding process.

18 illustrates an example of a method of changing a prediction tree structure according to embodiments.

18 illustrates an example of a method of changing the structure of a prediction tree (or a prediction tree, for example, the prediction tree described in FIGS. 15 to 17 ) according to embodiments.

1800 indicates points located on a space (eg, the bounding box described with reference to FIG. 5 ) according to embodiments. Points according to embodiments may be points of point cloud data corresponding to category 3 data (eg, category 3 data described with reference to FIG. 16 ). Each of 1800a, 1800b, 1800c, and 1800d represents a point included in points according to embodiments. The distance between 1800a and 1800b is less than the distance between 1800a and 1800c and the distance between 1800a and 1800d. The distance between 1800a and 1800c is greater than the distance between 1800a and 1800b and the distance between 1800a and 1800d. The distance between 1800a and 1800d is greater than the distance between 1800a and 1800b and less than the distance between 1800a and 1800c.

1801 indicates the structure of a prediction tree (eg, the prediction tree described with reference to FIGS. 15 to 17 ) generated based on 1800 . 1800a may be a point corresponding to the root vertex 1801a (eg, the root vertex described with reference to FIGS. 15 and 16 ). 1800b may be a point corresponding to the child vertex 1801b of the root vertex (eg, the child vertex described with reference to FIGS. 15 and 15 ). That is, 1800b may be a point located closest to 1800a among points of the point cloud data. The transmitting apparatus according to embodiments (eg, the prediction tree structure generator 1504 of FIG. 15 ) may search for a point located closest to 1800b among the points in response to the search for 1800b. The transmitting apparatus according to embodiments may search for 1800c as a point located closest to 1800b. Accordingly, 1800c may be registered as a point corresponding to the grandchild vertex 1801c of the root vertex. As described above with reference to FIGS. 15 and 16 , a prediction tree (or a prediction tree) according to embodiments may be generated by searching for points in a direction in which the depth of a vertex increases. That is, the grandchild vertex of the root vertex according to embodiments may have a depth greater than the depth of the root vertex by two. A direction in which a depth of a vertex according to embodiments increases may be based on geometric adjacency (eg, geometric recognizability described with reference to FIG. 15 ). That is, a point corresponding to a child vertex of a current vertex of the vertices of the prediction tree according to embodiments may be a point located closest to a point corresponding to the current vertex among points of point cloud data. For example, the transmitting device searches for 1800c as a point closest to 1800b and registers it as a child vertex of a vertex corresponding to 1800b, searches the point closest to 1800c again, and returns a vertex corresponding to 1800c. It can be registered as a child vertex. That is, the transmitter may search for a point located closest to 1800c in the direction of an arrow having 1800b and 1800c as both ends. As a result, the points arranged in the direction in which the depths of the vertices 1801a to 1801c of 1800 are increased may be the same as or similar to the points searched in the direction of the arrow pointing to both ends of 1800a and 1800c. When a point is searched for in the above-described direction, it may not be possible to search for 1800d that is actually closer to 1800a than 1800c before 1800c. That is, when a prediction tree is generated by searching for a point in the above-described direction, even though 1800d is actually located closer to 1800a than 1800c, the depth of the vertex 1801d corresponding to 1800d is greater than the depth of the vertex corresponding to 1800c. can have a large value. The transmitting apparatus (eg, the prediction tree structure transformation unit 1504c of FIG. 15 ) according to embodiments may change the structure of the prediction tree by changing a method of searching for points in order to solve the above-described problem.

1802 indicates a prediction tree having a changed structure according to embodiments. The prediction tree structure transformation unit 1504c according to embodiments may change the structure of the prediction tree by changing a method of searching for points. The prediction tree structure transformation unit 1504c according to the embodiments may search again for points that are not searched in the existing prediction tree point search process. For example, the prediction tree structure transformation unit may search 1800c and register it as 1801c, and search for points located between 1800c and 1800a. The prediction tree structure transformation unit may search 1800d as a point located between 1800c and 1800a and register it as a child vertex of 1801a or a child vertex of 1801b. 1802 is an example in which the prediction tree structure transformation unit according to the embodiments registers the vertex 1802d corresponding to the searched 1800d as a child vertex of the vertex 1802b corresponding to 1800b. Accordingly, the prediction tree 1802 having the changed structure includes 1802a to 1802d. In the prediction tree having the changed structure according to the embodiments, since a point corresponding to 1802d is searched and added as a point corresponding to a child vertex of 1802a or 18012, a depth smaller than the total number of depths of the prediction tree structure before the change can have the number of The information about the prediction tree structure according to the embodiments may be included in signaling information about the prediction geometry encoding scheme included in the bitstream and delivered to the receiving device. The above-described information on the structure of the prediction tree indicates information about the first structure in response to the prediction tree having the first structure, and information on the second structure in response to the prediction tree having the second structure. can indicate

The changed structure according to the embodiments may be referred to as a second structure, and the structure before the change may be referred to as a first structure. As described above, the number of total depths of the prediction tree (or prediction tree) having the second structure may be smaller than the total number of depths of the prediction tree having the first structure.

The prediction tree structure transformation unit according to the embodiments uses the prediction tree having the changed structure described in this figure to solve the problem that the geometrical adjacency of points is not reflected as the depth of the existing prediction tree increases, and the entire prediction tree By reducing the depth, it is possible to increase bitstream compression efficiency.

19 shows the structure of a bitstream according to embodiments.

The point cloud processing apparatus (eg, the transmission apparatus described with reference to FIGS. 1, 12 and 14 ) may transmit the encoded point cloud data in the form of a bitstream. A bitstream is a sequence of bits that forms a representation of point cloud data (or point cloud frame).

Point cloud data (or point cloud frame) may be divided into tiles and slices.

Point cloud data may be partitioned into multiple slices and encoded within a bitstream. One slice is a set of points and is expressed as a series of syntax elements representing all or part of encoded point cloud data. One slice may or may not have a dependency on other slices. In addition, one slice includes one geometry data unit, and may or may not have one or more attribute data units (zero attribute data unit). As described above, since the attribute encoding is performed based on the geometry encoding, the attribute data unit is based on the geometry data unit within the same slice. That is, the point cloud data receiving device (for example, the receiving device 10004 or the point cloud video decoder 10006) may process the attribute data based on the decoded geometry data. Therefore, within a slice, a geometry data unit must appear before the associated attribute data units. Data units within a slice are necessarily contiguous, and the order between slices is not specified.

A tile is a rectangular cuboid (three-dimensional) in a bounding box (eg, the bounding box described in FIG. 5). A bounding box may contain one or more tiles. One tile may completely or partially overlap another tile. One tile may include one or more slices.

Accordingly, the point cloud data transmitting apparatus may provide high-quality point cloud content by processing data corresponding to a tile according to importance. That is, the point cloud data transmission apparatus according to the embodiments may perform point cloud compression coding processing on data corresponding to an area important to a user with better compression efficiency and appropriate latency.

A bitstream according to embodiments includes signaling information and a plurality of slices (slice 0, ..., slice n). As shown in the figure, signaling information appears before slices in the bitstream. Accordingly, the point cloud data receiving apparatus may first secure signaling information and sequentially or selectively process a plurality of slices based on the signaling information. As shown in the figure, slice 0 (slice0) includes one geometry data unit (Geom00) and two attribute data units (Attr00, Attr10). Also, geometry data units appear before attribute data units within the same slice. Therefore, the point cloud data receiving apparatus first processes (decodes) the geometry data unit (or geometry data), and processes the attribute data unit (or attribute data) based on the processed geometry data. The signaling information according to the embodiments may be referred to as signaling data, metadata, or the like, and is not limited to examples.

The signaling information according to the embodiments includes a sequence parameter set (SPS), a geometry parameter set (GPS), and one or more attribute parameter sets (APS). The SPS is encoding information for the entire sequence, such as profile and level, and may include comprehensive information (sequence level) for the entire sequence, such as picture resolution and video format. GPS is information about the geometry encoding applied to the geometry included in the sequence (bitstream). The GPS may include information on an octree (eg, the octree described in FIG. 6 ), information on an octree depth, and the like. APS is information on attribute encoding to which an attribute is included in a sequence (bitstream). As shown in the figure, the bitstream includes one or more APSs (eg, APS0, APS1.. shown in the figure) according to an identifier for identifying an attribute.

The signaling information according to embodiments may further include a TPS. The TPS is information about a tile, and may include information about a tile identifier, a tile size, and the like. The signaling information according to the embodiments is information of a sequence, that is, a bitstream level, and is applied to a corresponding bitstream. In addition, the signaling information has a syntax structure including a syntax element and a descriptor for describing it. A pseudo code for describing the syntax may be used. Also, the point cloud receiving apparatus may sequentially parse and process the syntax elements appearing in the syntax.

Although not shown in the drawings, the geometry data unit and the attribute data unit according to the embodiments include a geometry header and an attribute header, respectively. The geometry header and the attribute header according to the embodiments have the above-described syntax structure as signaling information applied at a corresponding slice level.

A geometry header according to embodiments includes information (or signaling information) for processing a corresponding geometry data unit. Therefore, the geometry header appears first in the corresponding geometry data unit. The point cloud receiving apparatus may process the geometry data unit by first parsing the geometry header. The geometry header has a relationship with the GPS including information on the entire geometry. Accordingly, the geometry header includes information specifying gps_geom_parameter_set_id included in GPS. Also, the geometry header includes tile information (eg, tile_id) related to the slice to which the geometry data unit belongs, and a slice identifier.

The attribute header according to the embodiments includes information (or signaling information) for processing the corresponding attribute data unit. Therefore, the attribute header appears first in the corresponding attribute data unit. The point cloud receiving apparatus may process the attribute data unit by first parsing the attribute header. The attribute header has a relationship with the APS that includes information about all attributes. Accordingly, the attribute header includes information specifying aps_attr_parameter_set_id included in the APS. As described above, since attribute decoding is based on geometry decoding, the attribute header includes information specifying the slice identifier included in the geometry header in order to determine the geometry data unit associated with the corresponding attribute data unit.

