WO2021256887A1

WO2021256887A1 - Point cloud data transmission device, point cloud data transmission method, point cloud data reception device, and point cloud data reception method

Info

Publication number: WO2021256887A1
Application number: PCT/KR2021/007654
Authority: WO
Inventors: 박유선; 오세진
Original assignee: 엘지전자 주식회사
Priority date: 2020-06-18
Filing date: 2021-06-18
Publication date: 2021-12-23

Abstract

A point cloud data transmission method according to embodiments may comprise the steps of: encoding point cloud data; and transmitting a bitstream including the point cloud data. In addition, a point cloud data transmission device according to embodiments may comprise: an encoder for encoding point cloud data; and a transmitter for transmitting a bitstream including the point cloud data, wherein the encoder may include: a data alignment unit for aligning geometry data of the point cloud data; a prediction tree forming unit for forming a prediction tree for the geometry data; a prediction value calculation unit for calculating a prediction value for the geometry data on the basis of the prediction tree; and an encoding unit for encoding the geometry data.

Description

Point cloud data transmission device, point cloud data transmission method, point cloud data reception device and point cloud data reception method

Embodiments relate to a method and apparatus for processing point cloud content.

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.

In order to achieve the technical problem, a method for transmitting point cloud data according to embodiments may include encoding the point cloud data and transmitting a bitstream including the point cloud data. A method of receiving point cloud data according to embodiments may include 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 drawings in which like reference numerals include corresponding parts throughout.

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

15 illustrates examples of dense frame data and sparse frame data of point cloud data according to embodiments.

16 illustrates an example of generating and encoding a prediction tree structure in a method of transmitting point cloud data according to embodiments.

17 illustrates an example of generating a prediction tree structure from rearranged points in a method of transmitting point cloud data according to embodiments.

18 illustrates an example of points arranged in an azimuth order in generating a prediction tree structure according to embodiments.

19 is a diagram visually illustrating prediction points predicted by a Tetrahedron (tetrahedral) prediction method according to embodiments.

20 shows an example of a prediction tree structure component of an apparatus for transmitting point cloud data according to embodiments.

21 shows an example of an apparatus for receiving point cloud data according to embodiments.

22 shows an example of encoded point cloud data according to embodiments.

23 shows an example of a syntax of a Sequence Parameter Set according to embodiments.

24 shows an example of a syntax of a Tile Parameter Set according to embodiments.

25 shows an example of a syntax of a Geometry Parameter Set according to embodiments.

26 shows an example of a syntax of an Attribute Parameter Set according to embodiments.

27 shows an example of syntax of a Slice header of a geometry bitstream according to embodiments.

28 shows an example of a transmitter and a receiver according to embodiments.

29 shows an example of a transmission method according to embodiments.

30 shows an example of a receiving method 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 only showing 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 the embodiments may be practiced without these details.

Most terms used in the embodiments are selected from general ones widely used in the relevant field, but some terms are arbitrarily selected by the applicant and their meanings will be described in detail in the following description as necessary. Therefore, the embodiments should be understood based on the intended meaning of the term rather than the simple name or meaning of the term.

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 transmitting 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 communicate 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 an 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 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, 6G, etc.). Also, the transmission device 10000 may transmit encapsulated data according to an on demand method.

The receiving device 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 or a robot , vehicles, AR/VR/XR devices, portable 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 the 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 reception 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 on, 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 transmitting the 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 transmission 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, a transmitter, etc., 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. The 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 (eg, 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 an 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. The 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 reception device 10004) according to the embodiments may secure the 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 and 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 It is possible to capture point cloud video 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 user with a 360-degree image of a core object (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. It can be used to create 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 the 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 (eg, 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 secured 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), Transform Colors (40006), Attribute Transformer (Transfer Attributes, 40007), RAHT Transform It includes 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 approxy 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, geometry encoding is not limited to the above example.

As shown in the drawing, 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 3D space according to embodiments may be referred to as geometry information.

The quantizer 40001 according to embodiments quantizes the geometry. For example, the quantization unit 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 performs a quantization operation to find the nearest integer value by multiplying the difference between the minimum position value and the position value of each point by a preset quatization scale value, and then rounding down or rounding it up. 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 a unit (unit=1.0) of a 3D space based on the axes (eg, X-axis, Y-axis, Z-axis) representing the 3D 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 center point of the 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 appropriation 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 generation 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 converter 40006 may convert the format of color information (eg, convert 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 to which geometry encoding has not been performed and/or a reconstructed geometry. As described above, since the attributes are dependent on the geometry, the attribute transform 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 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 of each point, reflectance, etc.) of neighboring points within a specific position/radius from the position (or position value) of the central 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 conversion 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)) indicating a 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 transformation operation, if the nearest neighbor search (NNS) is required in another transformation 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 higher 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 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. 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 encoder of FIG. 4 . One or more memories in accordance with 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 devices (such as solid-state memory devices).

5 illustrates an example of a voxel according to embodiments.

5 shows voxels located in a three-dimensional space expressed 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 an octree structure that recursively subdivides a bounding box defined by two poles (0,0,0) and (2 ^d , 2 ^d , 2 ^{d ).} An example of a voxel generated through 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.

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 In order to do this, octree geometry coding (or octree coding) based on the 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 embodiments is expressed by axes (eg, X-axis, Y-axis, and Z-axis) of the coordinate system. An octree structure is created by recursive subdividing a bounding box defined by two poles (0,0,0) and (2 ^d , 2 ^d , 2 ^{d ).} . 2d may be set as 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 expressed as 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 occupancy code of the octree. The occupancy code of the octree is generated to indicate whether each of the 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 an 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, it indicates that 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 eight 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, there is no need 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 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. In addition, 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 less than a threshold within a specific node. points must exist. In addition, the number of whole points to be subjected to direct coding must not exceed a preset limit value. If the above condition is satisfied, the point cloud encoder (or the arithmetic encoder 40004 ) according to the 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, which 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 tri-soup mode. That is, the point cloud encoder according to the embodiments may operate in the tri-soup mode only when the specified 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 on 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 of each vertex, ② perform the square on the values obtained by subtracting the centroid 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 value that comes out when projecting 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 the aligned vertices, and the second triangle may be composed of 3rd, 4th, and 1st vertices among the aligned vertices. .

Triangles formed from vertices ordered 1,… ,n

n triangles

3 (1,2,3)

4 (1,2,3), (3,4,1)

5 (1,2,3), (3,4,5), (5,1,3)

6 (1,2,3), (3,4,5), (5,6,1), (1,3,5)

7 (1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)

8 (1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1)

9 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), ( 7,9,3)

10 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,1), (1,3,5), ( 5,7,9), (9,1,5)

11 (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)

12 (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. In addition, the point cloud encoder may perform attribute encoding based on the voxelized position (or position value).

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 depending on the number of referenced neighboring nodes. The larger the bit, the more complicated it becomes, but the compression efficiency can be increased by biasing it to one side. For example, if you have a 3-bit context, you need 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 shows 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 neighbor pattern value. 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 (neighboring nodes) that share 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 neighbor node pattern value 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 neighboring 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 (eg, when 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). When tri-soap geometry encoding is applied, the geometry reconstruction process is triangular reconstruction, upsampling, and voxelization. Since the attribute is dependent on the geometry, 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 figure shows the original point cloud content. The second figure from the left of the figure shows the distribution of points with the lowest LOD, and the rightmost figure of the figure shows the distribution of points with the highest LOD. That is, the points of the lowest LOD are sparsely distributed, and the points of the highest LOD are tightly 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 with reference to 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.

The point cloud encoder according to embodiments may generate predictors for points and perform predictive transform coding to set prediction attributes (or prediction attribute values) 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, indexing information on neighboring points existing within a 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 the 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 the 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 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 QW value of the predictor index of the neighboring node registered in the predictor by the weight of the predictor of the current point is added.

2) Lift prediction process: In order to calculate the predicted attribute value, a value obtained by multiplying an attribute value of a point 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 indices 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, a predicted attribute value is calculated by additionally multiplying an attribute value updated through the lift update process by a weight 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 estimating the attributes of nodes of a higher level by using an attribute associated with a node at a 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 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

weight of

class

to be.

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.

The point cloud decoder according to the 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) ), 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 synthesizer 11001 according to embodiments may generate an octree by obtaining an occupanci 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 embodiments may synthesize a surface based on a decoded geometry and/or a generated octree when trisupe 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-soap 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 generator 11008, the inverse lifting unit 11009, and/or the inverse color 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 on 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 not shown in the figure, the elements of the point cloud decoder of FIG. 11 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 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, 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 or a position value of points). 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 to 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 occult 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 voxels 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 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 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 to 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 in 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 in FIGS. 1 to 9 , detailed descriptions 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 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 sequence-level signaling, a Geometry Parameter Set (GPS) for signaling of geometry information coding, APS (Attribute Parameter Set) for signaling of attribute information coding, 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 on data included in a payload, etc. can As described above, the metadata processing unit 12007 according to 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 line processing. 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 receiving device shown in FIG. 13 is an example of the receiving device 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 to 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 occupancy code-based octree reconstruction processing unit 13003 according to 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 to 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 a geometry related thereto (eg, triangle reconstruction, up-sampling, voxelization) based on the surface model method, when trisoop geometry encoding is applied. can be performed. The surface model processing unit 13004 performs the same or similar operations to those 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 inverse color 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 an 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 operations and/or coding to the operations 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.

