WO2020189891A1

WO2020189891A1 - Point cloud data transmission apparatus, point cloud data transmission method, point cloud data reception apparatus, and point cloud data reception method

Info

Publication number: WO2020189891A1
Application number: PCT/KR2020/001615
Authority: WO
Inventors: 오세진; 허혜정; 심동규; 이종석; 최한솔
Original assignee: 엘지전자 주식회사
Priority date: 2019-03-15
Filing date: 2020-02-04
Publication date: 2020-09-24

Abstract

A point cloud data transmission method according to embodiments may comprise the steps of: encoding point cloud data; and/or transmitting a bitstream including the point cloud data. In addition, a point cloud data reception method according to embodiments may comprise the steps of: receiving point cloud data; decoding the point cloud data; and rendering the point cloud data.

Description

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

The embodiments are directed 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 media consisting of three dimensions, and provides various services such as VR (Virtual Reality, Virtual Reality), AR (Augmented Reality, Augmented Reality), MR (Mixed Reality, Mixed Reality), and autonomous driving services. Used to provide. However, tens of thousands to hundreds of thousands of point data are required to represent point cloud content. Therefore, a method for efficiently processing a vast amount of point data is required.

The 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 problem, and the scope of the 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 above object and other advantages, a method for transmitting point cloud data according to embodiments includes: obtaining point cloud data; Encoding the point cloud data; And/or transmitting point cloud data; It may include.

In addition, a method of receiving point cloud data according to embodiments includes: receiving point cloud data; Decoding the point cloud data; And rendering the point cloud data. It may include.

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 point cloud service of high quality.

The apparatus and method according to the embodiments may provide point cloud content for providing general-purpose services such as VR services and autonomous driving services.

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

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

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

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

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

5 shows 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 device according to embodiments.

13 is an example of a reception device according to embodiments.

14 illustrates an architecture for G-PCC-based point cloud content streaming according to embodiments.

15 shows an example of a transmission device according to embodiments.

16 shows an example of a receiving device according to embodiments.

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

18 shows an example of a space of points constituting point cloud content according to embodiments.

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

20 shows an example of a geometric information encoder according to embodiments.

21 illustrates a bounding box of geometric information of point cloud data according to embodiments.

22 shows an example of an operation of a space dividing unit according to embodiments.

23 is a diagram illustrating an example of performing octtree division information and/or derivation information of geometric information of point cloud data according to embodiments.

24 shows an example of a structure of point cloud data according to embodiments.

25 shows an example of a process of inverse quantization of geometric information according to embodiments.

26 illustrates an exemplary embodiment of inverse quantization of geometric information according to embodiments.

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

28 illustrates an example of a geometric information decoding unit according to embodiments.

29 shows an example of occupancy derivation according to embodiments.

30 shows signaling information related to a geometry node according to embodiments.

31 illustrates a method of transmitting point cloud data according to embodiments.

32 illustrates a method of receiving point cloud data according to embodiments.

The preferred embodiments of the embodiments will be described in detail, examples of which are shown in the accompanying drawings. The detailed description below with reference to the accompanying drawings is intended to describe preferred embodiments of the embodiments, rather than showing only embodiments that can 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 is obvious to a person 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. Accordingly, the embodiments should be understood based on the intended meaning of the term, not the simple name or meaning of the term.

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

. The transmission device 10000 according to the embodiments may secure, process, and transmit point cloud video (or point cloud content). According to embodiments, the transmission device 10000 is a fixed station, a base transceiver system (BTS), a network, an artificial intelligence (AI) device and/or system, a robot, an AR/VR/XR device and/or 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, Robots, vehicles, AR/VR/XR devices, portable devices, home appliances, Internet of Thing (IoT) devices, AI devices/servers, etc. may be included.

The transmission device 10000 according to embodiments includes 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). Include)

The point cloud video acquisition unit 10001 according to the embodiments acquires a point cloud video through a process such as capture, synthesis, or generation. The point cloud video is point cloud content expressed as a point cloud, which is a set of points located in a three-dimensional space, and may be referred to as point cloud video data. 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 secured 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, 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 the embodiments transmits a bitstream including encoded point cloud video data. The bitstream according to the embodiments is encapsulated into a file or segment (for example, 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, a file or segment may be transmitted to the receiving device 10004 through a network or stored in a digital storage medium (eg, USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.). The transmitter 10003 according to the embodiments may perform wired/wireless communication with the reception device 10004 (or a receiver 10005) through a network such as 4G, 5G, or 6G. In addition, the transmitter 10003 may perform necessary data processing operations according to a network system (for example, a communication network system such as 4G, 5G, or 6G). In addition, the transmission device 10000 may transmit encapsulated data according to an on demand method.