20 illustrates a Sequantial Parameter Set (SPS) structure of point cloud data according to embodiments.

A bitstream of point cloud data according to embodiments may include a sequential parameter set (SPS) including signaling information (or flags) of this figure. The sequential parameter set in this figure may refer to the sequential parameter set 27001 described with reference to FIG. 19 . The point cloud data receiver according to the embodiments may decode the point cloud data according to the embodiments based on the signaling information (or flag information) of this figure.

The profile (profile_idc) may mean information indicating a profile of a bitstream that can satisfy Annex A of the H.264 standard document. Other values of profile_idc may be used later by ISO/IEC. (indicates a profile to which the bitstream conforms as specified in Annex A. Bitstreams shall not contain values of profile_idc other than those specified in Annex A. Other values of profile_idc are reserved for future use by ISO/IEC.)

If the profile compatibility flag (profile_compatibility_flags) is 1, it may indicate that the corresponding bitstream satisfies a profile in which profile_idc is j according to Annex A. The value of profile_compatibility_flag[ j ] may be 0 if j has a value other than the value defined according to Annex A. (equal to 1, indicates that the bitstream conforms to the profile indicated by profile_idc equal to j as specified in Annex A. The value of profile_compatibility_flag[ j ] shall be equal to 0 for any value of j that is not specified as an allowed value of profile_idc in Annex A.)

The level IDC (level_idc) indicates the level of the bitstream that can satisfy Annex A of the H.264 standard document. The bitstream is information different from the information defined in Annex A of the H.264 standard document and does not have a level_idc value. Other values of Level_idc are reserved for future use by ISO/IEC. (indicates a level to which the bitstream conforms as specified in Annex A. Bitstreams may not contain values of level_idc other than those specified in Annex A. Other values of level_idc are reserved for future use by ISO/IEC.)

The SPS bounding box presence flag (sps_bounding_box_present_flag) may be 1 when the bounding box offset and size information are signaled. (equal to 1 specifies the bounding box offset and size information is signaled. sps_bounding_box_present_flag equal to 0 specifies)

When the SPS bounding box presence flag (sps_bounding_box_present_flag) has a true value, the SPS according to the embodiments is an SPS bounding box x offset (sps_bounding_box_offset_x), an SPS bounding box y offset (sps_bounding_box_offset_z), SPS bounding_box_offset_y), SPS bounding_box_offset (sps_bounding_box_offset_y) It further includes a box scale factor (sps_bounding_box_scale_factor), an SPS bounding box width size (sps_bounding_box_size_width), an SPS bounding box height size (sps_bounding_box_size_height), and an SPS bounding box depth size (sps_bounding_box_size_depth).

The SPS bounding box x offset (sps_bounding_box_offset_x) indicates the x offset of the original bounding box of the Cartesian coordinate system. If the corresponding information does not exist, the value of this parameter may be 0. (indicates the x offset of the source bounding box in the cartesian coordinates. When not present, the value of sps_bounding_box_offset_x is inferred to be 0.)

The SPS bounding box y offset (sps_bounding_box_offset_y) indicates the y offset of the original bounding box of the Cartesian coordinate system. If the corresponding information does not exist, the value of this parameter may be 0. (indicates indicates the y offset of the source bounding box in the cartesian coordinates. When not present, the value of sps_bounding_box_offset_y is inferred to be 0.)

The SPS bounding box z offset (sps_bounding_box_offset_z) indicates the z offset of the original bounding box of the Cartesian coordinate system. If the corresponding information does not exist, the value of this parameter may be 0. (indicates indicates the z offset of the source bounding box in the Cartesian coordinates. When not present, the value of sps_bounding_box_offset_z is inferred to be 0.)

The SPS bounding box scale factor (sps_bounding_box_scale_factor) indicates the scale factor of the original bounding box in the Cartesian coordinate system. If the corresponding information does not exist, the value of this parameter may be 1 or 0. (indicates the scale factor the source bounding box in the Cartesian coordinates. When not present, the value of sps_bounding_box_scale_factor is inferred to be 1. Indicates. When not present, the value of sps_bounding_box_scale_factor is inferred to be 0.)

The SPS bounding box width size (sps_bounding_box_size_width) indicates the width of the original bounding box in the Cartesian coordinate system. When the corresponding information does not exist, the value of sps_bounding_box_size_width may be a specific value such as 10. (indicates the width of the source bounding box in the Cartesian coordinates. … When not present, the value of sps_bounding_box_size_width is inferred to be a specific value (such as 10).)

The SPS bounding box height size (sps_bounding_box_size_height) indicates the height of the original bounding box in the Cartesian coordinate system. When the corresponding information does not exist, the value of sps_bounding_box_size_height may be 1 or 0. (indicates the height of the source bounding box in the Cartesian coordinates. When not present, the value of sps_bounding_box_size_height is inferred to be 1. When not present, the value of sps_bounding_box_size_hieght is inferred to be 0.)

The SPS bounding box depth size (sps_bounding_box_size_depth) indicates the depth of the original bounding box in the Cartesian coordinate system. When the corresponding information does not exist, the value of sps_bounding_box_size_height may be 1 or 0. (indicates the depth of the source bounding box in the Cartesian coordinates. When not present, the value of sps_bounding_box_size_depth is inferred to be 1. When not present, the value of sps_bounding_box_size_depth is inferred to be 0.)

The SPS source scale factor (sps_source_scale_factor) indicates the scale factor of the original point cloud. (Indicates the scale factor of the source point cloud.)

The SPS sequential parameter set ID (sps_seq_parameter_set_id) indicates id information for the SPS referenced by another syntax element. sps_seq_parameter_set_id may be set to a value of 0 to 15 within a range that satisfies the conditions in the specification of the corresponding version. As information other than 0, sps_seq_parameter_set_id may be used later by ISO/IEC. (provides an identifier for the SPS for reference by other syntax elements. In The value of sps_seq_parameter_set_id may be in the range of 0 to 15, inclusive in bitstreams conforming to this version of this Specification.. The value other than 0 for sps_seq_parameter_set_id is reserved for future use by ISO/IEC.)

The number of SPS attribute sets (sps_num_attribute_sets) indicates the number of coded attributes in the bitstream. sps_seq_parameter_set_id may have a range of 0 to 64. (indicates the number of coded attributes in the bitstream. The value of sps_num_attribute_sets may be in the range of 0 to 64.)

The attribute dimension (attribute_dimension[ i ]) indicates the number of components of the i-th attribute. (Specifies the number of components of the i-th attribute.) The index i may be greater than or equal to 0, and may be less than a value indicated by the number of SPS attribute sets (sps_num_attribute_sets).

The attribute instance (attribute_instance_id[ i ]) represents the attribute instance id. (Specifies attribute instance id.)

The attribute bit depth (attribute_bitdepth[i]) indicates bitdepth information of the i-th attribute signal(s). (Specifies the bitdepth of the i-th attribute signal(s).)

The attribute CICP color primary (attribute_cicp_colour_primaries[i]) indicates the chromaticity of color attribute source primaries. (indicates the chromaticity coordinates of the color attribute source primaries.)

The attribute CICP transfer characteristic (attribute_cicp_transfer_characteristics[ i ]) represents the reference optoelectronic transfer characteristic function of the color attribute, consisting of the original input linear optical intensity Lc and a nominal real-value between 0 and 1. Alternatively, this parameter may represent the inverse of a reference optoelectronic transfer characteristic function, consisting of an output linear optical intensity Lo and a nominal real-value ranging from 0 to 1. (either indicates the reference opto-electronic transfer characteristic function of the color attribute as a function of a source input linear optical intensity Lc with a nominal real-valued range of 0 to 1 or indicates the inverse of the reference electro-optical transfer characteristic function as a function of an output linear optical intensity Lo with a nominal real-valued range of 0 to 1. )

The attribute CICP matrix coeffs(attribute_cicp_matrix_coeffs[ i ]) represents matrix coefficients of luma and chroma signals of green, blue and red (or the three primary colors of Y, Z, and X). (describes the matrix coefficients used in deriving luma and chroma signals from the green, blue, and red, or Y, Z, and X primaries.)

The attribute CICP video fullrange flag (attribute_cicp_video_full_range_flag[ i ]) is the black level and luma derived from E'Y, E'PB and E'PR or E'R, E'G and E'B real-value component signals Indicates the range of the saturation signal. (specifies indicates the black level and range of the luma and chroma signals as derived from E′Y, E′PB, and E′PR or E′R, E′G, and E′B real-valued component signals.)

When the known attribute label flag (known_attribute_label_flag[ i ]) is 1, it indicates that know_attribute_label is signaled for the i-th attribute. When the corresponding parameter is 0, it indicates that attribute_label_four_bytes is signaled for the i-th attribute. (equal to 1 specifies know_attribute_label is signaled for the i-th attribute. known_attribute_label_flag[ i ] equal to 0 specifies attribute_label_four_bytes is signaled for the i-th attribute. )

When the known attribute label (known_attribute_label[ i ]) is 0, it indicates that the attribute is color. If the corresponding parameter is 1, it indicates that the attribute is reflectance. If the corresponding parameter is 2, it indicates that the attribute is a frame index. (equal to 0 specifies the attribute is colour. known_attribute_label[ i ] equal to 1 specifies the attribute is reflectance. known_attribute_label[ i ] equal to 2 specifies the attribute is farme index.)

The SPS according to the embodiments may further include signaling information (or signaling information about a prediction geometry encoding scheme) regarding a prediction tree (eg, a prediction tree or a prediction tree of FIGS. 15 to 19 ).

Information (predictive_tree_geometry_coding_flag) indicating whether to use a prediction geometry encoding scheme according to embodiments is a prediction geometry encoding scheme (eg, FIG. 15 ) in geometry coding (eg, the geometry coding described in FIGS. 1 to 19 ). to 19) are generated (or used). If predictive_tree_geometry_coding_flag according to embodiments indicates that the prediction geometry encoding scheme is used (eg, a true value), the prediction geometry encoding scheme is used in the geometry coding. When predictive_tree_geometry_coding_flag according to embodiments indicates a value corresponding to that the prediction geometry encoding scheme is not used (eg, a false value), the prediction geometry encoding scheme is not used in the geometry coding.