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 driving vehicle 1420 , the XR device 1430 , the smartphone 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 a 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 driving 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 the screen to the occupant 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 portion 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.

That is, 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 a real object image. Furthermore, MR technology is similar to the aforementioned AR technology in that it shows 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 this technology, encoding/decoding based on PCC, V-PCC, and G-PCC technology 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 to enable 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 illustrates examples of dense frame data and sparse frame data of point cloud data according to embodiments. Point cloud data may be acquired 20000 by the point cloud video acquisition unit 10001 according to embodiments, and may be encoded 20001 by the point cloud video encoder 10002 according to embodiments. In addition, the point cloud data may be received or acquired by the data input unit 12000 according to embodiments. 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 ). The point cloud data of 15 is the point cloud video encoder 10002 of FIG. 1 , the encoding 20001 of FIG. 2 , the encoder of FIG. 4 , the transmitting device of FIG. 12 , the xr device 1730 of FIG. 14 , and the transmitting device of FIG. 20 . , and/or hardware, software, firmware, or a combination thereof including one or more processors or integrated circuits configured to be communicable with one or more memories.

Point cloud data is encoded in the step of encoding the point cloud data according to the embodiments (S2900), and the encoded point cloud data is transmitted to the receiver as a bitstream in the step of transmitting the bitstream according to the embodiments (S2910) can be In the receiving device 10004 according to the embodiments, the receiver 10005 receives a bitstream including point cloud data (FIG. 30, S3000), and the point cloud decode 10006 may decode the received point cloud data. There is. (S3010)

Meanwhile, a point cloud according to embodiments is composed of a set of points, and each point may have geometry information (geometric information) and attribute information (attribute information). The point cloud encoding process may include compressing geometry information and compressing attribute information based on reconstructed geometry information reconstructed with information changed through compression. That is, the point cloud data according to the embodiments includes geometry data and attribute data. Geometry data includes information about position coordinate values for points.

The geometric information according to the embodiments includes positional information of each point, for example, (x, y) of a two-dimensional Cartesian coordinate system or (γ, θ) of a cylindrical coordinate system or (x, y, (x, y) of a Cartesian coordinate system in a three-dimensional space. z) or (γ, θ, z) of a cylindrical coordinate system, and (γ, θ, ρ) coordinate vectors of a spherical coordinate system.

Attribute information according to embodiments includes one or more vectors (R, G, B) indicating the color of a point or/and a brightness value or/and a reflection coefficient of lidar or/and a temperature value obtained from a thermal imaging camera. It may be a vector of values obtained from sensors.

In the point cloud, data can be divided into category 1 and category 3 according to its characteristics. Category 1 data is static data and consists of one frame. Category 3 data is dynamic data and consists of N frames or several points according to the method. In particular, category 3 frame data having an average of one million dots or less per sheet is encoded/decoded for each frame, and since it is composed of one slice, it can also be configured as a unit of a bitstream.

The category 3 frame data shown on the right side of FIG. 15 has a relatively low density of dots compared to the static data shown on the left side of FIG. 15 , and each dot has no color value and includes a reflectivity value. Category 3 frame data sequence is mainly aimed at low-latency and real-time processing in autonomous driving, but the conventional octree encoding/decoding method could not support low-delay decoding because the octree division in the decoder proceeded to the leaf. The method for transmitting point cloud data according to the embodiments enables real-time processing of data through a prediction tree-based encoding method, and at the same time improves encoding efficiency.

16 illustrates an example of generating a prediction tree structure and calculating and encoding a prediction value in a method of transmitting point cloud data according to embodiments. Generation and encoding of the prediction tree structure may be performed during encoding (20001, FIG. 2 ) by the point cloud video encoder 10002 ( FIG. 1 ) according to embodiments. In addition, the prediction tree structure construction unit 20001 (refer to FIG. 20) of the transmission apparatus according to the embodiments includes a data alignment unit 20002, a prediction tree formation unit 20003, a prediction value calculation unit 20004, and an encoding unit.

The prediction tree structure generation, prediction value calculation, and encoding process of Fig. 16 are the point cloud video encoder 10002 of Fig. 1, the encoding 20001 of Fig. 2, the encoder of Fig. 4, the transmitter of Fig. 12, and the xr device of Fig. 14 ( 1730), hardware, software, firmware, or a combination thereof including one or more processors or integrated circuits configured to communicate with the transmitting apparatus of FIG. 20, and/or one or more memories. have.

The prediction tree is created by considering the relationship between points from the x, y, and z coordinate values of the points. The data sorting unit 20002 aligns the input point cloud data (ply) according to a specific criterion, and the prediction tree forming unit 20003 searches for adjacent points based on the sorted points (ply) and nodes in the prediction modes. Calculate the predicted value of , and create a prediction tree structure. The prediction value calculator 20004 calculates a predicted value by a prediction equation for each point based on the generated prediction tree structure, and the encoder encodes the difference between the predicted values and the prediction mode for each point in the encoding process according to the coding order according to the coding order, The (encoded) point cloud data may be transmitted to the receiving device 10004 as a bitstream through the transmitter 10003 .

Meanwhile, the reception apparatus 10004 according to the embodiments receives a bitstream including point cloud data, and the decoder 10006 decodes (20003) (decodes) the encoded point cloud data. The reception apparatus 21001 (refer to FIG. 20 ) according to the embodiments includes a prediction value inverse calculation unit 21002, and the prediction value inverse calculation unit 21002 receives a bitstream including point cloud data to receive a difference in prediction values for each point. The position value of the point can be restored through the value and the prediction mode.

A method of transmitting point cloud data according to embodiments may include aligning point cloud data, forming a prediction tree, calculating a prediction value, and encoding the point cloud data as shown in FIG. 16 .

17 illustrates an example of generating a prediction tree structure from rearranged points in a method of transmitting point cloud data according to embodiments. The generation of the prediction tree structure may be performed by the prediction

tree forming units

20003 and 28003 according to embodiments.

The prediction tree structure of FIG. 17 is generated by the point cloud video encoder 10002 of FIG. 1 , the encoding 20001 of FIG. 2 , the encoder of FIG. 4 , the transmitting device of FIG. 12 , the xr device 1730 of FIG. hardware, software, firmware, or a combination thereof including one or more processors or integrated circuits configured to be communicable with the transmitting apparatus, and/or one or more memories.

The prediction

tree forming units

20003 and 28003 extract a proximity point through a spatial search algorithm such as kd-tree based on the rearranged points, and then compare prediction values according to a plurality of prediction modes with respect to the proximity point, and parent-child It forms a node of a relationship and connects the prediction tree. That is, adjacent points searched for through spatial search in the current node can be registered in the prediction tree, and when registered in the prediction tree, a plurality of prediction values according to prediction modes are compared, and the difference between the original and the prediction tree is the smallest. You can connect the node to the part.

A method of arranging point cloud data in the data aligning unit 20002 according to embodiments follows a morton order, a radius order, an azimuth order, or x, y, or It can be aligned in the z-axis direction. The order in which the point cloud data is sorted may be the order in which nodes of the prediction tree are connected. Alternatively, a prediction tree may be formed by searching for a neighboring node based on the rearranged points with respect to a node previously registered in the prediction tree and connecting the child nodes through prediction value comparison by prediction modes.

16 and 17, the points (ply) are arranged and rearranged by the data aligning unit 20002. The prediction tree forming unit 20003 searches for a predetermined number of neighboring nodes through a kd-tree spatial search algorithm based on the rearranged points (ply). Then, the prediction tree forming unit 20003 calculates a predicted value through the prediction modes for the searched adjacent nodes, compares the original prediction value with the difference values, and finds the found proximity to the part on the prediction tree having the smallest difference value. Nodes can be connected. That is, the prediction tree forming unit 20003 determines whether the predicted value is accurate by connecting the found adjacent node (the current node) to the child node of which parent node, and connects to the prediction tree. In making the determination, the prediction value of the current node can be calculated and compared by various prediction modes.

Meanwhile, the predicted value calculator 20004 according to the embodiments calculates a predicted value for each point according to prediction modes. Also, the prediction value calculator 20004 may compare a plurality of prediction values calculated for each point and transmit the prediction mode and the prediction value having the smallest difference value to the receiving apparatus 10004 . That is, one prediction mode and a predicted value for each point are transmitted to the receiving device 10004 , and the receiving device 10004 may reconstruct the corresponding point with the received prediction mode and predicted value. For the completed prediction tree, the coding order may be set based on a depth first search (DFS), but is not limited thereto.