The reception device 10004 according to the embodiments includes a receiver 10005, a point cloud video decoder 10006, and/or a renderer 10007. According to embodiments, the receiving device 10004 uses a wireless access technology (eg, 5G NR (New RAT), LTE (Long Term Evolution)) to communicate with a base station and/or other wireless devices, a robot , Vehicles, AR/VR/XR devices, portable devices, home appliances, Internet of Thing (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 necessary data processing operations according to a network system (for example, a communication network system such as 4G, 5G, or 6G). The receiver 10005 according to the embodiments may decapsulate the received file/segment and output a bitstream. In addition, according to embodiments, the receiver 10005 may include a decapsulation unit (or a decapsulation module) for performing a decapsulation operation. In addition, 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 the encoding method (for example, 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 point cloud content. According to embodiments, the display is not 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 feedback information acquired by the receiving device 10004. The feedback information is information for reflecting an interaction ratio with a user who consumes point cloud content, and includes user information (eg, head orientation information, viewport information, etc.). In particular, when the point cloud content is content for a service that requires interaction with a user (for example, an autonomous driving service, etc.), the feedback information is the content sending side (for example, the transmission device 10000) and/or a service provider. Can be delivered to According to embodiments, the feedback information may be used not only in the transmitting device 10000 but also in the receiving device 10004, and may not be provided.

Head orientation information according to embodiments is information on a position, direction, angle, and movement of a user's head. The receiving device 10004 according to the embodiments may calculate viewport information based on the head orientation information. The viewport information is information on the area of the point cloud video that the user is viewing. A viewpoint is a point at which the user is watching a point cloud video, and may mean a center point of a 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 receiving 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 performs a gaze analysis and the like to check the point cloud consumption method of the user, the point cloud video area that the user gazes, and the gaze time. According to embodiments, the receiving device 10004 may transmit feedback information including the result of 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 feedback information secured by the renderer 10007. The point cloud content providing system may process (encode/decode) point cloud data based on 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 feedback information. Therefore, the point cloud content providing system does not process (encode/decode) all point cloud data, but efficiently processes necessary data (e.g., point cloud data corresponding to the user's head position) based on feedback information. Point cloud content can be provided to users.

According to embodiments, the transmission device 10000 may be referred to as an encoder, a transmission device, a transmitter, and the like, and the reception device 10004 may be referred to as a decoder, a reception 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. I 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.

Elements of the point cloud content providing system shown in FIG. 1 may be implemented by hardware, software, 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).

A point cloud content providing system according to embodiments (for example, the point cloud transmission apparatus 10000 or the point cloud video acquisition unit 10001) may acquire a point cloud video (20000). The point cloud video is expressed as a point cloud belonging to a coordinate system representing a three-dimensional space. A point cloud video according to embodiments may include a Ply (Polygon File format or the Stanford Triangle format) file. When the point cloud video has one or more frames, the acquired point cloud video may include one or more Ply files. Ply files contain point cloud data such as the geometry and/or attributes of the point. The geometry includes the positions of the 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 composed of XYZ axes). Attributes include 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 attributes). For example, one point may have an attribute of one color, or two attributes of a color and reflectance. Depending on embodiments, geometry may be referred to as positions, geometry information, geometry data, and the like, and attributes 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) provides points from information related to the acquisition process of the point cloud video (eg, depth information, color information, etc.). Cloud data can be secured.

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

The point cloud content providing system according to embodiments (for example, the transmission device 10000 or the transmitter 10003) may transmit encoded point cloud data (20002). As described in FIG. 1, the encoded point cloud data may be expressed as a geometry bitstream and an attribute bitstream. In addition, 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 according to the embodiments (for example, the receiving device 10004 or the receiver 10005) may receive a bitstream including encoded point cloud data. In addition, the point cloud content providing system (for example, the receiving device 10004 or the receiver 10005) may demultiplex the bitstream.