If the predictive_tree_geometry_coding_flag according to the embodiments indicates a value corresponding to true, the SPS (or signaling information about the prediction geometry encoding scheme) is information about the reordering of points (sorting_order), a first method (eg, FIGS. 15 to The first method described in FIG. 18) may further include information on whether to use (prediction_method_use_flag), information on whether to generate a molton code of a prediction residual value (substitute_residual_flag), and/or information on the structure of a prediction tree (predictive_tree_structure). .

sorting_order according to embodiments may indicate information on the sorting order described above with reference to FIG. 15 . For example, if sorting_order indicates 0, it indicates that the points are rearranged according to a morton order (eg, the morton order described in FIG. 15 ). When sorting_order indicates 1, it indicates that points are rearranged according to an azimuth order (eg, the azimuth order described in FIG. 15 ). When sorting_order indicates 2, it indicates that the points are rearranged according to a radius order (eg, the radial distance order described with reference to FIG. 15 ).

The prediction_method_use_flag according to embodiments may indicate whether to use the first method (eg, the first method described with reference to FIG. 17 ). For example, if the prediction_method_use_flag has a value indicating information indicating that the first method is used (eg, a true value), the prediction value of the points may be calculated using the first method. When the prediction_method_use_flag has a value indicating information indicating that the first method is not used (eg, a false value), the method for calculating the modified prediction value may be used to calculate the corrected prediction value of the points.

As described above in FIG. 17 , the first method may be modified to the second method based on the rearranged order of points. Corresponding to that the first method according to the embodiments is modified to the second method based on the order in which the points are rearranged, the SPS (or signaling information about the prediction geometry encoding scheme) indicates that the first method is not used Information on whether to use the first method (eg, prediction_method_use_flag = false) and information on whether to use the second method (additional_prediction_method) may be included. The additional_prediction_method according to embodiments may indicate information on a value, b value, and c value of a*p0+b*p1+c*p2 described above with reference to FIG. 17 .

As described above with reference to FIG. 17 , the first method may be modified to the second method based on the predicted value and the threshold calculated based on the first method. Corresponding to that the first method according to the embodiments is modified by the second method based on the prediction value and the threshold value calculated based on the first method, the SPS (or signaling information about the prediction geometry encoding scheme) is the first It may include information about whether to use the first method indicating that the method is used (eg, prediction_method_use_flag = true), information about the second method (additional_prediction_method), and information about a threshold value (prediction_method_threshold). The additional_prediction_method according to embodiments may indicate information on a value, b value, and c value of a*p0+b*p1+c*p2 described above with reference to FIG. 17 . The prediction_method_threshold according to embodiments may be information indicating the above-described threshold value.

As described above with reference to FIG. 17 , the first method and/or the second method may be modified as the third method based on the coefficient values. In response to the first method and/or the second method according to the embodiments being modified to the third method based on the coefficient value, the SPS (or signaling information about the prediction geometry encoding scheme) is information about the coefficient value ( prediction_method_coefficient) may be further included. The prediction_method_coefficient according to the embodiments may be information on a value indicating the coefficient value described with reference to FIG. 17 . That is, prediction_method_coefficient according to embodiments may indicate a value, b value, and c value that are respectively multiplied by the x-coordinate value, the y-coordinate value, and the z-coordinate value of the prediction value and/or the modified prediction value.

Information (substitute_residual_flag) on whether to generate a Morton code of a prediction residual value according to embodiments is transmitted by replacing the x-coordinate value, y-coordinate value, and z-coordinate value of the prediction residual value with one value as described above with reference to FIG. 15 . can indicate whether or not For example, if substitute_residual_flag has a value indicating true, the x-coordinate value, y-coordinate value, and bit value of the z-coordinate value of the prediction residual value are mixed and replaced with a Morton code (the Morton code described above in FIG. 4). can do. If substitute_residual_flag has a value indicating false, the x-coordinate value, y-coordinate value, and z-coordinate value of the prediction residual value are not substituted with a single value.

As described above with reference to FIG. 15 , the prediction residual value may be replaced with one value. That is, the prediction residual value according to the embodiments may be replaced with a Morton code generated based on bits of the x-coordinate value, the y-coordinate value, and the z-coordinate value of the prediction residual value. In response to generating the Molton code of the prediction residual value based on the bits of the x-coordinate value, the y-coordinate value, and the z-coordinate value of the prediction residual value according to the embodiments, the SPS (or prediction geometry encoding scheme) signaling information) may include information (eg, substitute_residual_flag=true) on whether to generate a molton code of the prediction residual value indicating that a molton code of the prediction residual value is generated.

The predictive_tree_structure according to embodiments may indicate information about the prediction tree structure (or information about the prediction tree structure). Information on the prediction tree structure according to the embodiments includes information on the number of child vertices of each vertex of the vertices, child vertex index information of the current vertex, parent vertex index information of the current vertex, and/or a depth that is smaller than the depth of the current vertex by 2 The branch may include vertex index information. Descriptions of the current vertex, child vertex, and parent vertex are the same as those described with reference to FIGS. 15 to 18 . Descriptions of parent vertex index information, child vertex number information, and child vertex index information are the same as described above with reference to FIG. 16 . The prediction tree structure according to the embodiments may represent the first structure of the prediction tree described above with reference to FIGS. 15 to 18 or the second structure of the prediction tree.

As described above with reference to FIG. 18 , the prediction tree may have a first structure or a second structure. In response to the prediction tree according to the embodiments having the first structure, the information about the prediction tree structure may indicate information about the first structure. In response to the prediction tree according to embodiments having the second structure, the information about the prediction tree structure may indicate information about the second structure. As described above with reference to FIG. 18 , the total number of depths of the prediction tree having the second structure may be smaller than the total number of depths of the prediction tree having the first structure.

When the SPS extension presence flag (sps_extension_present_flag) is 1, it indicates that sps_extension_data exists in the SPS RBSP syntax structure. When the corresponding parameter is 0, it indicates that the corresponding syntax structure does not exist. If it does not exist, the value of sps_extension_present_flag may be 0. (equal to 1 specifies that the sps_extension_data syntax structure is present in the SPS RBSP syntax structure. sps_extension_present_flag equal to 0 specifies that this syntax structure is not present. When not present, the value of sps_extension_present_flag is inferred to be equal to 0.)

The SPS extension data flag sps_extension_data_flag may have any value. The existence of this parameter does not affect the behavior of the profile presented in Annex A of the corresponding standard document of the decoder. (may have any value. Its presence and value do not affect decoder conformance to profiles specified in Annex A Decoders conforming to a profile specified in Annex A.)

The signaling information included in the bitstream according to the embodiments is one of the metadata processing unit or the transmission processing unit (eg, the metadata processing unit 12007 or the transmission processing unit 12012 of FIG. 12 ) included in the point cloud data transmission apparatus. Or it can be created by more elements. The signaling information according to embodiments may be generated based on a result of performing geometry encoding and/or attribute encoding. The point cloud data transmitting apparatus according to the embodiments may transmit the bitstream in the above-described form, thereby increasing compression efficiency, increasing image quality performance, and reducing the burden on the receiving apparatus.

A bitstream of point cloud data according to embodiments may include a tile parameter set including signaling information (or flag) shown in this figure. The tile parameter set 28000 shown in this figure may refer to the tile parameter set 27004 described with reference to FIG. 19 . The point cloud data receiver according to the embodiments may decode the point cloud data according to the embodiments based on the signaling information (or flag information) described in this figure.

A Tile Parameter Set (TPS) 28000 may mean a syntax structure including syntax elements to which zero or more entire tiles (or coded tiles) are applied.

num_tiles indicates the number of tiles existing in the corresponding bitstream. (Represents the number of tiles signaled for the bitstream). If there is no tile existing in the corresponding bitstream, num_tiles may be signaled as 0. (When not present, num_tiles is inferred to be 0)

The TPS 28000 according to the embodiments includes information on positions at which tiles existing in a corresponding bitstream are located in a bounding box (eg, tile_bounding_box_offset_x, tile_bounding_box_offset_y, tile_bounding_box_offset_z, etc.), a scale factor in a bounding box of tiles. factor) information (eg, tile_bounding_box_scale_factor, etc.), width or height information (eg, tile_bounding_box_size_width, tile_bounding_box_size_height information) in the bounding box of tiles.