On the other hand, the step of forming the prediction tree in the method for transmitting point cloud data according to the embodiments includes four predictions using a parent node (p0), a parent node (p1), or a parent node (p2). A prediction tree can be formed using prediction modes and non-prediction modes. At this time, in the case of parent node, parent-parent node, and parent-parent-parent node, the node connected to the upper level becomes the parent node in the prediction tree, and the node connected to the lower level It is set according to the relationship that becomes a child node. That is, the node connected to the parent of a specific node on the prediction tree is the parent node of the specific node, and the parent node of this parent node is the parent node of the specific node. Also, a parent node of a parent/parent node may be expressed as a parent/parent/parent node of a specific node.

The predicted value p of the node may be calculated based on the location values of the parent node p0 and the parent-parent node p1 or the parent-parent-parent node p2 of the corresponding node to be predicted. Hereinafter, prediction modes in which the prediction tree forming unit 20003 and the prediction value calculating unit 20004 calculate a prediction value according to embodiments will be described.

The no prediction mode is a mode in which prediction is not performed. Accordingly, (0, 0, 0) becomes the predicted value p, and the x, y, and z values that are residuals between the current node and the predicted value are used as they are during encoding. When the prediction value calculator 20004 calculates a prediction value for a specific point in a no prediction mode, the prediction mode information for the specific point may be transmitted to the reception device 10004 in mode 0. The non-prediction mode is typically used when predicting a root node, and when prediction mode information is transmitted in mode 0 for a specific node, the receiver 10004 may recognize the corresponding node as a root.

The calculation formula of the delta prediction mode calculates the predicted value using the parent node (p0, coordinate values (x', y', z')) of the corresponding node that is the prediction target. The difference value ( x' - x , y' - y , z' - z) of the predicted value with respect to the corresponding node (coordinate value (x, y, z)) is encoded and transmitted to the receiving device 10004, and the prediction value calculator When the prediction value is calculated in the delta prediction mode for a specific point in 20004, the prediction mode information for the specific point may be transmitted to the receiving device 10004 in mode 1 .

In the linear prediction mode, the calculation formula is the parent node (p0, coordinate values (x', y', z')) and parent node (p1, coordinate values (x'', y'', z'') ))), calculate the predicted value (p). The residual is (2x' - x"- x, 2y' - y"- y, 2z' - z"- z). Also, when the prediction value calculator 20004 calculates a prediction value in the linear prediction mode for a specific point, the prediction mode information for the specific point may be transmitted to the reception apparatus 10004 in mode 2 .

The calculation formula of the parallelogram prediction mode calculates the predicted value (p = 2 * p0 + p1 - p2) using the parent parent node (p2), the parent node (p1), and the parent node (p0). . When the position value of the parent parent node (p2) is x“', y”', z”', the difference between the x values (residual) is 2x' + x”- x”' - x, y The difference value is calculated in the same way for the axis and z-axis. In addition, when the prediction value calculator 20004 calculates a prediction value in the parallelogram prediction mode for a specific point, the prediction mode information for the specific point may be transmitted to the reception apparatus 10004 in mode 3 .

The prediction tree forming unit 20003 and the prediction value calculating unit 20004 according to embodiments may calculate a prediction value by further including an optional prediction mode in the above-described prediction modes.

In the optional prediction mode, a prediction point can be calculated in various ways by selectively applying a plurality of prediction methods according to the distribution of point cloud data. Hereinafter, prediction methods included in the optional prediction mode will be described.

Prediction method 1 is an xyz quadrant prediction method, in which point cloud data aligned by the data sorting unit 20002 according to embodiments is 1, 2, 3 or 4 based on the origin (0,0,0). In the case of distribution in quadrants, the prediction point is calculated using a formula that transforms the coefficients (constant values) in the formulas of delta prediction mode, linear prediction mode, or parallelogram prediction mode according to the quadrant distribution of the sorted data. For example, if the coefficients of each term in the formula are α, β, and γ, α, β, and γ can be arbitrary constants depending on the sorted data distribution. When the data distribution is located in the first quadrant, the coefficient (constant value) of each term uses a formula that satisfies the condition α*β*γ> 0. When the data distribution is located in the third quadrant, the coefficient (constant value) of each term is used. can use a formula that satisfies the condition of α*β*γ < 0. That is, when the optional prediction mode uses prediction method 1, the constant value for at least one node among the parent node, parent parent node, and parent parent parent node in the calculation formula of the prediction modes is changed based on the quadrant distribution of the sorted geometric data. can

Meanwhile, FIG. 18 shows an example of distribution of points arranged in an azimuth order according to embodiments, and a number indicated at each point means a coding order. As shown in Fig. 18, the data arranged in the order of Azimuth shows a data distribution that increases in the x-axis direction and increases in the y-axis direction in the first quadrant, and decreases in the x-axis direction and increases in the y-axis direction in the second quadrant. looks in shape Also, in the third quadrant, the data distribution pattern decreases in the x-axis direction and the y-axis direction, and in the fourth quadrant, the data distribution pattern increases in the x-axis direction and decreases in the y-axis direction. The prediction tree forming unit 20003 or the prediction value calculating unit 20004 according to the embodiments may calculate a prediction value using a prediction method of an appropriate optional prediction mode in consideration of the distribution of sorted data.

FIG. 18 numerically shows a coding order for points of point cloud data. Referring to FIG. 18 , there is a case where the coding order of points distributed in a line is jumped without proceeding in order. For example, in the case of the 122 point in the coding order, it is not located near the 121 point but is jumped and located in another arrangement. The more the prediction modes according to the embodiments are diversified, the better the prediction value can be predicted by the prediction formula, and as the prediction value is well predicted, the parent-child relationship of the prediction tree structure can be efficiently formed to reduce the coding order jumping phenomenon. This reduces the difference between predicted values in the encoding step to improve encoding efficiency.

Prediction method 2 of the optional prediction mode is a tetrahedral (Tetrahedron) prediction method and can be used when data is aligned in a specific axis (x, y, or z) direction. For example, frame data has circularly distributed data at the bottom (the bottom of the z-axis), so when sorting in ascending order along the z-axis, circularly distributed data (z values are 0 or p0, p1, From p2), the predicted value p can be calculated. That is, in the Tetrahedron prediction method, positions of remaining vertices in a virtual tetrahedron including three points as vertices may be calculated as predicted values.

In addition, the Tetrahedron prediction method may predict geometric data based on one or more points having the same or similar values for at least one of the x-axis, the y-axis, and the z-axis. That is, any one of the x-axis, y-axis, and z-axis values of the points (p0, p1, p2) used for prediction of geometric data may be the same or similar. In the prediction method 2, the coordinates of the prediction point can be calculated by the following calculation formula.

(x)) + a,

(y)) + b,

(z)) + c}

In the above formula, the constants a, b, and c may be √6/3 or other arbitrary constants, and the constants α, β, γ, a, b, and c are negative or positive numbers, and may be integers or real numbers.

19 is a diagram visually illustrating a prediction point p of a corresponding node through a tetrahedral (Tetrahedron) prediction method according to embodiments. That is, FIG. 19 shows the predicted position of the predicted point (p) from p0, p1, and p2 of the corresponding node. The number of points used in the tetrahedral prediction method may be three or less or more, and in this case, the calculation formula may be appropriately modified and applied according to the number of points.

On the other hand, prediction method 3 of the option prediction mode is an inverse parallelogram prediction method, and the formula is as follows.

The constants are composed of α, β, γ (-x <= α <= +x, -y <= β<= +y, -z <= γ<= +z), and can be any constant. . Also, the prediction method 3 may use at least one of the parent node (p0), the parent-parent node (p1), and the parent-parent-parent node (p2). In addition, prediction method 3 is set to α=-1, β=-1, γ=1 regardless of the quadrant.

can be That is, in the prediction method 3, the calculation formula of the parallelogram prediction mode based on the parent node, the parent-parent node, and the parent-parent-parent node and the calculation formula of each term coefficient sign are opposite to each other can be used. The number of points used in the antiparallelogram calculation formula may be appropriately changed.

Prediction methods 1 to 3 of the optional prediction mode according to the embodiments include a No prediction mode, a Delta prediction mode, and a Linear prediction mode, which are prediction modes according to the embodiments. ) or the calculation formula of the Parallelogram prediction mode can be substituted. The prediction method of the optional prediction mode may be adaptively selected according to the sorted point cloud data distribution, and a different prediction method may be applied to each slice of the point cloud data.

When forming the prediction tree structure, the prediction tree forming unit 20003 according to the embodiments includes a non-prediction mode (mode 0), a delta prediction mode (mode 1), a linear prediction mode (mode 2), and a parallelogram prediction mode (mode). By adding the optional prediction mode to 3), a total of 5 prediction formulas can be used.

The prediction value calculator 20004 according to embodiments may calculate a prediction value in four prediction modes when calculating the prediction value. In this case, the apparatus 10004 for receiving the prediction mode and difference value information from the transmitter 10003 according to the embodiments and encoding the difference value and the prediction mode having the smallest difference value among the prediction values calculated by the encoder according to the embodiments , 21001). The number of prediction modes and prediction formulas used in the prediction tree structure formation process and the encoding process may be different from each other.