The point cloud content providing system (e.g., the receiving device 10004 or the point cloud video decoder 10005) can decode the encoded point cloud data (e.g., geometry bitstream, 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) can 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 (for example, the receiving device 10004 or the point cloud video decoder 10005) may restore positions (geometry) of points by decoding a geometry bitstream. The point cloud content providing system may restore the attributes of points by decoding an attribute bitstream based on the restored geometry. The point cloud content providing system (for example, the receiving device 10004 or the point cloud video decoder 10005) may restore the point cloud video based on the decoded attributes and positions according to the restored geometry.

The point cloud content providing system according to the embodiments (for example, the receiving device 10004 or the renderer 10007) 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 geometry and attributes decoded through a decoding process according to a rendering method according to various rendering methods. Points of the point cloud content may be rendered as a vertex having a certain thickness, a cube having a specific minimum size centered on the vertex position, or a circle centered on 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 according to the embodiments (for example, the receiving device 10004) may secure feedback information (20005). The point cloud content providing system may encode and/or decode point cloud data based on feedback information. Since the operation of the system for providing feedback information and point cloud content according to the embodiments is the same as the feedback information and operation described in FIG. 1, a detailed description will be omitted.

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

The point cloud content is an object located in various three-dimensional spaces (for example, a three-dimensional space representing a real environment, a three-dimensional space representing a virtual environment, etc.) and/or a point cloud video (images and/or Videos). Therefore, the point cloud content providing system according to the embodiments includes one or more cameras (eg, an infrared camera capable of securing depth information, color information corresponding to the depth information) to generate the point cloud content. You can capture a point cloud video using an RGB camera that can extract the image), a projector (for example, an infrared pattern projector to secure depth information), and LiDAR. The point cloud content providing system according to the embodiments may obtain point cloud data by extracting a shape of a geometry composed of points in a 3D space from depth information, and extracting an attribute of each point from color information. An image and/or an image according to the embodiments may be captured based on at least one or more of an inward-facing method and an outward-facing method.

The left side of Fig. 3 shows an inword-facing scheme. The inword-facing method refers to a method in which one or more cameras (or camera sensors) located surrounding a central object capture a central object. The in-word-facing method provides point cloud content that provides users with 360-degree images of key objects (e.g., provides users with 360-degree images of objects (e.g., key objects such as characters, players, objects, actors, etc.) VR/AR content).

The right side of Fig. 3 shows the outword-pacing scheme. The outward-facing method refers to a method in which one or more cameras (or camera sensors) located surrounding the central object capture the environment of the central object other than the central object. The outward-pacing method may be used to generate point cloud content (for example, content representing an external environment that may be provided to a user of a self-driving vehicle) to provide an environment that appears from a user's point of view.

As shown in the figure, the point cloud content may be generated based on the 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 calibrate one or more cameras to set a global coordinate system before the capture operation. In addition, the point cloud content providing system may generate point cloud content by synthesizing an image and/or image captured by the above-described capture method with an arbitrary image and/or image. In addition, the point cloud content providing system may not perform the capture operation described in FIG. 3 when generating point cloud content representing a virtual space. The point cloud content providing system according to embodiments may perform post-processing on the captured image and/or image. In other words, the point cloud content providing system removes an unwanted area (e.g., background), recognizes the space where captured images and/or images are connected, and performs an operation to fill in a spatial hole if there is. I 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 acquired from each camera. The point cloud content providing system may perform a coordinate system transformation of points based on the position coordinates of each camera. Accordingly, the point cloud content providing system may generate content representing a wide range, or 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 uses point cloud data (for example, positions and/or positions of points) to adjust the quality of the point cloud content (for example, lossless-lossless, loss-lossy, near-lossless) according to network conditions or applications. Attributes) and perform an encoding operation. When the total size of the point cloud content is large (for example, a 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 content in real time. Therefore, the point cloud content providing system can reconstruct the point cloud content based on the maximum target bitrate in order to provide it according to the network environment.

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

Point cloud encoders according to embodiments include a coordinate system transform unit (Transformation Coordinates, 40000), a quantization unit (Quantize and Remove Points (Voxelize), 40001), an octree analysis unit (Analyze Octree, 40002), and a surface aproximation analysis unit ( Analyze Surface Approximation, 40003), Arithmetic Encode (40004), Reconstruct Geometry (40005), Transform Colors (40006), Transfer Attributes (40007), RAHT Transformation A unit 40008, an LOD generation unit (Generated LOD) 40009, a lifting transform unit (Lifting) 40010, a coefficient quantization unit (Quantize Coefficients, 40011), and/or an Arithmetic Encode (40012).