The TPS 28000 according to the embodiments may include parameters (eg, tile_bounding_box_offset_x, tile_bounding_box_offset_y, tile_bounding_box_offset_z, tile_bounding_box_scale_factor, tile_bounding_box_size_width_size_tile) included in the for statement of FIG. 27 by the number of tiles, respectively. In FIG. 27, i may mean an index for each tile. tile_bounding_box_offset_x [i], tile_bounding_box_offset_y [i], tile_bounding_box_offset_z [i], tile_bounding_box_scale_factor [i], tile_bounding_box_size_width [i], tile_bounding_box_size_height [i] is the i-th tile of _bounding_box_offset_x, tile_bounding_box_offset_y, tile_bounding_box_offset_z, tile_bounding_box_scale_factor, tile_bounding_box_size_width, tile_bounding_box_size_height information in each of the for statement can mean

tile_bounding_box_offset_x[ i ] indicates the x offset of the i-th tile in Cartesian coordinates. If the corresponding parameter does not exist (that is, if the tile_bounding_box_size_offset_x parameter for the non-zero i-th tile does not exist), the value of tile_bounding_box_offset_x[ 0 ] may mean sps_bounding_box_offset_x included in the SPS according to the embodiments. .

tile_bounding_box_offset_y[ i ] indicates the y offset of the i-th tile in Cartesian coordinates. If the corresponding parameter does not exist (that is, when the tile_bounding_box_size_offset_y parameter for the non-zero i-th tile does not exist), the value of tile_bounding_box_offset_y[ 0 ] may mean sps_bounding_box_offset_y included in the SPS according to the embodiments. .

tile_bounding_box_offset_z[ i ] indicates the z offset of the i-th tile in Cartesian coordinates. If the corresponding parameter does not exist (that is, when the tile_bounding_box_size_offset_z parameter for the non-zero i-th tile does not exist), the value of tile_bounding_box_offset_z[ 0 ] may mean sps_bounding_box_offset_z included in the SPS according to the embodiments. .

tile_bounding_box_scale_factor[ i ] indicates a scale factor associated with the i-th tile in Cartesian coordinates. If the corresponding parameter does not exist (that is, when the tile_bounding_box_size_factor parameter for the non-zero i-th tile does not exist), tile_bounding_box_scale_factor[ 0 ] may mean sps_bounding_box_scale_factor included in the SPS according to embodiments.

tile_bounding_box_size_width[ i ] represents the width of the i-th tile in Cartesian coordinates. If the corresponding parameter does not exist (that is, if the tile_bounding_box_size_width parameter for the non-zero i-th tile does not exist), tile_bounding_box_size_width[ 0 ] may mean sps_bounding_box_size_width included in the SPS according to the embodiments.

tile_bounding_box_size_height[ i ] represents the height of the i-th tile in Cartesian coordinates. If the corresponding parameter does not exist (that is, when the tile_bounding_box_size_height parameter for the non-zero ith tile does not exist), tile_bounding_box_size_height[ 0 ] may mean sps_bounding_box_size_height included in the SPS according to embodiments.

tile_bounding_box_size_depth[ i ] indicates the depth of the i-th tile in Cartesian coordinates. If there is no height value, tile_bounding_box_size_depth[ 0 ] may mean sps_bounding_box_size_depth included in the SPS according to embodiments.

The TPS according to the embodiments may further include signaling information (or signaling information about a prediction geometry encoding scheme) regarding a prediction tree (eg, a prediction tree or a prediction tree of FIGS. 15 to 19 ). Description of the signaling information regarding the prediction geometry encoding scheme according to the embodiments is the same as described above with reference to FIG. 20 .

A bitstream of point cloud data according to embodiments may include a geometry parameter set including signaling information (or flags) of this figure. The geometric parameter set of this figure may refer to the geometric parameter set 27002 described with reference to FIG. 19 . The point cloud data receiver according to the embodiments may decode the point cloud data according to the embodiments based on the signaling information (or flag information) of this figure.

The GPS parameter set ID (gps_geom_parameter_set_id) indicates an identifier of the GPS referenced by other syntax elements. The value of this parameter may be 0 to 15. (provides an identifier for the GPS for reference by other syntax elements. The value of gps_seq_parameter_set_id may be in the range of 0 to 15, inclusive.)

The GPS sequential parameter set ID (gps_seq_parameter_set_id) indicates the value of sps_seq_parameter_set_id for the corresponding active SPS. The value may be 0 to 15. (Specifies the value of sps_seq_parameter_set_id for the active SPS. The value of gps_seq_parameter_set_id shall be in the range of 0 to 15, inclusive.)

The geometry coding type (geometry_coding_type) may mean a coding type for geometry information. The value of the corresponding parameter may be 0 to 1, and other values may be used later by ISO/IEC. The decoder may ignore it if the corresponding parameter has a different value. The corresponding parameter may represent, for example, an octree if 0, and a triangle soup (trisoup) if 1. (indicates that the coding type for the geometry in Table 7 1Table 7 1 for the given value of geometry_coding_type. The value of geometry_coding_type shall be equal to 0 or 1 in bitstreams conforming to this version of this Specification. Other values of geometry_coding_type are reserved for future use by ISO/IEC.Decoders conforming to this version of this Specification may ignore reserved values of geometry_coding_type.0= Octree, 1=Triangle Soup (Trisoup))

The GPS bounding box presence flag (gps_box_present_flag) may be 1 when additional bounding box information is provided in a geometry header within the corresponding GPS. This parameter may indicate 0 when additional bounding box information is not provided in the geometry header. (equal to 1 specifies an additional bounding box information is provided in a geometry header that references the current GPS. gps_bounding_box_present_flag equal to 0 specifies that additional bounding box information is not signaled in the geometry header.)

The unique geometry point flag (unique_geometry_points_flag) may be 1 when all output points have unique positions. This parameter may be 0 if the output points are located at the same location. (equal to 1 indicates that all output points have unique positions. unique_geometry_points_flag equal to 0 indicates that the output points may have same positions.)

When the neighbor context restriction flag (neighbor_context_restriction_flag) is 0, it indicates that octree occupancy coding uses contexts determined based on 6 neighbor nodes. If 1, it indicates that octree occupancy coding uses contexts determined based only on sibling nodes. (equal to 0 indicates that octree occupancy coding uses contexts determined from six neighboring parent nodes. neighbor_context_restriction_flag equal to 1 indicates that octree coding uses contexts determined from sibling nodes only.)

When the inferred direct coding mode setting flag (inferred_direct_coding_mode_enabled_flag) is 0, it indicates that octree coding used inferred_direct_coding_mode. When 1, it indicates that octree coding was performed using a plurality of contexts determined from sibling neighboring nodes. (equal to 0 indicates the octree coding uses inferred_direct_coding_mode. inferred_direct_coding_mode_enabled_flag equal to 1 indicates the octree coding uses multiple context determined from sibling neighbouring nodes.)

log2_neighbour_avail_boundary indicates the value of NeighbAvailBoundary for which the decoding process is used as follows. (specifies the value of the variable NeighbAvailBoundary that is used in the decoding process as follows: )

NeighbAvailBoundary = 2log2_neighbour_avail_boundary

When neighbor_context_restriction_flag is 1, neighbor_context_restriction_flag may be 13. (When neighbor_context_restriction_flag is equal to 1, NeighbAvailabilityMask is set equal to 13.) When neighbor_context_restriction_flag is 0, NeighborAvailabilityMask may be determined as follows. (Otherwise, neighbor_context_restriction_flag equal to 0, NeighbAvailabilityMask is set equal to)

(1 << log2_neighbor_avail_boundary).

log2_trisoup_node_size represents TrisoupNodeSize as the size of triangle nodes determined as follows. (Specifies the variable TrisoupNodeSize as the size of the triangle nodes as follows.)

TrisoupNodeSize = 2log2_trisoup_node_size

The value of log2_trisoup_node_size may be greater than 0. When the size of log2_trisoup_node_size is 0, it may indicate that the geometry bitstream includes only the octree coding syntax. (The value of log2_trisoup_node_size may be equal to or greater than 0. When log2_trisoup_node_size is equal to 0, the geometry bitstream includes only the octree coding syntax.)

Trisoup_depth indicates the number of bits used to indicate each component of point coordinates. trisoup_depth may have a value from 2 to 21. (Specifies the number of bits used to represent each component of a point coordinate. The value of trisoup_depth may be in the range of 2 to 21. [Ed(df): 21 should perhaps be a level limit].)

The trisoup triangle level (trisoup_triangle_level) indicates a level at which an octree is pruned. trisoup_triangle_level may have a value from 1 to trisoup_depth-1. (Specifies the level at which the octree is pruned. The value of trisoup_triangle_level may be in the range of 1 to trisoup_depth-1.)

If the GPS extension presence flag (gps_extension_present_flag) is 1, it indicates that the gps_extension_data syntax structure is present in the GPS RBSP syntax structure. When it is 0, it indicates that the corresponding syntax structure does not exist. (equal to 1 specifies that the gps_extension_data syntax structure is present in the GPS RBSP syntax structure. gps_extension_present_flag equal to 0 specifies that this syntax structure is not present. When not present, the value of gps_ extension_present_flag is inferred to be equal to 0.)

The GPS extension presence flag (gps_extension_data_flag) may have any value. If the corresponding value is present, the value has no effect on the decoder. (may have any value. Its presence and value do not affect decoder conformance to profiles specified in Annex A. Decoders conforming to a profile specified in Annex A.)

The GPS according to embodiments may further include signaling information (or signaling information about a prediction geometry encoding scheme) related to a prediction tree (eg, a prediction tree or a prediction tree of FIGS. 15 to 19 ). Description of the signaling information regarding the prediction geometry encoding scheme according to the embodiments is the same as described above with reference to FIG. 20 .

A bitstream of point cloud data according to embodiments may include an attribute parameter set including signaling information (or flag) of this figure. The attribute parameter set in this figure may refer to the attribute parameter set 27003 described with reference to FIG. 19 . The point cloud data receiver according to the embodiments may decode the point cloud data according to the embodiments based on the signaling information (or flag information) of this figure.

The APS attribute parameter set ID (aps_attr_parameter_set_id) may indicate an identifier for the APS for reference according to other syntax elements. The value of aps_attr_parameter_set_id must be within the range of 0 to 15.

aps_seq_parameter_set_id may indicate a value of sps_seq_parameter_set_id for an active SPS. The value of aps_seq_parameter_set_id must be within the range of 0 to 15.

Lifting (isLifting) indicates whether a coding type for an attribute according to embodiments is a method based on a lifting method. For example, isLifting indicates whether the coding type is predicting weight lifting or fixed weight lifting. isLifting may have a specific value (eg, 0 or 1) to indicate whether the coding type for the attribute is a method based on the lifting method. For example, when the value of attr_coding_type according to the embodiments is 0 (ie, the coding type for the attribute is predictive weight lifting) or the value of attr_coding_type is 2 (ie, the coding type for the attribute is fixed) In case of weight lifting), isLifting may be 1 (ie, it may indicate that a coding type for an attribute according to embodiments is a method based on a lifting method). For example, when the value of attr_coding_type according to the embodiments is 1 (ie, when the coding type for the attribute is RAHT), isLifting may be 0 (ie, the coding type for the attribute according to the embodiments). It may indicate that the method is not based on this lifting method).