The method of transmitting point cloud data according to embodiments may include an optional prediction mode except for a non-prediction mode among four prediction modes used when the prediction value calculation unit 20004 calculates a prediction value. Since the non-prediction mode generates a large difference between the prediction values, the prediction value of the corresponding node becomes accurate by excluding the non-prediction mode, and the encoding efficiency is improved by reducing the difference value. In the method of transmitting point cloud data according to the embodiments, information on four prediction modes including an optional prediction mode instead of a non-prediction mode in 2 bits required for transmission of the information on the four prediction modes is transmitted to the receiving

apparatuses

10004 and 21001. can transmit

20 shows an example of a prediction tree structure construction unit 20001 according to embodiments. The prediction tree structure construction unit 20001 may be included in the point cloud video encoder 10002 of the transmission apparatus 10001 according to embodiments. The prediction tree structure construction unit 20001 according to the embodiments corresponds to a point cloud video encoder (FIG. 1), a point cloud encoder (FIG. 4), an encoding process (FIG. 2), or component(s) thereof (FIG. 13) or may be combined. Also, the prediction tree formation process performed by the prediction tree structure construction unit 20001 may be performed in the encoding 20001 step of the point cloud video encoder 10002 .

The prediction tree structure construction unit 20001 includes a data alignment unit 20002, a prediction tree formation unit 20003, a prediction value calculation unit 20004, and an encoding unit.

The data sorting unit 20002 may sort the point cloud data in a Morton order, a Radius order, an azimuth order, or a specific axis (x, y, z axis) direction. Also, the data sorting unit may generate information on a sorting order (Sorting_order) of the input points, and the information on the sorting order may be transmitted to the receiving apparatus 10004 according to embodiments.

The prediction tree forming unit 20003 forms a prediction tree based on the data sorted by the data alignment unit 20002 . The prediction tree forming unit 20003 transmits to the receiving device 21001 whether or not to transmit the root node information based on the prediction mode (use_root_prediction_mode0). In the case of transmitting root node information based on the prediction mode, when the transmitting device transmits the non-prediction mode for a specific point to the receiving device 21001, the receiving device recognizes the corresponding point as a root node. When the transmission of the root node information is an index, if the transmitting device transmits the root node index information in the coding order to the receiving device 21001, the receiving device 21001 may recognize the root node according to the index information. Also, the prediction tree forming unit 20003 may signal information on the number of prediction modes to be used for forming the prediction tree.

The prediction value calculator 20004 may calculate a difference between prediction values using a total of four prediction modes including an optional prediction mode. In addition, the point cloud data transmission apparatus 10000 according to the embodiments may transmit prediction mode information used when calculating the prediction value to the

reception apparatuses

10004 and 21001 . For example, the delta prediction mode can be transmitted as 1, the linear prediction mode as 2, the parallelogram prediction mode as 3, and the optional prediction mode as 4.

Also, the transmitting device 10000 may transmit information (prediction mode) on which prediction method the optional prediction mode uses to the receiving

devices

10004 and 21001 . For example, you can pass 0 for xyz quadrant prediction method, 1 for tetrahedron prediction method, 2 for inverse parallelogram prediction method, and 3 for other prediction methods. have.

The encoder according to the embodiments may encode a difference between prediction values and a prediction mode according to a coding order, and the bitstream including the point cloud data may be transmitted to the receiving device 21001 .

21 shows an example of an apparatus 21001 for receiving point cloud data according to embodiments. The receiving device 21001 may correspond to the receiving device 10004 of FIG. 1 , and may perform the decoding 20003 process of FIG. 2 .

The reception device 21001 may include a prediction value inverse calculator 21002. The prediction value inverse calculator 21002 receives the bitstream including the encoded point cloud data and restores the geometric position value of the point based on the prediction mode and difference value information.

The predictive value inverse calculator 21002 of the receiving apparatus according to the embodiments includes a point cloud video decoder (FIG. 1), a point cloud decoder (FIGS. 11 and 12), a decoding process (FIG. 2) or its component(s) ( 14) or may be combined.

Meanwhile, the method of transmitting point cloud data according to embodiments may transmit whether prediction tree-based coding is performed to the receiving device. The parameters (metadata, signaling information, etc.) according to the embodiments are generated in a point cloud data transmission process according to the embodiments, and are transmitted to the receiving

devices

10004 and 21001 according to the embodiments to reconstruct the point cloud data. can be used For example, the parameter according to the embodiments may be generated by the metadata processing unit (or metadata generator) of the transmitting device according to the embodiments, and may be obtained from the metadata parser of the receiving device according to the embodiments.

22 shows an example of encoded point cloud data according to embodiments. The point cloud video encoder 10002 according to the embodiments encodes the point cloud data in the process of encoding 20001, and the transmitter 10003 according to the embodiments receives a bitstream including the encoded point cloud data. 10004) can be transmitted. When the predictive value calculator 20004 according to the embodiments calculates the predicted value of a point, the encoder may encode a difference value for each point and a prediction mode.

Encoded point cloud data (bitstream) according to embodiments is the point cloud video encoder 10002 of FIG. 1 , the encoding 20001 of FIG. 2 , the encoder of FIG. 4 , the transmitter of FIG. 12 , and the xr device of FIG. 14 . 1430 , to be generated by hardware, software, firmware, or a combination thereof including one or more processors or integrated circuits configured to be communicable with the transmitter 1430 , and/or one or more memories. can

In addition, the encoded point cloud data (bitstream) according to the embodiments is the point cloud video decoder 10006 of FIG. 1 , the decoding 20003 of FIG. 2 , the decoder of FIG. 11 , the transmitter of FIG. 13 , and the transmission device of FIG. By hardware, software, firmware, or a combination thereof including one or more processors or integrated circuits configured to communicate with the xr device 1430 , the receiving device of FIG. 21 , and/or one or more memories can be decoded.

The abbreviations shown in FIG. 22 are as follows.

- SPS: Sequence Parameter Set

- GPS: Geometry Parameter Set

- APS: Attribute Parameter Set

- TPS: Tile Parameter Set

- Geom: geometry bitstream = geometry slice header + geometry slice data

- Attr: Attribute bitstream = attribute brick header + attribute brick data

Point cloud data may be divided and processed for each area by a tile or a slice. In this case, each region may have a different importance level, and a different filter or a different filter unit may be applied to each region according to the importance level. Therefore, it is possible to use a filtering method having high result quality instead of high complexity in an important area.

On the other hand, depending on the receiver's capacity (capacity), instead of using a complex filtering method for the entire point cloud data, different filtering can be applied for each area (area divided into tiles or slices), so that the image quality is better in important areas and the system It is possible to ensure an appropriate latency (latency).

Accordingly, when the point cloud data is divided into tiles, different filters and different filter units may be applied to each tile. In addition, when the point cloud data is divided into slices, different filters and different filter units may be applied to each slice.

23 illustrates an example of a syntax of a sequence parameter set according to embodiments.

23 shows a syntax example of a sequence parameter set included in the bitstream of FIG. 22 . The prediction tree structure information may be signaled by being added to a sequence parameter set.

The prediction tree geometry coding flag (predictive_tree_geometry_coding_flag) indicates flag information regarding whether prediction tree-based coding is performed in geometry coding. A true value indicates that the prediction tree-based coding is performed, and a false value indicates that the prediction tree-based coding is performed.

The sorting order (sorting_order) represents information on a sorting criterion of point cloud data. For example, 0 is morton order, 1 is azimuth order, 2 is radius order, 3 is x-, y- or z-based order, and 4 is any other order. order can be indicated.

A use root prediction mode 0 (use_root_prediction_mode0) represents a method of transmitting root node information to the receiving device 21001 according to embodiments. A value of true indicates that root node information is transmitted based on the prediction mode (eg, mode 0), and a value of false indicates that root node information is transmitted as an index array. When the root node is transmitted as an index array, the index in the coding order of the point serving as the root node may be transmitted to the receiving device 21001 .

The prediction mode (prediction_method) signals a prediction method of the optional prediction mode. In addition, α, β, γ values or other arbitrary constant values used in the prediction method may be signaled. If the prediction mode (prediction_method) is 0, it indicates the xyz quadrant prediction method (adaptive quadrant prediction method), 1 indicates the tetrahedron prediction method, 2 indicates the inverse parallelogram prediction method, 3 may indicate other prediction methods.

Profile idc (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.)

The profile compatibility flag (profile_compatibility_flags) indicates that the bit stream complies with the profile indicated by the profile idc (profile_idc) equal to j specified in Annex A. The value of profile_compatibility_flag[j] may be 0 for a value of j that is not specified as an allowable value of profile_idc in Annex A.

Level_idc (level_idc) indicates a level of a bitstream that can satisfy Annex A of the H.264 standard document. The bit stream cannot contain level_idc values other than those specified in Annex A. Other values of level_idc may be used in the future by ISO/IEC.

When the SPS bounding box present flag (sps_bounding_box_present_flag) is 1, it indicates that the bounding box offset and size information are signaled.