The coordinate system transform unit 40000, the quantization unit 40001, the octree analysis unit 40002, the surface aproximation 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 trisoup 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 positions and converts them into a coordinate system. For example, positions may be converted into position information in a three-dimensional space (eg, a three-dimensional space represented by an XYZ coordinate system). The location information of the 3D space according to embodiments may be referred to as geometry information.

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

The octree analysis unit 40002 according to the 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 an octal tree structure.

The surface aproxiation analysis unit 40003 according to the embodiments may analyze and approximate the octree. The octree analysis and approximation according to the 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. As a result of encoding, a geometry bitstream is generated.

Color conversion unit 40006, attribute conversion unit 40007, RAHT conversion unit 40008, LOD generation unit 40009, lifting conversion unit 40010, coefficient quantization unit 40011 and/or Arismatic encoder 40012 Performs attribute encoding. As described above, one point may have one or more attributes. Attribute encoding according to embodiments is applied equally 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 includes color transform coding, attribute transform coding, Region Adaptive Hierarchial 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)) coding may be included. Depending on the point cloud content, the aforementioned RAHT coding, predictive transform coding, and lifting transform coding may be selectively used, or a combination of one or more codings may be used. In addition, attribute encoding according to embodiments is not limited to the above-described example.

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

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

The attribute conversion unit 40007 is an average value of attributes or attribute values (for example, the color of each point or reflectance) of points neighboring within a specific position/radius from the position (or position value) of the center point of each voxel. Attribute conversion can be performed by calculating. The attribute conversion unit 40007 may apply a weight according to a distance from a central point to each point when calculating an average value. Thus, each voxel has a position and a calculated 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 a K-D tree or a Molton code. The K-D tree is a binary search tree and supports a data structure that can manage points based on location so that the Nearest Neighbor Search (NNS) can be quickly performed. The Molton code represents a coordinate value (for example, (x, y, z)) representing 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 values are mixed according to the bit index in the order of z, y, x, it is 010001000111. If this value is expressed as a decimal number, it becomes 1095. That is, the Molton code value of the point whose coordinate value is (5, 9, 1) is 1095. The attribute conversion unit 40007 may sort points based on a Morton code value and perform a shortest neighbor search (NNS) through a depth-first traversal process. After the attribute transformation operation, when the shortest 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 conversion unit 40008 according to embodiments performs RAHT coding for predicting attribute information based on the reconstructed geometry information. For example, the RAHT conversion unit 40008 may predict attribute information of a node at a higher level of the octree based on attribute information associated with a node at a lower level of the octree.

The LOD generation unit 40009 according to embodiments generates a level of detail (LOD) to perform predictive transform coding. The LOD according to the embodiments is a degree representing the detail of the point cloud content, and a smaller LOD value indicates that the detail of the point cloud content decreases, and a larger LOD value indicates that the detail of the point cloud content is high. Points can be classified according to LOD.

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

The coefficient quantization unit 40011 according to embodiments quantizes attribute-coded attributes based on coefficients.

Arismatic encoder 40012 according to embodiments encodes quantized attributes based on Arismatic coding.

The elements of the point cloud encoder of FIG. 4 are not shown in the drawing, but hardware including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing apparatus. , Software, firmware, or a combination thereof. One or more processors may perform at least one or more of the operations and/or functions of the elements of the point cloud encoder of FIG. 4 described above. Further, 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 according to embodiments may include high speed random access memory, and non-volatile memory (e.g., one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid state Memory devices (solid-state memory devices, etc.).

5 shows an example of a voxel according to embodiments.

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

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

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

The upper part of FIG. 6 shows an octree structure. The three-dimensional space of the point cloud content according to the embodiments is expressed by axes of a coordinate system (eg, X-axis, Y-axis, Z-axis). The octree structure is created by recursive subdividing of a cubical axis-aligned bounding box defined by two poles (0,0,0) and (2 ^d , 2 ^d , 2 ^d ). . 2d may be set to a value constituting the smallest bounding box surrounding all points of the point cloud content (or point cloud video). d represents the depth of the octree. The d value 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 portion of FIG. 6, the entire 3D space may be divided into eight spaces according to the division. Each divided space is represented by a cube with 6 faces. As shown on the right side of the upper part of FIG. 6, each of the eight spaces is divided again based on the axes of the coordinate system (eg, X axis, Y axis, Z axis). Thus, each space is further divided into eight smaller spaces. The divided small space is also represented as a cube with 6 faces. This division method is applied until a leaf node of an octree becomes a voxel.