The APS according to the embodiments may include some or all of num_pred_nearest_neighbours, max_num_direct_predictors, lifting_search_range, lifting_quant_step_size, lifting_quant_step_size_chroma, lod_binary_tree_enabled_flag, and num_detail_levels_minus1 parameters when the isLifting information according to the embodiments is 1. Also, when the isLifting information according to the embodiments is 1, the APS 30000 according to the embodiments may include sampling_distance_squared information as much as the value of num_detail_levels_minus1 (ie, the number of LODs).

lifting_num_pred_nearest_neighbours indicates the maximum number of nearest neighbors used for prediction transformation. A value of numberOfNearestNeighboursInPrediction may be included in the range of 1 to xx.

lifting_max_num_direct_predictors indicates the number of predictors used for direct prediction transformation. max_num_direct_predictors has a value within the range of 0 to num_pred_nearest_neighbours.

The APS according to embodiments may include neighbor point set generation information (eg, neighbor point set property information described with reference to FIGS. 15 and 17 to 19 ). Neighbor point set generation information according to embodiments indicates information on generation of the neighboring point set described with reference to FIGS. 1 to 20 .

The different_nn_search_type_per_lod_flag information may indicate whether to use different neighbor point set search methods for each LOD (eg, the LOD to which the target point of FIGS. 15 to 19 belongs). As described above with reference to FIGS. 17 to 19 , the neighbor point set search method according to embodiments may be applied differently for each LOD.

When the different_nn_search_type_per_lod_flag information indicates a false value, the above-described neighboring point set generation information may include reference point selection type information that is equally applied to all LOD values. A description of reference point selection type information according to embodiments is the same as described with reference to FIG. 21 .

When the different_nn_search_type_per_lod_flag information does not indicate a false value, reference point set generation information identified by an idx value may be included. The index idx is greater than or equal to 0 and less than the value indicated by num_detail_levels_minus1. The num_detail_levels_minus1 value may specify the maximum number of all LOD values constituting the LOD according to embodiments. A description of reference point selection type information according to embodiments is the same as described with reference to FIG. 21 .

nearest_neighbor_search_type_per_tile_flag may indicate whether the above-described neighbor point set generation information is applied differently for each tile. When nearest_neighbor_search_type_per_tile_flag indicates a value of true, the TPS (eg, Tile Parameter Set 28000 of FIG. 20 ) according to embodiments may include neighbor point set generation information. When nearest_neighbor_search_type_per_tile_flag does not indicate a value of true, the TPS according to embodiments may not include neighbor point set generation information.

The APS according to the embodiments may further include signaling information (or signaling information about a prediction geometry encoding scheme) regarding a prediction tree (eg, a prediction tree or a prediction tree of FIGS. 15 to 19 ). Description of the signaling information regarding the prediction geometry encoding scheme according to the embodiments is the same as described above with reference to FIG. 20 .

24 shows a GSH (Geom_slice_header) structure of point cloud data according to embodiments.

A geometry slice header (GSH) according to embodiments may be referred to as a geometry slice header. GSH according to embodiments may refer to data having header information included in a Geom (Geometry Bitstream) included in one or more slices. That is, the GSH may be header information for geometry information included in the corresponding slice. GSH, which is header information of the geometry information, may include parameters such as geom_geom_parameter_set_id, geom_tile_id, geom_slice_id, geom_BoxOrigin, geom_box_log2_scale, beom_max_node_size_log2, geom_num_points, and the like.

gsh_geometry_parameter_set_id indicates a value of gps_geom_parameter_set_id of active GPS. (specifies the value of the gps_geom_parameter_set_id of the active GPS)

gsh_tile_id indicates an identifier (id) of a tile.

gsh_slice_id indicates an identifier (id) of a slice.

gps_box_present_flag indicates whether a source bounding box (or box) indicated by GSH according to embodiments exists. When the value of gps_box_present_flag is 1, the GPS 31000 according to embodiments may include some/all of gsh_box_log2_scale, gsh_box_origin_x, gsh_box_origin_y, and gsh_box_origin_z.

gsh_box_log2_scale indicates a scale value of a source bounding box (or box) indicated by GSH according to embodiments.

gsh_box_origin_x represents x information of a source bounding box indicated by GSH in the Cartesian coordinate system.

gsh_box_origin_y indicates y information of a source bounding box indicated by GSH in the coordinate system.

gsh_box_origin_z indicates z information of a source bounding box indicated by GSH in the coordinate system.

gsh_log2_max_nodesize indicates the value of the MaxNodeSize variable used in the following decoding operation.

MaxNodeSize = 2 ( gbh_log2_max_nodesize )

gbh_points_number indicates the number of coded points in the slice.

Information indicating a method of generating a molton code included in GSH according to embodiments may be information commonly applied to all slices signaled by GSH according to embodiments.

The GSH according to embodiments may further include signaling information (or signaling information about a prediction geometry encoding scheme) related to a prediction tree (eg, a prediction tree or a prediction tree of FIGS. 15 to 19 ). Description of the signaling information regarding the prediction geometry encoding scheme according to the embodiments is the same as described above with reference to FIG. 20 .

The signaling information included in the bitstream according to the embodiments may include a metadata processing unit or a transmission processing unit (for example, the metadata processing unit 12007 of FIG. 12 or a transmission processing unit (in the first embodiments) included in the point cloud data transmission apparatus. may be generated by one or more elements in the data input unit 15012012. The signaling information according to embodiments may be generated based on a result of performing geometry encoding and/or attribute encoding. By transmitting the bitstream in the form as described above, the point cloud data transmitting apparatus according to , may increase compression efficiency, increase image quality performance, and reduce the burden on the receiving apparatus.

25 is a point cloud data receiving apparatus (eg, the receiving apparatus 10004 of FIG. 1 , the point cloud video decoder 10006 of FIG. 1 , the decoder of FIG. 10 , the receiving apparatus of FIG. 13 and FIG. 14 is a block diagram showing the XR device 1430). The receiving apparatus 2500 according to the embodiments may perform the same or similar operation to the decoding operation described with reference to FIGS. 1 to 24 , and may perform a reverse process of the operation of the transmitting apparatus of FIG. 15 . The receiving apparatus 2500 according to the embodiments may include a receiving unit 2501, an arithmetic decoder 2502, a prediction value inverse calculation unit 2503, an inverse quantization processing unit 2504, and/or a coordinate inverse transformation unit 2505. . Although not shown in FIG. 25, the receiving apparatus according to the embodiments may further include one or more elements for performing the same or similar operation to the decoding operation described with reference to FIGS. 1 to 24 .

The receiver according to embodiments may receive a bitstream in which a geometry bitstream and/or an attribute bitstream are multiplexed.

The arithmetic decoder according to the embodiments may decode the received geometry bitstream based on arithmetic coding. The arithmetic decoder according to the embodiments may perform the same or similar operation to the operation of the arithmetic decoder 11000 of FIG. 11 .

The prediction value inverse calculator according to the embodiments may decode the geometry using a prediction geometry decoding scheme based on the prediction tree (the prediction tree or the prediction tree described with reference to FIGS. 15 to 24 ). The prediction value inverse calculator according to embodiments may correspond to the prediction tree structure generator 1504 of FIG. 15 . The process of decoding the geometry using the prediction geometry decoding scheme according to the embodiments may include a process of rearranging one or more points based on the geometry and a process of generating a prediction tree based on the rearranged points. A process of rearranging one or more points based on a geometry according to embodiments may be the same or similar to a process of rearranging the points described above with reference to FIG. 15 . In addition, the data aligning unit according to embodiments may align data based on signaling information regarding a prediction geometry encoding scheme included in a bitstream. For example, the data arranging unit may rearrange the points based on information about the rearrangement of the points included in the signaling information about the prediction geometry encoding scheme. The data alignment unit (not shown in this figure) according to embodiments may perform a process of rearranging one or more points based on a geometry. The process of generating the prediction tree based on the rearranged points according to the embodiments is the same as or similar to the process of generating the prediction tree described above with reference to FIGS. 15 to 18 . The prediction tree generator (not shown in this figure) according to embodiments may perform a process of generating a prediction tree based on the rearranged points. That is, the predictive value inverse calculator according to the embodiments may include a data aligner and/or a prediction tree generator. The prediction value inverse calculator according to the embodiments may generate a prediction tree and calculate a predicted value (the predicted value or the modified prediction value described with reference to FIGS. 15 to 24 ) of a point corresponding to each vertex of the vertices. The process of calculating the predicted value according to the embodiments is the same as or similar to the process of calculating the predicted value described above with reference to FIGS. 15 to 24 .

The prediction value inverse calculator according to the embodiments may calculate the prediction value based on signaling information (signaling information described with reference to FIGS. 20 to 24 ) about the prediction geometry encoding scheme included in the received bitstream. That is, the prediction value inverse calculator according to the embodiments includes information on whether the first method is used (information on whether to use the first method described with reference to FIGS. 20 to 24) and the second signaling information indicating that the first method is not used. In response to including information about the second method (information about the second method described with reference to FIGS. 20 to 24 ), the first method is modified based on the second method (eg, the rearrangement of points described with reference to FIG. 17 ) The predicted value can be calculated based on the second method). The prediction value inverse calculator according to the embodiments includes information on whether to use the first method indicating that the signaling information uses the first method (information on whether to use the first method described in FIGS. 20 to 24), and information on the second method In response to including the information (information about the second method described in FIGS. 20 to 24 ) and information about the threshold value (information about the threshold value described in FIGS. 20 to 24 ), the second method (for example, , the predicted value calculated based on the first method described with reference to FIG. 17 and the predicted value may be calculated based on the second method in which the first method is modified based on the threshold value. The prediction value inverse calculator according to the embodiments generates the Morton code of the prediction residual value by using the information on whether or not the Morton code is generated of the prediction residual value (information on whether the Morton code is generated as described in FIGS. 20 to 24) included in the signaling information. It can be seen that the prediction residual value is a Morton code generated based on bits of the x-coordinate value, the y-coordinate value, and the z-coordinate value of the prediction residual value. The prediction value inverse calculator according to the embodiments corresponds to information on whether or not to generate a Morton code of the prediction residual value included in the signaling information indicates that a Morton code of the prediction residual value is generated, and predicts based on the Morton code of the prediction residual value. An x-coordinate value, a y-coordinate value, and a z-coordinate value of the residual value can be generated. The process of generating the x-coordinate value, y-coordinate value, and z-coordinate value of the predicted residual value based on the Morton code of the predicted residual value is the process of generating the x-coordinate value, the y-coordinate value, and the z-coordinate value of the predicted residual value. It may be the reverse process of the process of generating the molton code based on the The prediction value inverse calculator according to the embodiments may generate the prediction tree based on information on the prediction tree structure included in the signaling information (information on the prediction tree structure described with reference to FIGS. 20 to 24 ). The prediction value inverse calculator according to the embodiments corresponds to the information about the prediction tree structure included in the signaling information indicating the information about the first structure (information about the first structure described in FIGS. 20 to 24), A prediction tree having a structure of 1 can be created. The prediction value inverse calculator according to the embodiments corresponds to the information about the prediction tree structure included in the signaling information indicating the information about the second structure (information about the second structure described in FIGS. 20 to 24), A prediction tree having two structures can be created. As described above with reference to FIGS. 18 and 20 , the total number of depths of the prediction tree having the second structure may be smaller than the total number of depths of the prediction tree having the first structure.