SPS bounding box offset x(sps_bounding_box_offset_x) indicates the x offset of the source bounding box in Cartesian coordinates. If it does not exist, the sps_bounding_box_offset_x value is inferred to be 0.

SPS bounding box offset y(sps_bounding_box_offset_y) indicates the y offset of the source bounding box in Cartesian coordinates. If it does not exist, the sps_bounding_box_offset_y value is inferred to be 0.

The SPS bounding box offset z(sps_bounding_box_offset_z) indicates the z offset of the source bounding box in Cartesian coordinates. If it does not exist, the sps_bounding_box_offset_z value is inferred to be 0.

The SPS bounding box scale factor (sps_bounding_box_scale_factor) indicates a scale factor indicating a source bounding box in Cartesian coordinates. If it does not exist, the value of sps_bounding_box_scale_factor is inferred to be 1. If it does not exist, the value of sps_bounding_box_scale_factor is inferred to be 0.

The SPS bounding box size width (sps_bounding_box_size_width) indicates the width of the source bounding box in Cartesian coordinates. If it does not exist, the value of sps_bounding_box_size_width is assumed to be 10.

The SPS bounding box size height (sps_bounding_box_size_height) indicates the height of the source bounding box in Cartesian coordinates. If it does not exist, the value of sps_bound_box_size_height is inferred to be 1. If not, the value of sps_bounding_box_size_height is inferred to be 0.

The SPS bounding box size depth (sps_bounding_box_size_depth) indicates the depth of the source bounding box in Cartesian coordinates. If it does not exist, the value of sps_bound_box_size_depth is inferred to be 1. If not, the value of sps_bounding_box_size_depth is inferred to be 0.

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

The SPS sequence parameter set id (sps_seq_parameter_set_id) provides an identifier for the SPS so that other syntax elements can refer to it. The SPS sequence parameter set id (sps_seq_parameter_set_id) value MAY range from 0 to 15 inclusive of 0 in bit streams conforming to this version of this specification. A value other than 0 for sps_seq_parameter_set_id is reserved for future use in ISO/IEC.

The SPS number attribute set (sps_num_attribute_sets) indicates the number of coded attributes in the bitstream. The sps_num_attribute_sets value can be in the range 0 to 64.

The SPS according to the embodiments includes an attribute dimension [i] (attribute_dimension[i]), an attribute instance id[i] (attribute_instance_id[i]), an attribute bit depth [i] by the value of the SPS number attribute set (sps_num_attribute_sets) [i]), attribute CICP color primaries[i](attribute_cicp_colour_primaries[i]), attribute CICP transfer characteristics[i](attribute_cicp_transfer_characteristics[i]), attribute CICP matrix coefficients[i](attribute_cicp_matrix_coeffs[i]), attribute CICP video full range flag [i] (attribute_cicp_video_full_range_flag[i]), may include a known attribute label flag (known_attribute_label_flag[i], in this case, if the known attribute label flag [i] (known_attribute_label_flag[i]) is 1, the known attribute Contains the label (known_attribute_label[i], otherwise may include the attribute label four bytes[i](attribute_label_four_bytes[i]).

The attribute dimension[i](attribute_dimension[i]) specifies the number of components of the i-th attribute.

The attribute instance id[i](attribute_instance_id[i]) specifies the attribute instance ID.

The attribute bitdepth[i] (attribute_bitdepth[i]) designates the bitdepth of the i-th attribute signal.

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

The attribute CICP transfer characteristic[i](attribute_cicp_transfer_characteristics[i]) represents the reference photoelectron transfer characteristic function of the color attribute as a function of the source input linear optical intensity Lc with a nominal true value range of 0 to 1 or has a nominal true value range of 0 to 1. The phosphorus output represents the inverse of the reference electro-optical transfer characteristic function as a function of the linear optical intensity Lo.

The attribute CICP matrix coefficients[i](attribute_cicp_matrix_coeffs[i]) describes the matrix coefficients used to derive luma and chroma signals from green, blue, red or Y, Z and X bases.

24 shows a syntax example of a tile parameter set included in the bitstream of FIG. 22 . The prediction tree structure information may be signaled by being added to a tile parameter set.

A use root prediction mode 0 (use_root_prediction_mode0) represents a method of transmitting root node information to the receiving device 21001 according to embodiments. A value of true indicates that root node information is transmitted based on the prediction mode (eg, mode 0), and a value of false indicates that root node information is transmitted as an index array. When the root node is transmitted as an index array, the coding order index of a point serving as the root node may be transmitted to the receiving device 21001 .

The prediction mode (prediction_method) signals a prediction method of the optional prediction mode. In addition, α, βγ values or other arbitrary constant values used in the prediction method may be signaled. If the prediction mode (prediction_method) is 0, it indicates the xyz quadrant prediction method (adaptive quadrant prediction method), 1 indicates the tetrahedron prediction method, 2 indicates the inverse parallelogram prediction method, 3 may indicate other prediction methods.

The number tile (num_tiles) specifies the number of tiles signaled for the bitstream. If it does not exist, the number tile (num_tiles) is inferred to be 0.

The tile bounding box offset x[i] (tile_bounding_box_offset_x[i]) indicates the x offset of the i-th tile in Cartesian coordinates. If it does not exist, the tile bounding box offset x[0] (tile_bounding_box_offset_x[0]) value is inferred as the SPS bounding box offset x (sps_bounding_box_offset_x).

The tile bounding box offset y[i](tile_bounding_box_offset_y[i]) represents the y offset of the i-th tile in Cartesian coordinates. If it does not exist, the tile bounding box offset y[0](tile_bounding_box_offset_y[0]) value is inferred as the SPS bounding box offset y(sps_bounding_box_offset_y).

The tile bounding box offset z[i] (tile_bounding_box_offset_z[i]) indicates the z offset of the i-th tile in Cartesian coordinates. If it does not exist, the value of the tile bounding box offset z[0] (tile_bounding_box_offset_z[0]) is inferred as the SPS bounding box offset z(sps_bounding_box_offset_z).

The tile bounding box scale factor [i] (tile_bounding_box_scale_factor[i]) indicates the scale factor of the i-th tile of Cartesian coordinates. If it does not exist, the value of the tile bounding box scale factor [0] (tile_bounding_box_scale_factor[0]) is inferred as the SPS bounding box scale factor (sps_bounding_box_scale_factor).

Tile bounding box size width[i](tile_bounding_box_size_width[i]) indicates the width of the i-th tile in Cartesian coordinates. If it does not exist, the tile bounding box size width[0] (tile_bounding_box_size_width[0]) value is inferred as the SPS bounding box size width (sps_bounding_box_size_width).

tile bounding box size height[i](tile_bounding_box_size_height[i] indicates the height of the i-th tile in Cartesian coordinates. If it does not exist, the tile bounding box size height[0](tile_bounding_box_size_height[0] value is the SPS bounding box size height. It is inferred as (sps_bounding_box_size_height).

The tile bounding box size depth[i](tile_bounding_box_size_depth[i]) represents the depth of the ith tile in Cartesian coordinates. If it does not exist, the tile bounding box size depth [0] (tile_bounding_box_size_depth[0]) value is inferred as the SPS bounding box size depth (sps_bounding_box_size_depth).

The TPS according to the embodiments is a tile bounding box offset x[i] (tile_bounding_box_offset_x[i]) by a value of a number tile (num_tiles), a tile bounding box offset y[i] (tile_bounding_box_offset_y[i]), a tile bounding box offset z [i](tile_bounding_box_offset_z[i]), tile bounding box scale factor[i](tile_bounding_box_scale_factor[i]), tile bounding box size width[i](tile_bounding_box_size_width[i]), tile bounding box size height[i](tile_bounding_box_size_height) [i] May contain information.

25 shows an example of a syntax of a geometric parameter set included in the bitstream of FIG. 22 . The prediction tree structure information may be signaled by being added to a geometry parameter set.

The prediction mode (prediction_method) signals a prediction method of the optional prediction mode. In addition, α, βγ values or other arbitrary constant values used in the prediction method may be signaled. If the prediction mode (prediction_method) is 0, it represents the xyz quadrant prediction method (adaptive quadrant prediction method), 1 represents the tetrahedron prediction method, 2 represents the inverse parallelogram prediction method, 3 may indicate other prediction methods.

The GPS geom parameter set id (gps_geom_parameter_set_id) provides a GPS identifier for reference in other syntax elements. The GPS geom parameter set id (gps_seq_parameter_set_id) value may range from 0 to 15.

The GPS sequence parameter set id (gps_seq_parameter_set_id) specifies an SPS sequence parameter set id (sps_seq_parameter_set_id) value for the active SPS. The GPS sequence parameter set id (gps_seq_parameter_set_id) value may range from 0 to 15.