The lower part of FIG. 6 shows the octree's ocupancy code. The octree's ocupancy code is generated to indicate whether each of the eight divided spaces generated by dividing one space includes at least one point. Therefore, one Okufanshi code is represented by 8 child nodes. Each child node represents the occupancy of the divided space, and the child node has a value of 1 bit. Therefore, the Ocufanshi 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 node has a value of 1. If the point is not included in the space corresponding to the child node (empty), the node has a value of 0. Since the ocupancy code shown in FIG. 6 is 00100001, it indicates that the 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 represented by an 8-bit ocupancy code. The drawing shows that the occupancy code of the third child node is 10000111, and the ocupancy code of the 8th child node is 01001111. A point cloud encoder (for example, the Arismatic encoder 40004) according to embodiments may entropy encode an ocupancy code. In addition, in order to increase the compression efficiency, the point cloud encoder can intra/inter code the ocupancy code. The reception device (for example, the reception device 10004 or the point cloud video decoder 10006) according to the embodiments reconstructs an octree based on an ocupancy code.

A point cloud encoder according to embodiments (for example, the point cloud encoder of FIG. 4 or the octree analyzer 40002) may perform voxelization and octree coding to store positions of points. However, since 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 over the entire 3D space. For example, if there are almost no points in a specific area, it is not necessary to perform voxelization to the corresponding area.

Therefore, the point cloud encoder according to the embodiments does not perform voxelization for the above-described specific region (or nodes other than the leaf nodes of the octree), but directly codes the positions of points included in the specific region. ) Can be performed. Coordinates of a direct coding point according to embodiments are referred to as a direct coding mode (DCM). In addition, the point cloud encoder according to embodiments may perform trisoup geometry encoding in which positions of points within a specific region (or node) are reconstructed based on voxels based on a surface model. Trisoup geometry encoding is a geometry encoding that expresses the representation of an object as a series of triangle meshes. Therefore, the point cloud decoder can generate a point cloud from the mesh surface. Direct coding and trisoup geometry encoding according to embodiments may be selectively performed. In addition, direct coding and trisoup 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 direct mode to apply direct coding must be activated, and the node to which direct coding is applied is not a leaf node, but below the threshold within a specific node. There must be points of. In addition, the number of all points subject to direct coding must not exceed a preset limit. When the above condition is satisfied, the point cloud encoder (or the arithmetic encoder 40004) according to the embodiments may entropy-code the positions (or position values) of the points.

The point cloud encoder according to the embodiments (for example, the surface aproximation analysis unit 40003) determines a specific level of the octree (if the level is less than the depth d of the octree), and from that level, the node Trisoup geometry encoding that reconstructs the position of a point in the region based on voxels can be performed (tri-soup mode). A point cloud encoder according to embodiments may designate a level to which trisoup geometry encoding is applied. For example, if the specified level is equal to the depth of the octree, the point cloud encoder does not operate in the try-soup mode. That is, the point cloud encoder according to the embodiments may operate in the try-soup mode only when the specified level is less than the depth value of the octree. A three-dimensional cube area of nodes of a designated level according to the embodiments is referred to as a block. One block may include one or more voxels. The block or voxel may correspond to a brick. Within each block, the geometry is represented by a surface. The surface according to embodiments may intersect each edge (edge) of the block at most once.

Since one block has 12 edges, there are at least 12 intersection points within 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 occupied voxel according to embodiments refers to a voxel including a point. The position of the vertex detected along the edge is the average position along the edge of all voxels among all blocks sharing the edge.

When a vertex is detected, the point cloud encoder according to the embodiments entropy-codes the starting point of the edge (x, y, z), the direction vector of the edge (Δx, Δy, Δz), and vertex position values (relative position values within the edge). I can. When trisoup geometry encoding is applied, the point cloud encoder (e.g., the geometry reconstruction unit 40005) according to embodiments performs a triangle reconstruction, up-sampling, and voxelization process. By doing so, you can create reconstructed geometry (reconstructed geometry).