The prediction value inverse calculator according to the embodiments may reconstruct the geometry based on the calculated prediction value and the prediction residual value included in the bitstream (eg, the prediction residual value described with reference to FIGS. 15 to 24 ). That is, the prediction value inverse calculator according to the embodiments may obtain a coordinate value indicating a position of a point by adding a prediction residual value to the calculated prediction value.

The inverse quantization processing unit according to the embodiments may inverse quantize the decoded attribute bitstream or information on the attribute secured as a result of decoding, and output inverse quantized attributes (or attribute values). Inverse quantization may be selectively applied based on attribute encoding of the point cloud encoder. The inverse quantization processing unit according to embodiments may perform the same or similar operation as that of the inverse quantization unit 11006 of FIG. 11 .

The coordinate inverse transform unit according to embodiments may obtain positions (or positions) of points by transforming the coordinate system based on the reconstructed geometry. The coordinate inverse transform unit according to the embodiments may perform the same or similar operation to the operation of the coordinate system inverse transform unit 11004 of FIG. 11 .

The point cloud data receiving apparatus according to the embodiments may output (or render) final point cloud data based on the restored (or reconstructed) geometry information and/or the restored (or reconstructed) attribute information. In addition, the point cloud data receiving apparatus according to the embodiments reconstructs the geometry of points based on signaling information (signaling information described with reference to FIGS. 20 to 24 ) about the prediction geometry encoding scheme included in the received bitstream and decoding process You can adjust the latency in .

26 is a point cloud data transmission method of a point cloud data transmission apparatus (eg, the point cloud data transmission apparatus described in FIGS. 1, 2, 4, 11, 12 and 15 ) according to embodiments; indicates. The transmitting apparatus according to the embodiments performs the same or similar operation to the encoding operation described with reference to FIGS. 1 to 25 .

The point cloud data transmission apparatus according to the embodiments may encode the point cloud data ( 2600 ). An apparatus for transmitting point cloud data according to embodiments may include a geometry encoder for encoding a geometry indicating a position of one or more points of point cloud data and an attribute encoder for encoding an attribute of one or more points. A geometry encoder according to embodiments may include a prediction tree structure generator that encodes a geometry with a prediction geometry encoding scheme based on a prediction tree. The prediction tree structure generation unit according to the embodiments performs the same or similar operation to the operation of the prediction tree structure generation unit 1504 of FIG. 15 . The prediction tree structure generator according to embodiments may further include a data aligner that rearranges one or more points based on a geometry and a prediction tree generator that generates a prediction tree based on the rearranged points. The data arranging unit according to embodiments performs the same or similar operation as that of the data aligning unit 1504a of FIG. 15 . The prediction tree generation unit according to the embodiments performs the same or similar operation to the operation of the prediction value calculation unit 1504b, the prediction tree structure transformation unit 1504c, and/or the prediction value calculation and transmission method determination unit 1504d of FIG. 15 . . A prediction tree according to embodiments includes one or more vertices, and the one or more vertices include a root vertex corresponding to a lowest depth among the one or more vertices and one Alternatively, a leaf vertex corresponding to the highest depth among the vertices may be included, and the depth corresponding to each vertex may indicate the number of hops from the root vertex to each vertex. The descriptions of the prediction tree, vertex, depth, root vertex, and leaf vertex are the same as described above with reference to FIGS. 15 to 25 . One vertex of the one or more vertices according to embodiments may correspond to one of the rearranged one or more points.

One or more vertices according to embodiments may include a first vertex, a second vertex, a third vertex, and a fourth vertex. According to embodiments, p0 is a coordinate value indicating a position of a second point corresponding to a second vertex having a depth smaller than the depth by 1 in the first vertex, and p1 is a first vertex having a depth smaller than the depth by 2 A coordinate value indicating the position of the third point corresponding to the 3 vertex and p2 may indicate a coordinate value indicating the position of the fourth point corresponding to the fourth vertex having a depth in the first vertex that is smaller than the depth by 3 vertices. The descriptions of p0, p1, p2, the first vertex, the second vertex, the third vertex, and the fourth vertex are the same as those described above with reference to FIGS. 15 to 25 . A prediction value of the first point corresponding to the first vertex is calculated based on a prediction mode, and the prediction mode is a first point for calculating a prediction value based on at least one of 0, p0, p1, and p2. 1 can indicate the mode of the method. A prediction residual value of the first point according to embodiments may indicate a difference between the prediction value of the first point and a coordinate value indicating the position of the first point. Descriptions of the above-described prediction value, prediction mode, and prediction residual value are the same as described above with reference to FIGS. 15 to 25 .

An apparatus for transmitting point cloud data according to embodiments may transmit a bitstream including encoded point cloud data. Bitstreams according to embodiments are the same as or similar to those described with reference to FIGS. 20 to 24 . A bitstream according to embodiments includes signaling information on a prediction geometry encoding scheme, the signaling information includes information indicating whether a prediction geometry encoding scheme is used, and information indicating whether a prediction geometry encoding scheme is used Corresponding to indicating that the prediction geometry encoding scheme is used, the signaling information includes information on the reordering of points, information on whether to use the first method, information on whether to generate a Morton code of the prediction residual value, and information on the structure of the prediction tree. It may include more information about Signaling information on the prediction geometry encoding scheme, information indicating whether the prediction geometry encoding scheme is used, information on rearrangement of points, information on whether to use the first method, information on whether to generate a Morton code of the prediction residual value, and Description of the information on the structure of the dictionary tree is the same as described above with reference to FIGS. 20 to 24 .

Corresponding to that the first method according to the embodiments is modified to the second method based on the order in which the points are rearranged, the signaling information includes information on whether to use the first method and the second method indicating that the first method is not used. Further comprising information about the method, and in response to the first method being modified to the second method based on a threshold value and a predicted value calculated based on the first method, the signaling information uses the first method It may further include information about whether to use the first method, information about the second method, and information about a threshold value. Corresponding to that the modified second method according to the embodiments is modified to the third method based on a coefficient value, the signaling information may further include information about the coefficient value. The descriptions of the first method, the second method, the third method, the coefficient value, and the threshold value are the same as those described above with reference to FIGS. 15 to 25 .

Corresponding to generating a Morton code of a prediction residual value based on bits of an x-coordinate value, a y-coordinate value, and a z-coordinate value of the prediction residual value according to the embodiments, in response to generating a Morton code of the prediction residual value The related information may indicate that a molton code of the predicted residual value is generated. Information on whether the above-described molton code is generated is the same as described above with reference to FIGS. 20 to 24 .

Corresponding to the prediction tree according to the embodiments having the first structure, the information about the prediction tree structure indicates information about the first structure, and corresponding to the prediction tree having the second structure, the prediction The information about the tree structure may indicate information about the second structure, and the total number of depths of the prediction tree having the second structure may be smaller than the total number of depths of the prediction tree having the first structure. The information about the first structure, the second structure, the prediction tree structure, the information about the first structure, and the information about the second structure are the same as described above with reference to FIGS. 15 to 25 .

The bitstream according to the embodiments may include signaling information regarding the prediction geometry encoding scheme (eg, signaling information regarding the prediction geometry encoding scheme described with reference to FIGS. 20 to 24 ). In addition, the signaling information regarding the prediction geometry encoding scheme according to the embodiments may be transmitted through SPS, APS, TPS, and/or ASH, as described above with reference to FIGS. 20 to 24, and is limited to the above-described example. doesn't happen

27 illustrates a point cloud data transmission method of an apparatus for receiving point cloud data (eg, the apparatus for receiving point cloud data described with reference to FIGS. 1, 2, 11, 13, and 25) according to embodiments. The transmitting apparatus according to the embodiments performs the same or similar operation to the decoding operation described with reference to FIGS. 1 to 25 .

The device for receiving point cloud data according to embodiments may receive a bitstream including point cloud data ( 2700 ). Bitstreams according to embodiments are the same as or similar to those described with reference to FIGS. 20 to 24 .

A bitstream according to embodiments includes signaling information on a prediction geometry decoding scheme, the signaling information includes information indicating whether a prediction geometry decoding scheme is used, and information indicating whether a prediction geometry decoding scheme is used Corresponding to indicating that the prediction geometry decoding scheme is used, the signaling information includes information on the reordering of points, information on whether to use the first method, information on whether to generate a Morton code of the prediction residual value, and information on the structure of the prediction tree. It may include more information about Signaling information on the prediction geometry encoding scheme, information indicating whether the prediction geometry encoding scheme is used, information on rearrangement of points, information on whether to use the first method, information on whether to generate a Morton code of the prediction residual value, and Description of the information on the structure of the dictionary tree is the same as described above with reference to FIGS. 20 to 24 .