The geometry coding type (geometry_coding_type) indicates the geometry coding type of Table 71 Table 71 for the specified geometry coding type (geometry_coding_type) value. The value of geometry_coding_type shall be equal to 0 or 1 in bitstreams conforming to this version of this specification. Another value of the geometry coding type (geometry_coding_type) may be used later in ISO/IEC. Decoders conforming to this version of this specification MUST ignore the reserved value of the geometry coding type (geometry_coding_type). 0=octree, 1=Triangle Soup(Trisoup)

When the GPS box present flag (gps_box_present_flag) is 1, it specifies that additional bounding box information is provided in the geometry header referring to the current GPS. Specifies that additional bounding box information is not signaled in the geometry header when the GPS bounding box present flag (gps_bounding_box_present_flag) is 0.

The value of the attribute dump type (attr_dump_type). The value of the attribute coding type (attr_coding_type) may be 0, 1 or 2 in a bit stream conforming to this version of this specification. Other values of the attribute coding type (attr_coding_type) may be used later in ISO/IEC. Decoders conforming to this version of this specification ignore the reserved value of attribute coding type (attr_coding_type). 0 = Predicting weight lifting, 1 = Region Adaptive Hierarchical Transferm (RAHT), 2 = Fixed weight lifting

The prediction proximity number (num_pred_nearest) specifies the maximum number of nearest neighbors to use for prediction. The value of the number of nearest neighbors in the prediction can be in the range of 1 to xx.

The maximum direct predictor number (max_num_direct_predictors) specifies the maximum number of predictors to be used for direct prediction. The value of the maximum direct predictor number (max_num_direct_pedictors) must range from 0 to the nearest neighbor prediction number (num_pred_nearest_neighbors). The value of the MaxNumPredictors variable used in the decoding process is as follows.

Maximum predictor numbers (MaxNumPredictors) = maximum direct predictor numbers (max_num_direct_predictor)s + 1

Lifting search range (lifting_search_range) specifies a search range for lifting.

The lifting quant step size (lifting_quant_step_size) specifies the quantification step size for the first component of the attribute. The quant step size (quant_step_size) value may be in the range of 1 to xx.

The lifting quant step size chroma (lifting_quant_step_size_chroma) specifies the quantification step size for the chroma component of an attribute when the attribute is a color. A quant step size chroma (quant_step_size_chroma) value may be in the range of 1 to xx.

The load binary tree enable flag (lod_binary_tree_enabled_flag) specifies whether to generate a binary tree log.

Number detail level minus 1 (num_detail_levels_minus1) specifies the number of levels of detail for attribute coding. The number detail level minus1 (num_detail_levels_minus1) value can range from 0 to xx. The sampling distance squared[idx](sampling_distance_squared[idx]) specifies the square of the sampling distance with respect to idx. The sampling distance squared[] (sampling_distance_squared[]) value may be in the range of 0 to xx. The adaptive_prediction_threshold specifies a prediction threshold.

The RAHT depth (raht_depth) specifies the number of levels of detail for the RAHT. The value of the depth RAHT may be in the range of 1 to xx.

The RAHT binary level threshold (raht_binarylevel_threshold) specifies a level of detail for truncating the RAHT coefficients. The value of binaryLevelThreshold RAHT (binaryLevelThreshold RAHT) may be in the range of 0 to xx. The RAHT quant step size (raht_quant_step_size) specifies the quantification step size for the first component of the attribute. The quant step size (quant_step_size) value may be in the range of 1 to xx. When the APS extension present flag (aps_extension_present_flag) is 1, it specifies that the APS extension data (aps_extension_data) syntax structure exists in the APS RBSP syntax structure. When the APS extension present flag (aps_extension_present_flag) is 0, it specifies that this syntax structure does not exist. If it does not exist, the value of the APS extension present flag (aps_extension_present_flag) is inferred to be 0.

The APS extension data flag (aps_extension_data_flag) may have a value. Its presence and value do not affect decoder conformance to the profile specified in Annex A. A decoder conforming to the profile specified in Annex A.

If the unique geometry point flag (unique_geometry_points_flag) is 1, it indicates that the positions of all output points are unique. If the unique geometry point flag (unique_geometry_points_flag) is 0, it indicates that the positions of the output points may be the same.

If the neighbor context restriction flag (Neighbor_context_restriction_flag) is 0, it indicates that octree accuracy coding uses a context determined from six neighboring parent nodes. If the neighbor context restriction flag (Neighbor_context_restriction_flag) is 1, it indicates that the octree coding uses the context determined only by the sibling nodes.

If the inferred direct coding mode enable flag (Inferred_direct_coding_mode_enabled_flag) is 0, it indicates that octree coding uses the inferred direct coding mode (inferred_direct_coding_mode). If the inferred direct coding mode enable flag (Inferred_direct_coding_mode_enabled_flag) is 1, it indicates that octree coding uses multiple contexts determined from sibling neighboring nodes.

If the bitwise occupancy coding flag (bitwise_occupancy_coding_flag) is 1, it indicates that the geometry node occupancy was encoded using bitwise contextualization of the syntax element occupancy_map (occipancy_map). If the bitwise accuracy coding flag (bitwise_occupancy_coding_flag) is 0, it indicates that the geometry node occupancy was encoded using a dictionary encoded with the syntax element occupancy byte (occypancy_byte).

When the adjacent child contextualization enable flag (Adjacent_child_contextualisation_enabled_flag) is 1, it indicates that adjacent lower nodes of the neighboring octree nodes are used for bitwise occupancy contextualization. If the adjacent child contextualization enable flag (Adjacent_child_contextualisation_enabled_flag) is 0, it indicates that the neighboring octree nodes are not used for occupancy contextualization.

Log2 neighbor availability boundary (log2_neighbour_avail_boundary) specifies the value of the neighbor availability boundary (NeighbAvailBoundary) variable used in the decoding process as follows.

NeighborAvailBoundary = 2log2 NeighborAvailBoundary(2log2_neighbour_avail_boundary)

If the neighbor context restriction flag (Neighbor_context_restriction_flag) is 1, the neighbor availability mask (NeighbAvailabilityMask) is set to 13. Otherwise, the neighbor context restriction flag (Neighbor_context_restriction_flag) is 0, and the neighbor availability mask (NeighbAvailabilityMask) is set as follows.

(1 << log2 neighbor available boundary(log2_neighbour_avail_boundary)).

Log2 intra-pred maximum node size (log2_intra_pred_max_node_size) specifies.

Log2 treetop node size (log2_trisoup_node_size) designates a variable treetop node size (TrisoupNodeSize) as the size of a triangle node as follows.

TrisoupNodeSize = 2log2 TrisoupNodeSize (2log2_trisoup_node_size)

The value of Log2 trisoup node size (log2_trisoup_node_size) must be greater than or equal to 0. If the Log2 trisoup node size (log2_trisoup_node_size) is 0, the geometry bitstream contains only the octree coding syntax.

The trisoup_depth specifies the number of bits used to represent each component of the point coordinate. A value of the trisoup_depth may be in the range of 2 to 21. [Ed(df): 21 may be a level limit.]

The trisoup triangle level (trisoup_triangle_level) specifies the level at which the octree is organized. The value of the trisoup triangle level (trisoup_triangle_level) may be in the range of 1 to the trisoup_depth-1.

If the GPS extension present flag (gps_extension_present_flag) is 1, it specifies that the GPS extension data (gps_extension_data) syntax structure is present in the GPS RBSP syntax structure. If the GPS extension present flag (gps_extension_present_flag) is 0, it specifies that the syntax structure does not exist. If it does not exist, the value of the GPS extension present flag (gps_extension_present_flag) is inferred to be 0.

The GPS extension data flag (gps_extension_data_flag) may have a value. Its presence and value do not affect decoder conformance to the profile specified in Annex A. A decoder conforming to the profile specified in Annex A.

26 shows an example of a syntax of an attribute parameter set included in the bitstream of FIG. 22 . The prediction tree structure information may be signaled by being added to an attribute parameter set.

The APS attribute parameter set id (aps_attr_parameter_set_id) provides an identifier for the APS so that other syntax elements can refer to it. APS attribute parameter set id (aps_attr_parameter_set_id) value may be in the range of 0 to 15.

APS sequence parameter set id (aps_seq_parameter_set_id) specifies an SPS sequence parameter set id (sps_seq_parameter_set_id) value for the active SPS. The APS sequence parameter set id (aps_seq_parameter_set_id) value may be in the range of 0 to 15.

The attribute coding type (attr_coding_type) indicates the coding type for the given attribute in Table 72 and Table 72.

27 illustrates an example of syntax of a slice header of a geometry bitstream according to embodiments.

27 shows an example of syntax of a slice header of a geometry bitstream included in the bitstream of FIG. 22 . The prediction tree structure information may be signaled by being added to a slice header of a Geom.

The GSH geometry parameter set id (gsh_geometry_parameter_set_id) specifies a GSH geom parameter set id (gps_geom_parameter_set_id) value of the active GPS.

The GSH tile id (gsh_tile_id) designates the ID of the tile.

GSH slice id (gsh_slice_id) specifies the ID of the slice.

GSH box log2 scale (gsh_box_log2_scale) specifies the scale value.

GSH box origin x(gsh_box_origin_x) specifies the x of the source bounding box in Cartesian coordinates.