The vertices located at the edge of the block determine the surface that passes through the block. The surface according to the embodiments is a non-planar polygon. The triangle reconstruction process reconstructs the surface represented by a triangle based on the starting point of the edge, the direction vector of the edge, and the position value of the vertex. The triangle reconstruction process is as follows. ① Calculate the centroid value of each vertex, ② calculate the squared values of the values subtracted from each vertex value by subtracting the center value, and calculate the sum of all the values.

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

The upsampling process is performed to voxelize by adding points in the middle along the edge of the triangle. Additional points are created based on the upsampling factor and the width of the block. The additional point is called a refined vertice. The point cloud encoder according to embodiments may voxelize 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.

As described in FIGS. 1 to 6, a point cloud content providing system or a point cloud encoder (for example, a point cloud video encoder 10002, a point cloud encoder or an Arismatic encoder 40004 of FIG. 4) directly converts the Ocufanshi 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 ocupancy code of the current node and the ocupancy of neighboring nodes, or entropy encoding (inter encoding) based on the ocupancy code of the previous frame. ) Can be performed. A frame according to embodiments means a set of point cloud videos generated at the same time. The compression efficiency of intra-encoding/inter-encoding according to embodiments may vary depending on the number of referenced neighbor nodes. The larger the bit, the more complicated it is, but it can be skewed to one side, increasing the compression efficiency. For example, if you have a 3-bit context, you have to code in 8 ways. The divided coding part affects the complexity of the implementation. Therefore, it is necessary to match the appropriate level of compression efficiency and complexity.

7 shows a process of obtaining an ocupancy pattern based on the ocupancy of neighboring nodes. A point cloud encoder according to embodiments determines occupancy of neighboring nodes of each node of an octree and obtains a value of a neighbor pattern. The neighboring node pattern is used to infer the occupancy pattern of the corresponding node. The left side of FIG. 7 shows a cube corresponding to a node (centered cube) and six cubes (neighbor nodes) that share at least one surface with the cube. Nodes shown in the figure are nodes of the same depth (depth). Numbers shown in the figure indicate 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 neighboring node pattern values. The neighbor node pattern value is the sum of values multiplied by weights of the occupied neighbor nodes (neighbor nodes having points). Therefore, the neighbor node pattern value has a value from 0 to 63. When the neighbor node pattern value is 0, it indicates that no node (occupied node) has a point among neighboring nodes of the corresponding node. If the neighboring node pattern value is 63, it indicates that all neighboring nodes are occupied nodes. As shown in the figure, since neighboring nodes to which

weights

1, 2, 4, and 8 are assigned are occupied 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 neighboring node pattern value (for example, if the neighboring node pattern value is 63, 64 codings are performed). According to embodiments, the point cloud encoder may reduce coding complexity by changing a neighbor node pattern value (for example, based on a table changing 64 to 10 or 6).

As described with reference to FIGS. 1 through 7, the encoded geometry is reconstructed (decompressed) before attribute encoding is performed. When direct coding is applied, the geometry reconstruction operation may include changing the placement of the direct coded points (eg, placing the direct coded points in front of the point cloud data). When trisoup geometry encoding is applied, the geometry reconstruction process is triangular reconstruction, upsampling, voxelization, and the attribute is dependent on geometry, so the attribute encoding is performed based on the reconstructed geometry.

The point cloud encoder (for example, the LOD generator 40009) may reorganize points for each LOD. The figure shows 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 the lowest LOD points, and the rightmost figure in the figure shows the distribution of the highest LOD points. That is, the points of the lowest LOD are sparsely distributed, and the points of the highest LOD are densely distributed. That is, as the LOD increases according to the direction of the arrow indicated at the bottom of the drawing, the spacing (or distance) between points becomes shorter.

As described in FIGS. 1 to 8, a point cloud content providing system or a point cloud encoder (for example, a point cloud video encoder 10002, a point cloud encoder in FIG. 4, or an LOD generator 40009) generates an LOD. can do. The LOD is generated by reorganizing the points into a set of refinement levels according to a set LOD distance value (or a set of Euclidean distance). The LOD generation process is performed in the point cloud decoder as well as the point cloud encoder.

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 represents the order of points P0 to P9 before LOD generation. The LOD based order of FIG. 9 represents the order of points according to 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 embodiments may selectively or combine predictive transform coding, lifting transform coding, and RAHT transform coding.