Corresponding to the signaling information according to the embodiments further comprising information on whether the first method is used and information on the second method indicating that the first method is not used, the first method is performed in the order in which the points are rearranged. The second method is modified based on the second method, and the signaling information further includes information on whether to use the first method indicating that the first method is used, information about the second method, and information about a threshold value. Correspondingly, the first method is modified to the second method based on the threshold value and the predicted value calculated based on the first method, and in response to the signaling information further comprising information about a coefficient value, The modified second method may be modified as the third method based on the coefficient value. The descriptions of the first method, the second method, the third method, the coefficient value, and the threshold value are the same as those described above with reference to FIGS. 15 to 25 .

Corresponding to the information regarding whether the Morton code of the prediction residual value is generated according to the embodiments indicates that the Morton code of the prediction residual value is generated, the prediction residual value is the x-coordinate value, the y-coordinate value, and the z-coordinate value of the prediction residual value. It can represent a molton code generated based on the bits of . Information on whether the above-described molton code is generated is the same as described above with reference to FIGS. 20 to 24 .

In response to the information on the prediction tree structure according to the embodiments indicating the information on the first structure, the structure of the prediction tree has a first structure, and the information on the prediction tree structure relates to the second structure. Corresponding to representing information, the structure of the prediction tree has a second structure, and the total number of depths of the prediction tree having the second structure may be smaller than the total number of depths of the prediction tree having the first structure. . The information about the first structure, the second structure, the prediction tree structure, the information about the first structure, and the information about the second structure are the same as described above with reference to FIGS. 15 to 25 .

The point cloud data receiving apparatus according to the embodiments may decode the point cloud data. An apparatus for receiving point cloud data according to embodiments includes a geometry decoder for decoding a geometry indicating a position of one or more points of point cloud data, and an attribute decoder for decoding an attribute of one or more points. The geometry decoder according to the embodiments may perform the geometry decoding operation described with reference to FIGS. 1 to 25 . A geometry decoder according to embodiments may include a prediction value inverse calculator that decodes a geometry with a prediction geometry decoding scheme based on a prediction tree. The prediction value inverse calculator according to embodiments may include a data aligner that rearranges one or more points based on a geometry and a prediction tree generator that generates a prediction tree based on the rearranged points. The data arranging unit according to the embodiments performs the same or similar operation to the operation of the data aligning unit of FIG. 25 . The prediction tree generator according to embodiments performs the same or similar operation to the prediction tree generator of FIG. 25 . A prediction tree according to embodiments includes one or more vertices, and the one or more vertices include a root vertex corresponding to a lowest depth among the one or more vertices and one Alternatively, a leaf vertex corresponding to the highest depth among the vertices may be included, and the depth corresponding to each vertex may indicate the number of hops from the root vertex to each vertex. The descriptions of the prediction tree, vertex, depth, root vertex, and leaf vertex are the same as described above with reference to FIGS. 15 to 25 . One vertex of the one or more vertices according to embodiments may correspond to one of the rearranged one or more points.

Although each drawing has been described separately for convenience of description, it is also possible to design to implement a new embodiment by merging the embodiments described in each drawing. And, according to the needs of those skilled in the art, designing a computer-readable recording medium in which a program for executing the previously described embodiments is recorded also falls within the scope of the rights of the embodiments. The apparatus and method according to the embodiments are not limited to the configuration and method of the described embodiments as described above, but all or some of the embodiments are selectively combined so that various modifications can be made. may be configured. Although preferred embodiments of the embodiments have been shown and described, the embodiments are not limited to the specific embodiments described above, and common knowledge in the technical field to which the invention pertains without departing from the gist of the embodiments claimed in the claims Various modifications are possible by those having

Various components of the apparatus of the embodiments may be implemented by hardware, software, firmware, or a combination thereof. Various components of the embodiments may be implemented with one chip, for example, one hardware circuit. According to embodiments, the components according to the embodiments may be implemented with separate chips. According to the embodiments, at least one or more of the components of the device according to the embodiments may be composed of one or more processors capable of executing one or more programs, and the one or more programs may be implemented Any one or more of the operations/methods according to the examples may be performed or may include instructions for performing the operations/methods. Executable instructions for performing the method/acts of the apparatus according to the embodiments may be stored in non-transitory CRM or other computer program products configured for execution by one or more processors, or one or more may be stored in temporary CRM or other computer program products configured for execution by processors. Also, the memory according to the embodiments may be used as a concept including not only volatile memory (eg, RAM, etc.) but also non-volatile memory, flash memory, PROM, and the like. Also, it may be implemented in the form of a carrier wave, such as transmission through the Internet. In addition, the processor-readable recording medium is distributed in a computer system connected through a network, so that the processor-readable code can be stored and executed in a distributed manner.

In this document, “/” and “,” are interpreted as “and/or”. For example, “A/B” is interpreted as “A and/or B”, and “A, B” is interpreted as “A and/or B”. Additionally, “A/B/C” means “at least one of A, B and/or C”. Also, “A, B, C” means “at least one of A, B and/or C”. Additionally, “or” in this document is to be construed as “and/or”. For example, “A or B” may mean 1) only “A”, 2) only “B”, or 3) “A and B”. In other words, “or” in this document may mean “additionally or alternatively”.

Terms such as first, second, etc. may be used to describe various elements of the embodiments. However, interpretation of various components according to the embodiments should not be limited by the above terms. These terms are only used to distinguish one component from another. it is only For example, the first user input signal may be referred to as a second user input signal. Similarly, the second user input signal may be referred to as a first user input signal. Use of these terms should be interpreted as not departing from the scope of the various embodiments. Although both the first user input signal and the second user input signal are user input signals, they do not mean the same user input signals unless the context clearly indicates otherwise.

The terminology used to describe the embodiments is used for the purpose of describing specific embodiments, and is not intended to limit the embodiments. As used in the description of embodiments and in the claims, the singular is intended to include the plural unless the context clearly dictates otherwise. and/or expressions are used in their sense to include all possible combinations between terms. The expression includes describes the presence of features, numbers, steps, elements, and/or components, and does not imply that additional features, numbers, steps, elements, and/or components are not included. . Conditional expressions such as when, when, etc., used to describe the embodiments, are not construed as being limited to only optional cases. When a specific condition is satisfied, a related action is performed in response to the specific condition, or a related definition is intended to be interpreted.

As described above, the related contents have been described in the best mode for carrying out the embodiments.

As described above, the embodiments may be wholly or partially applied to a point cloud data transmission/reception device and system.

Those skilled in the art can variously change or modify the embodiments within the scope of the embodiments.

Embodiments may include variations/modifications without departing from the scope of the claims and the like.

Claims

encoding point cloud data, comprising:

encoding geometry representing the position of one or more points of the point cloud data; and

encoding an attribute of the one or more points; including; and

transmitting a bitstream including the encoded point cloud data; containing,

How to transmit point cloud data.
The method of claim 1,

Encoding the geometry comprises:

encoding the geometry with a prediction geometry encoding scheme based on a prediction tree; Including, encoding the geometry with the prediction geometry encoding scheme comprises:

reordering one or more points based on the geometry; and

generating the prediction tree based on the rearranged points; including,

The prediction tree includes one or more vertices, and the one or more vertices include a root vertex corresponding to a lowest depth among the one or more vertices and the one or more vertices. and a leaf vertex corresponding to the highest depth among the vertices above, and the depth corresponding to each vertex represents the number of hops from the root vertex to each vertex,

a vertex of the one or more vertices corresponds to a point of the reordered one or more points;

How to transmit point cloud data.
3. The method of claim 2,

wherein the one or more vertices include a first vertex, a second vertex, a third vertex, and a fourth vertex;

p0 is a coordinate value indicating a position of a second point corresponding to a second vertex having a depth smaller than a depth by 1 in the first vertex, p1 is a third vertex having a depth smaller than the depth by 2 to the first vertex Coordinate values indicating the position of the corresponding third point and p2 are coordinate values indicating the position of the fourth point corresponding to the fourth vertex having a depth smaller than the depth by 3 to the first vertex,

A prediction value of the first point corresponding to the first vertex is calculated based on a prediction mode, and the prediction mode is based on at least one of 0, p0, p1 and p2. indicates the mode of the first method of calculating,

The prediction residual value of the first point represents a difference between the predicted value of the first point and a coordinate value indicating the position of the first point,

How to transmit point cloud data.
4. The method of claim 3,

The bitstream includes signaling information about the prediction geometry encoding scheme,

The signaling information includes information indicating whether to use the prediction geometry encoding scheme,

In response to the information indicating whether the prediction geometry encoding scheme is used or not indicates that the prediction geometry encoding scheme is used, the signaling information includes information on the reordering of the points, information on whether to use the first method, the prediction Further comprising information on whether or not the Molton code of the residual value is generated and information on the structure of the prediction tree,

How to transmit point cloud data.
5. The method of claim 4,

Corresponding to that the first method is modified to the second method based on the order in which the points are rearranged, the signaling information includes information on whether to use the first method indicating that the first method is not used and the second method 2 further comprising information about the method;

In response to the first method being modified to the second method based on a threshold value and a predicted value calculated based on the first method, the signaling information indicates that the first method is used. Information on whether to use a method, information on the second method, and information on the threshold value,

In response to the modified second method being modified to the third method based on a coefficient value, the signaling information further includes information about the coefficient value,

How to transmit point cloud data.
6. The method of claim 5,

In response to generating the Molton code of the prediction residual value based on bits of the x-coordinate value, the y-coordinate value, and the z-coordinate value of the prediction residual value,

The information on whether to generate a molton code of the prediction residual value indicates that a molton code of the prediction residual value is generated,

How to transmit point cloud data.
7. The method of claim 6,

Corresponding to the fact that the prediction tree has a first structure, the information about the prediction tree structure indicates information about the first structure,

Corresponding to that the prediction tree has a second structure, the information about the prediction tree structure indicates information about the second structure,

The number of total depths of the prediction tree having the second structure is smaller than the total number of depths of the prediction tree having the first structure,