GSH box origin y(gsh_box_origin_y) specifies the y of the source bounding box in Cartesian coordinates.

GSH box origin z(gsh_box_origin_z) specifies the z of the source bounding box in Cartesian coordinates.

GSH log2 maximum node size (gsh_log2_max_nodesize) specifies the value of the variable maximum node size (MaxNodeSize) used in the decoding process as follows.

Maximum node size (MaxNodeSize) = 2 (GSH log2 maximum node size (gsh_log2_max_nodesize))

GSH point number (gsh_points_numbe)r specifies the number of coded points in the slice.

Meanwhile, FIG. 28 shows an example of a transmitter (left) and a receiver (right) according to embodiments. The transmitter of FIG. 28 is an example of the transmitter 10000 of FIG. 1 (or the point cloud encoder of FIG. 4 ). The transmitter shown in FIG. 28 may perform at least one 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 .

Each component of FIG. 28 may correspond to hardware, software, firmware, or a combination thereof including one or more processors or integrated circuits configured to communicate with one or more memories.

The transmitter of FIG. 28 includes a prediction tree structure construction unit 28001 according to embodiments, and the prediction tree structure construction unit 28001 includes a data alignment unit 28002, a prediction tree formation unit 28003, and a prediction value calculation unit ( 28004) and an encoder. The prediction tree structure construction unit 28001 of the transmitter of FIG. 28 may correspond to the prediction tree structure construction unit 20001 of FIG. 20 .

The receiver of Fig. 28 is an example of the receiver 10004 of Fig. 1 (or the point cloud decoder of Figs. 10 and 11). The receiver shown in FIG. 28 may perform at least one 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 receiver of FIG. 28 includes a prediction value inverse calculation unit 28005 according to embodiments, and the prediction value inverse calculation unit 28005 is a point based on a prediction mode and a difference value for a point received from a transmitter according to embodiments of the geometric data can be restored. The inverse predicted value calculator 28005 of the receiver of FIG. 28 may correspond to the inverse predicted value calculator 21002 of the receiver of FIG. 21 .

29 shows an example of a method for transmitting point cloud data according to embodiments. The transmitting apparatus 10000 according to the embodiments may perform encoding the point cloud data ( S2900 ) and transmitting the bitstream including the point cloud data ( S2910 ).

The step of encoding the point cloud data (S2900) includes the point cloud video encoder 10002 of FIG. 1, the encoding 20001 of FIG. 2, the encoder of FIG. 4, the transmitting device of FIG. 12, the xr device 1430 of FIG. Encoding point cloud data by hardware, software, firmware, or a combination thereof including one or more processors or integrated circuits configured to communicate with the transmitting apparatus of FIG. 20, and/or one or more memories is a step

More specifically, the encoding of the point cloud data ( S2900 ) may include encoding geometric data of the point cloud data and encoding attribute data of the point cloud data.

The encoding of the geometry data includes the steps of aligning the geometry data, forming a prediction tree for the geometry data, calculating a prediction value for the geometry data constituting the prediction tree, and encoding the geometry data. include Each step may be performed by the data sorting unit 28002, the prediction tree forming unit 28002, the prediction value calculating unit 28004, and the encoding unit of FIG. 28 .

In the step of forming the prediction tree, the prediction tree can be formed by calculating the prediction value based on the four prediction modes and the non-prediction mode using at least one node among the parent node, parent-parent node, or parent-parent-parent node. have. In this case, one of the four prediction modes may be an optional prediction mode in which a plurality of prediction methods are selectively applied according to the distribution of geometric data.

The calculating of the predicted value may include calculating the predicted value based on four prediction modes using at least one of a parent node, a parent-parent node, and a parent-parent-parent node. One of the four prediction modes may be an optional prediction mode for selectively applying a plurality of prediction methods according to the distribution of the geometric data.

Meanwhile, the step of transmitting the bitstream including the point cloud data ( S2910 ) is performed by the transmitter 10003 of FIG. 1 , the transmission processing unit 12012 of FIG. 12 , the xr device 1430 of FIG. 14 , and/or one or more memories It is a step of transmitting the point cloud data as in the transmission 20002 of FIG. 2 by hardware, software, firmware, or a combination thereof including one or more processors or integrated circuits configured to be able to communicate with each other.

In more detail, in the step of transmitting the bitstream including the point cloud data ( S2910 ), the index information of the root node according to the coding order and the prediction mode used in calculating the prediction value of the geometry data may be transmitted. By passing information about the root node as an index instead of in mode 0 (no prediction mode), the optional prediction mode can be used when calculating the prediction value of the geometry data, and encoding efficiency can be improved with more accurate prediction.

30 shows an example of a receiving method according to embodiments. The reception method according to the embodiments may include receiving a bitstream including point cloud data (S3000) and decoding the point cloud data (S3010).

Receiving the bitstream including the point cloud data (S3000) includes the receiving device 10004 of FIG. 1 , the receiving device of FIGS. 10 and 11 , the receiving unit 13000 of FIG. 13 , and the xr device 1430 of FIG. 14 . , hardware, software, firmware, or a combination thereof including one or more processors or integrated circuits configured to communicate with the receiver of FIG. 21 , the receiver of the receiver of FIG. 28 and/or one or more memories Receive point cloud data.

In the receiving of the point cloud data, information on a root node generated based on index information according to a coding order, information on a prediction mode in which a prediction value of the point cloud data is calculated, and information on a difference value between the prediction values may be received.

The step of decoding the point cloud data (S3010) is the point cloud video decoder 10006 of FIG. 1, the receiver of FIGS. 10, 11, and 13, the xr device 1430 of FIG. 14, and the receiver of FIGS. 21 and 28 and/or decodes the point cloud data by hardware, software, firmware, or a combination thereof including one or more processors or integrated circuits configured to be communicable with one or more memories.

Decoding the point cloud data may include decoding geometry data of the point cloud data and decoding attribute data of the point cloud data.

In the decoding of the geometry data of the point cloud data, the geometry data is restored based on the prediction mode information for each point and the difference value information of the prediction values. At this time, the geometry data may be reconstructed based on four prediction modes using at least one node of the parent node, the parent parent node, or the parent parent parent node, and one of the four prediction modes is of the geometry data. It may be an optional prediction mode in which a plurality of prediction methods are selectively applied according to a distribution.

When the optional prediction mode is the first mode, the optional prediction mode may change a constant value for at least one of a parent node, a parent parent node, and a parent parent parent node based on a distribution on a quadrant of the geometric data.

When the optional prediction mode is the second mode, geometric data may be reconstructed based on at least one or more points having the same or similar values of at least one of the x-axis, y-axis, and z-axis.

When the optional prediction mode is the third mode, the geometric data may be restored by reversing the sign of a constant value with respect to a prediction mode based on a parent node, a parent-parent node, and a parent-parent-parent node.

A reception apparatus according to embodiments includes the reception apparatus 10004 of FIG. 1 , the reception apparatus of FIG. 11 , the xr apparatus 1430 of FIGS. 13 and 14 , the receiver of FIGS. 21 and 28 , and/or one or more memories; The point cloud data may be received, decoded, and rendered by hardware, software, firmware, or a combination thereof including one or more processors or integrated circuits configured to be communicable.

A reception apparatus according to the embodiments includes a reception unit for receiving point cloud data, a decoder for decoding the point cloud data, and a renderer for rendering the point cloud data.

The receiver may receive geometry data of the point cloud data and attribute data of the point cloud data. In addition, the receiver receives information on the root node generated based on the index information according to the coding order, the prediction mode information calculated by calculating the predicted value of the point cloud data, and the difference value information between the predicted values and transmits the received information to the decoder.

The decoder includes a prediction value inverse calculator for reconstructing geometry data based on prediction mode information that calculates a prediction value of the point cloud data and information on a difference value between prediction values.

The prediction value inverse calculator reconstructs the geometry data based on four prediction modes using at least one node of a parent node, a parent parent node, or a parent parent node, and one of the four prediction modes is the geometry data It may be an optional prediction mode in which a plurality of prediction methods are selectively applied according to the distribution of .

When the optional prediction mode is the first mode, the optional prediction mode changes a constant value of at least one of a parent node, a parent parent node, and a parent parent node based on a distribution on a quadrant of the geometric data.

When the optional prediction mode is the second mode, geometric data is reconstructed based on at least one point having the same or similar values of at least one of the x-axis, y-axis, and z-axis.

When the optional prediction mode is the third mode, geometric data may be restored by reversing the sign of a constant value with respect to a prediction mode based on a parent node, a parent-parent node, and a parent-parent-parent node.

The embodiments have been described in terms of a method and/or an apparatus, and the description of the method and the description of the apparatus may be applied complementary to each other.

Although each drawing is 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 of ordinary skill 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 part of each embodiment is selectively combined so that various modifications can be made to the embodiments. 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 art 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. In addition, it may be implemented in the form of a carrier wave, such as transmission through the Internet may be included. 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” would be interpreted as “A and/or B”, and “A, B” would be 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 components 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. 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 that features, numbers, steps, elements, and/or components are present, and does not mean that the 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.