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

The predicted attribute (or attribute value) according to the embodiments is a weight calculated based on the distance to each neighboring point to the attributes (or attribute values, for example, color, reflectance, etc.) of neighboring points set in the predictor of each point. It is set as the average value multiplied by (or weight value). A point cloud encoder according to embodiments (e.g., the coefficient quantization unit 40011) subtracts a predicted attribute (attribute value) from an attribute (attribute value) of each point, residuals (residuals, residual attributes, residual attribute values, attributes) It can be called a prediction residual value, etc.) can be quantized and inverse quantized The quantization process is as 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 (for example, the arithmetic encoder 40012) according to the embodiments may entropy-code the quantized and dequantized residual values as described above when there are points adjacent to the predictors of each point. The point cloud encoder according to embodiments (for example, the arithmetic encoder 40012) may entropy-code attributes of the corresponding point without performing the above-described process if there are no points adjacent to 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 calculated LOD to the predictor, registers neighboring points, and increases 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 above-described predictive transform coding, but differs in that a weight is accumulated and applied to an attribute value. A process of cumulatively applying a weight to an attribute value according to embodiments is as follows.

1) Create an array QW (Quantization Wieght) that stores the weight value of each point. The initial value of all elements of QW is 1.0. The QW value of the predictor index of the neighboring node registered in the predictor is multiplied by the weight of the predictor of the current point.

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

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

4) The weights calculated by additionally multiplying the weights calculated for all predictors by the weights stored in the QW corresponding to the predictor indexes are cumulatively added to the update weight array by the indexes of neighboring nodes. In the update array, the value obtained by multiplying the calculated weight by the attribute value of the index of the neighboring 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 to the divided value.

6) For all predictors, the predicted attribute value is calculated by additionally multiplying the attribute value updated through the lift update process by the weight updated through the lift prediction process (stored in QW). A point cloud encoder (for example, the coefficient quantization unit 40011) according to embodiments quantizes a predicted attribute value. In addition, the point cloud encoder (for example, the Arismatic 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 that estimates the attributes of higher-level nodes 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 through octree backward scan. The point cloud encoder according to the embodiments scans from voxels to the entire area, and repeats the merging process up to the root node while merging the voxels into larger blocks in each step. The merging process according to the embodiments is performed only for an occupied node. The merging process is not performed for the empty node, and the merging process is performed for the node immediately above the empty node.

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

The point cloud decoder illustrated in FIG. 10 is an example of the point cloud video decoder 10006 described in FIG. 1, and may perform the same or similar operation as that 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 performs geometry decoding on the geometry bitstream and outputs decoded geometry. The attribute decoder outputs decoded attributes by performing attribute decoding on the basis of the decoded geometry and the attribute bitstream. The decoded geometry and decoded attributes are used to reconstruct the point cloud content.

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

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

As described in FIGS. 1 and 10, the point cloud decoder may perform geometry decoding and attribute decoding. Geometry decoding is performed prior to attribute decoding.

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

The arithmetic decoder 11000, the octree synthesis unit 11001, the surface opoxidation synthesis unit 11002, the geometry reconstruction unit 11003, and the coordinate system inverse transform unit 11004 may perform geometry decoding. Geometry decoding according to embodiments may include direct coding and trisoup geometry decoding. Direct coding and trisoup geometry decoding are optionally applied. Further, the geometry decoding is not limited to the above example, and is performed in the reverse process of the geometry encoding described in FIGS. 1 to 9.

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

The octree synthesizer 11001 according to the embodiments may generate an octree by obtaining an ocupancy code from a decoded geometry bitstream (or information on a geometry obtained as a result of decoding). A detailed description of the OQFancy code is as described in FIGS. 1 to 9.

When trisoup geometry encoding is applied, the surface opoxidation synthesizer 11002 according to embodiments may synthesize a surface based on the decoded geometry and/or the generated octree.

The geometry reconstruction unit 11003 according to the embodiments may regenerate the geometry based on the surface and/or the decoded geometry. 1 to 9, direct coding and trisoup geometry encoding are selectively applied. Accordingly, the geometry reconstruction unit 11003 directly imports and adds position information of points to which direct coding is applied. In addition, when trisoup geometry encoding is applied, the geometry reconstruction unit 11003 performs a reconstruction operation of the geometry reconstruction unit 40005, such as triangle reconstruction, up-sampling, and voxelization, to restore the geometry. have. Detailed contents are the same as those described in FIG. 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 acquire positions of points by transforming a coordinate system based on the restored geometry.