How to transmit point cloud data.
An encoder for encoding point cloud data, comprising:

a geometry encoder for encoding a geometry representing a position of one or more points of the point cloud data; and

an attribute encoder for encoding an attribute of the one or more points; including; and

a transmitter for transmitting a bitstream including the encoded point cloud data; including,

The geometry encoder comprises a prediction tree structure generator for encoding the geometry with a prediction geometry encoding scheme based on a prediction tree, wherein the prediction tree structure generator comprises:

a data alignment unit for rearranging one or more points based on the geometry; and

a prediction tree generator for generating the prediction tree based on the rearranged points; including,

The prediction tree includes one or more vertices, and the one or more vertices include a root vertex corresponding to a lowest depth among the one or more vertices and the one or more vertices. including a leaf vertex corresponding to the highest depth among the vertices above, and the depth corresponding to each vertex represents the number of hops from the root vertex to each vertex,

a vertex of the one or more vertices corresponds to a point of the reordered one or more points;

Point cloud data transmission device.
9. The method of claim 8,

wherein the one or more vertices include a first vertex, a second vertex, a third vertex, and a fourth vertex;

p0 is a coordinate value indicating a position of a second point corresponding to a second vertex having a depth smaller than a depth by 1 in the first vertex, p1 is a third vertex having a depth smaller than the depth by 2 to the first vertex Coordinate values indicating the position of the corresponding third point and p2 are coordinate values indicating the position of the fourth point corresponding to the fourth vertex having a depth smaller than the depth by 3 to the first vertex,

A prediction value of the first point corresponding to the first vertex is calculated based on a prediction mode, and the prediction mode is based on at least one of 0, p0, p1 and p2. indicates the mode of the first method of calculating,

The prediction residual value of the first point represents a difference between the predicted value of the first point and a coordinate value indicating the position of the first point,

The bitstream includes signaling information about the prediction geometry encoding scheme,

The signaling information includes information indicating whether to use the prediction geometry encoding scheme,

In response to the information indicating whether the prediction geometry encoding scheme is used or not indicates that the prediction geometry encoding scheme is used, the signaling information includes information on the reordering of the points, information on whether to use the first method, the prediction Further comprising information on whether or not the Molton code of the residual value is generated and information on the structure of the prediction tree,

Point cloud data transmission device.
10. The method of claim 9,

Corresponding to that the first method is modified to the second method based on the order in which the points are rearranged, the signaling information includes information on whether to use the first method indicating that the first method is not used and the second method 2 further comprising information about the method;

In response to the first method being modified to the second method based on a threshold value and a predicted value calculated based on the first method, the signaling information indicates that the first method is used. Information on whether to use a method, information on the second method, and information on the threshold value,

In response to the modified second method being modified to the third method based on a coefficient value, the signaling information further includes information about the coefficient value,

In response to generating the Molton code of the prediction residual value based on bits of the x-coordinate value, the y-coordinate value, and the z-coordinate value of the prediction residual value,

The information on whether to generate a molton code of the prediction residual value indicates that a molton code of the prediction residual value is generated,

Corresponding to the fact that the prediction tree has a first structure, the information about the prediction tree structure indicates information about the first structure,

Corresponding to that the prediction tree has a second structure, the information about the prediction tree structure indicates information about the second structure,

The number of total depths of the prediction tree having the second structure is smaller than the total number of depths of the prediction tree having the first structure,

Point cloud data transmission device.
receiving a bitstream including point cloud data; and

decoding the point cloud data; Including, decoding the point cloud data comprises:

decoding a geometry indicative of a position of one or more points of the point cloud data; and

decoding an attribute of the one or more points; containing,

How to receive point cloud data
12. The method of claim 11,

Decoding the geometry comprises:

decoding the geometry with a prediction geometry decoding scheme based on a prediction tree; Including, decoding the geometry with the prediction geometry decoding scheme,

reordering one or more points based on the geometry; and

generating the prediction tree based on the rearranged points; including,

The prediction tree includes one or more vertices, and the one or more vertices include a root vertex corresponding to a lowest depth among the one or more vertices and the one or more vertices. and a leaf vertex corresponding to the highest depth among the vertices above, and the depth corresponding to each vertex represents the number of hops from the root vertex to each vertex,

a vertex of the one or more vertices corresponds to a point of the reordered one or more points;

How to receive point cloud data
13. The method of claim 12,

wherein the one or more vertices include a first vertex, a second vertex, a third vertex, and a fourth vertex;

p0 is a coordinate value indicating a position of a second point corresponding to a second vertex having a depth smaller than a depth by 1 in the first vertex, p1 is a third vertex having a depth smaller than the depth by 2 to the first vertex Coordinate values indicating the position of the corresponding third point and p2 are coordinate values indicating the position of the fourth point corresponding to the fourth vertex having a depth smaller than the depth by 3 to the first vertex,

A prediction value of the first point corresponding to the first vertex is calculated based on a prediction mode, and the prediction mode is based on at least one of 0, p0, p1 and p2. indicates the mode of the first method of calculating,

The prediction residual value of the first point represents a difference between the predicted value of the first point and a coordinate value indicating the position of the first point,

How to receive point cloud data
14. The method of claim 13,

The bitstream includes signaling information about the prediction geometry decoding scheme,

The signaling information includes information indicating whether to use the prediction geometry decoding scheme,

In response to the information indicating whether the prediction geometry decoding scheme is used or not indicates that the prediction geometry decoding scheme is used, the signaling information includes information on the reordering of the points, information on whether to use the first method, the prediction Further comprising information on whether or not the Molton code of the residual value is generated and information on the structure of the prediction tree,

How to receive point cloud data.
15. The method of claim 14,

Corresponding to the signaling information further comprising information on whether the first method is used and information on the second method indicating that the first method is not used, the first method is performed in the order in which the points are rearranged. modified by the second method based on

Corresponding to that the signaling information further includes information about whether to use the first method indicating that the first method is used, information about a second method, and information about a threshold value, the first method Based on the predicted value calculated based on and the threshold value, the first method is modified to the second method,

In response to the signaling information further comprising information about a coefficient value, the modified second method is modified to a third method based on the coefficient value,

How to receive point cloud data.
16. The method of claim 15,

In response to the information on whether or not to generate a molton code of the prediction residual value indicates that a molton code of the prediction residual value is generated,

The prediction residual value represents a Molton code generated based on bits of an x-coordinate value, a y-coordinate value, and a z-coordinate value of the prediction residual value,

How to receive point cloud data.
17. The method of claim 16,

In response to the information about the prediction tree structure indicating information about the first structure, the structure of the prediction tree has the first structure,

In response to the information about the prediction tree structure indicating information about the second structure, the structure of the prediction tree has the second structure,

The number of total depths of the prediction tree having the second structure is smaller than the total number of depths of the prediction tree having the first structure.

How to receive point cloud data.
a receiver for receiving a bitstream including point cloud data; and

a decoder for decoding the point cloud data; Including, the decoder,

a geometry decoder for decoding a geometry indicating a position of one or more points of the point cloud data; and

an attribute decoder for decoding an attribute of the one or more points; including,

The geometry decoder may include: a prediction value inverse calculator configured to decode the geometry using a prediction geometry decoding scheme based on a prediction tree; Including, the predicted value inverse calculator,

a data alignment unit for rearranging one or more points based on the geometry; and

a prediction tree generator for generating the prediction tree based on the rearranged points; including,

The prediction tree includes one or more vertices, and the one or more vertices include a root vertex corresponding to a lowest depth among the one or more vertices and the one or more vertices. and a leaf vertex corresponding to the highest depth among the vertices above, and the depth corresponding to each vertex represents the number of hops from the root vertex to each vertex,

a vertex of the one or more vertices corresponds to a point of the reordered one or more points;

Point cloud data receiving device.
19. The method of claim 18,

wherein the one or more vertices include a first vertex, a second vertex, a third vertex, and a fourth vertex;

p0 is a coordinate value indicating a position of a second point corresponding to a second vertex having a depth smaller than a depth by 1 in the first vertex, p1 is a third vertex having a depth smaller than the depth by 2 to the first vertex Coordinate values indicating the position of the corresponding third point and p2 are coordinate values indicating the position of the fourth point corresponding to the fourth vertex having a depth smaller than the depth by 3 to the first vertex,

A prediction value of the first point corresponding to the first vertex is calculated based on a prediction mode, and the prediction mode is based on at least one of 0, p0, p1 and p2. indicates the mode of the first method of calculating,

The prediction residual value of the first point represents a difference between the predicted value of the first point and a coordinate value indicating the position of the first point,

The bitstream includes signaling information about the prediction geometry decoding scheme,

The signaling information includes information indicating whether to use the prediction geometry decoding scheme,

In response to the information indicating whether the prediction geometry decoding scheme is used or not indicates that the prediction geometry decoding scheme is used, the signaling information includes information on the reordering of the points, information on whether to use the first method, the prediction Further comprising information on whether or not the Molton code of the residual value is generated and information on the structure of the prediction tree,

Point cloud data receiving device.
20. The method of claim 19,

Corresponding to the signaling information further comprising information on whether the first method is used and information on the second method indicating that the first method is not used, the first method is performed in the order in which the points are rearranged. modified by the second method based on

Corresponding to that the signaling information further includes information about whether to use the first method indicating that the first method is used, information about a second method, and information about a threshold value, the first method Based on the predicted value calculated based on and the threshold value, the first method is modified to the second method,

In response to the signaling information further comprising information about a coefficient value, the modified second method is modified to a third method based on the coefficient value,

In response to the information on whether or not to generate a molton code of the prediction residual value indicates that a molton code of the prediction residual value is generated,

The prediction residual value represents a Molton code generated based on bits of an x-coordinate value, a y-coordinate value, and a z-coordinate value of the prediction residual value,

In response to the information about the prediction tree structure indicating information about the first structure, the structure of the prediction tree has the first structure,

In response to the information about the prediction tree structure indicating information about the second structure, the structure of the prediction tree has the second structure,

The number of total depths of the prediction tree having the second structure is smaller than the total number of depths of the prediction tree having the first structure.

Point cloud data receiving device.