In addition, the operations according to the embodiments described in this document may be performed by a transceiver including a memory and/or a processor according to the embodiments. The memory may store programs for processing/controlling operations according to the embodiments, and the processor may control various operations described in this document. The processor may be referred to as a controller or the like. In embodiments, operations may be performed by firmware, software, and/or a combination thereof, and the firmware, software, and/or a combination thereof may be stored in a processor or stored in a memory.

Meanwhile, the operations according to the above-described embodiments may be performed by the transmitting apparatus and/or the receiving apparatus according to the embodiments. The transceiver device may include a transceiver for transmitting and receiving media data, a memory for storing instructions (program code, algorithm, flowchart and/or data) for a process according to embodiments, and a processor for controlling operations of the transmitting/receiving device. can

A processor may be referred to as a controller or the like, and may correspond to, for example, hardware, software, and/or a combination thereof. Operations according to the above-described embodiments may be performed by a processor. In addition, the processor may be implemented as an encoder/decoder or the like for the operation of the above-described embodiments.

As described above, the related content has been described in the best mode for carrying out the embodiments.

As described above, the embodiments may be applied in whole or in part 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 modifications/modifications, which do not depart from the scope of the claims and the like.

Claims

encoding the point cloud data; and

Transmitting a bitstream including the point cloud data,

How to transfer point cloud data.
The method according to claim 1,

The encoding of the point cloud data comprises:

encoding geometry data of the point cloud data;

encoding attribute data of the point cloud data;

How to transfer point cloud data.
3. The method according to claim 2,

The encoding of the geometric data comprises:

aligning the geometric data;

forming a prediction tree for the geometric data;

calculating a prediction value for the geometric data constituting the prediction tree;

encoding the geometric data;

How to transfer point cloud data.
4. The method according to claim 3,

The step of forming the prediction tree comprises:

A method comprising: forming a prediction tree using four prediction modes that predict based on at least one of a parent node, a parent parent node, or a parent parent node, and a mode that does not predict,

How to transfer point cloud data.
5. The method according to claim 4,

The step of forming the prediction tree comprises:

One of the four prediction modes is an optional prediction mode for selectively applying a plurality of prediction methods according to the distribution of the geometric data,

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

The step of forming the prediction tree comprises:

When the optional prediction mode is the first mode, the optional prediction mode changes a constant value for at least one of a parent node, a parent parent node, and a parent parent parent node based on a distribution on a quadrant of the geometry data,

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

The step of forming the prediction tree comprises:

When the optional prediction mode is the second mode, predicting the geometry data based on at least one point having the same or similar values of at least one of the x-axis, y-axis, and z-axis,

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

The step of forming the prediction tree comprises:

When the optional prediction mode is the third mode, predicting the geometry data by reversing the sign of a constant value for a prediction mode based on a parent node, a parent parent node, and a parent parent node,

How to transfer point cloud data.
4. The method according to claim 3,

Calculating the predicted value comprises:

Calculate the prediction value based on the four prediction modes using at least one of the parent node, parent-parent node, or parent-parent-parent node,

Transmitting the bitstream including the point cloud data,

Including the step of transmitting the index information of the root node according to the coding order and the prediction mode used in calculating the prediction value of the geometry data,

How to transfer point cloud data.
10. The method of claim 9,

Calculating the predicted value comprises:

One of the four prediction modes is an optional prediction mode for selectively applying a plurality of prediction methods according to the distribution of the geometric data,

How to transfer point cloud data.
11. The method of claim 10,

Calculating the predicted value comprises:

When the optional prediction mode is the first mode, the optional prediction mode changes a constant value for at least one node among a parent node, a parent parent node, and a parent parent parent node based on a distribution on a quadrant of the geometric data.

How to transfer point cloud data.
11. The method of claim 10,

Calculating the predicted value comprises:

When the optional prediction mode is the second mode, predicting the geometry data based on at least one point having the same or similar values of at least one of the x-axis, y-axis, and z-axis,

How to transfer point cloud data.
Calculating the predicted value comprises:

When the optional prediction mode is the third mode, predicting the geometry data by reversing the sign of a constant value for a prediction mode based on a parent node, a parent parent node, and a parent parent node,

How to transfer point cloud data.
an encoder for encoding point cloud data; and

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

The encoder is

a data alignment unit for aligning the geometric data of the point cloud data;

a prediction tree forming unit for forming a prediction tree for the geometric data;

a predicted value calculator for calculating a predicted value for the geometric data based on the prediction tree; and

Comprising an encoder for encoding the geometry data,

Point cloud data transfer device.
15. The method of claim 14,

The predicted value calculator,

Calculate the prediction value based on the four prediction modes using at least one of the parent node, parent-parent node, or parent-parent-parent node,

The transmitter is

Transmitting the index information of the root node according to the coding order and the prediction mode used in calculating the prediction value of the geometry data,

Point cloud data transfer device.
16. The method of claim 15,

The predicted value calculator,

One of the four prediction modes is an optional prediction mode for selectively applying a plurality of prediction methods according to the distribution of the geometric data,

Point cloud data transfer device.
17. The method of claim 16,

The predicted value calculator,

When the optional prediction mode is the first mode, the optional prediction mode changes a constant value for at least one node among a parent node, a parent parent node, and a parent parent parent node based on a distribution on a quadrant of the geometric data.

Point cloud data transfer device.
16. The method of claim 15,

The predicted value calculator,

When the optional prediction mode is the second mode, predicting the geometry data based on at least one or more points having the same or similar values of at least one of the x-axis, y-axis, and z-axis,

Point cloud data transfer device.
16. The method of claim 15,

The predicted value calculator,

When the optional prediction mode is the third mode, predicting the geometry data by reversing the sign of a constant value for a prediction mode based on a parent node, a parent parent node and a parent parent node,

Point cloud data transfer device.
receiving point cloud data;

decoding the point cloud data; and

rendering the point cloud data;

How to receive point cloud data.
21. The method of claim 20,

The decoding of the point cloud data comprises:

decoding the geometry data of the point cloud data;

decoding attribute data of the point cloud data;

How to receive point cloud data.
22. The method of claim 21,

Receiving the point cloud data comprises:

Receive information on the root node generated based on the index information according to the coding order, prediction mode information for calculating the prediction value of the point cloud data, and information on the difference value between the prediction values,

Decoding the geometry data comprises:

Comprising the step of reconstructing the geometry data based on the prediction mode information and the difference value information,

How to receive point cloud data.
23. The method of claim 22,

Decoding the geometry data comprises:

Restoring the geometry data based on four prediction modes using at least one of a parent node, a parent parent node, or a parent parent node node,

One of the four prediction modes is an optional prediction mode for selectively applying a plurality of prediction methods according to the distribution of the geometric data,

How to receive point cloud data.
24. The method of claim 23,

Decoding the geometry data comprises:

When the optional prediction mode is the first mode, the optional prediction mode changes a constant value for at least one node among a parent node, a parent parent node, and a parent parent parent node based on a distribution on a quadrant of the geometric data.

How to receive point cloud data.
24. The method of claim 23,

Decoding the geometry data comprises:

If the optional prediction mode is the second mode, restoring the geometry data based on at least one or more points having the same or similar values of at least one of the x-axis, y-axis, and z-axis,

How to receive point cloud data.
24. The method of claim 23,

Decoding the geometry data comprises:

When the optional prediction mode is the third mode, the geometry data is restored by reversing the sign of the constant value for the prediction mode based on the parent node, the parent parent node and the parent parent node,

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

a decoder for decoding the point cloud data; and

a renderer for rendering the point cloud data; including,

The receiving unit,

Receiving the geometry data of the point cloud data and the attribute data of the point cloud data,

Point cloud data receiving device.
28. The method of claim 27,

The receiving unit,

Receives information on the root node generated based on the index information according to the coding order, the prediction mode information for calculating the prediction value of the point cloud data, and the difference value information between the prediction values and transmits it to the decoder,

The decoder is

and a prediction value inverse calculator for reconstructing the geometry data based on the prediction mode information and the difference value information,

The predicted value inverse calculator,

Restoring the geometry data based on four prediction modes using at least one of a parent node, a parent parent node, or a parent parent node node,

One of the four prediction modes is an optional prediction mode for selectively applying a plurality of prediction methods according to the distribution of the geometric data,

Point cloud data receiving device.
29. The method of claim 28,

When the optional prediction mode is the first mode, the optional prediction mode changes a constant value for at least one node among a parent node, a parent parent node, and a parent parent parent node based on a distribution on a quadrant of the geometry data.

Point cloud data receiving device.
29. The method of claim 28,

If the optional prediction mode is the second mode, restoring the geometry data based on at least one or more points having the same or similar values of at least one of the x-axis, y-axis, and z-axis,

Point cloud data receiving device.
29. The method of claim 28,

When the optional prediction mode is the third mode, the geometry data is restored by reversing the sign of the constant value for the prediction mode based on the parent node, the parent parent node and the parent parent node,

Point cloud data receiving device.