Arithmetic decoder 11005, inverse quantization unit 11006, RAHT conversion unit 11007, LOD generation unit 11008, inverse lifting unit 11009, and/or color inverse conversion unit 11010 are attributes described in FIG. Decoding can be performed. Attribute decoding according to embodiments includes Region Adaptive Hierarchial 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. In addition, attribute decoding according to embodiments is not limited to the above-described example.

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

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

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

The inverse color transform unit 11010 according to embodiments performs inverse transform coding for inverse transforming a color value (or texture) included in the 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 elements of the point cloud decoder of FIG. 11 are not shown in the drawing, hardware including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing apparatus , Software, firmware, or a combination thereof. One or more processors may perform at least one or more of the operations and/or functions of the elements of the point cloud decoder of FIG. 11 described above. Further, one or more processors may operate or execute a set of software programs and/or instructions for performing operations and/or functions of elements of the point cloud decoder of FIG. 11.

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

The transmission device shown in FIG. 12 is an example of the transmission device 10000 of FIG. 1 (or a point cloud encoder of FIG. 4 ). The transmission device illustrated in FIG. 12 may perform at least one or more of the same or similar operations and methods as the operations and encoding methods of the point cloud encoder described in 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 octree occupancy code generation unit 12003, a surface model processing unit 12004, an intra/ Inter-coding processing unit (12005), Arithmetic coder (12006), metadata processing unit (12007), color conversion processing unit (12008), attribute transformation processing unit (or attribute transformation processing unit) (12009), prediction/lifting/RAHT transformation A processing unit 12010, an Arithmetic coder 12011, and/or a transmission processing unit 12012 may be included.

The data input unit 12000 according to the embodiments receives or acquires point cloud data. The data input unit 12000 may perform the same or similar operation and/or an 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. The geometry encoding according to the embodiments is the same as or similar to the geometry encoding described in FIGS. 1 to 9, so a detailed description thereof will be omitted.

The quantization processing unit 12001 according to embodiments quantizes geometry (eg, a position value or position value of points). The operation and/or quantization of the quantization processor 12001 is the same as or similar to the operation and/or quantization of the quantization unit 40001 described in FIG. 4. Detailed descriptions are the same as those described in FIGS. 1 to 9.

The voxelization processor 12002 according to embodiments voxelsizes the position values of the quantized points. The voxelization processor 120002 may perform the same or similar operation and/or process as the operation and/or the voxelization process of the quantization unit 40001 described in FIG. 4. Detailed descriptions are the same as those described in FIGS. 1 to 9.

The octree occupancy code generation unit 12003 according to embodiments performs octree coding on positions of voxelized points based on an octree structure. The octree ocupancy code generation unit 12003 may generate an ocupancy code. The octree occupancy code generation unit 12003 may perform the same or similar operation and/or method as the operation and/or method of the point cloud encoder (or octree analysis unit 40002) described in FIGS. 4 and 6. Detailed descriptions are the same as those described in FIGS. 1 to 9.

The surface model processing unit 12004 according to embodiments may perform trisoup geometry encoding to reconstruct positions of points within a specific area (or node) based on a voxel based on a surface model. The face model processing unit 12004 may perform the same or similar operation and/or method as the operation and/or method of the point cloud encoder (eg, the surface aproxiation analysis unit 40003) described in FIG. 4. Detailed descriptions are the same as those described in FIGS. 1 to 9.

The intra/inter coding processor 12005 according to embodiments may intra/inter code point cloud data. The intra/inter coding processing unit 12005 may perform the same or similar coding as the intra/inter coding described in FIG. 7. The detailed description is the same as described in FIG. 7. According to 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 operation and/or method to the operation and/or method of the arithmetic encoder 40004.

The metadata processing unit 12007 according to embodiments processes metadata related to point cloud data, for example, a set value, and provides it to a necessary processing such as geometry encoding and/or attribute encoding. In addition, 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. In addition, 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. Attribute encoding according to embodiments is the same as or similar to the attribute encoding described in FIGS. 1 to 9, and thus a detailed description thereof will be omitted.

The color conversion processing unit 12008 according to embodiments performs color conversion coding that converts color values included in attributes. The color conversion processing unit 12008 may perform color conversion coding based on the reconstructed geometry. Description of the reconstructed geometry is the same as described in 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 in FIG. 4 is performed. Detailed description will be omitted.

The attribute conversion processing unit 12009 according to embodiments performs attribute conversion for converting attributes based on the reconstructed geometry and/or positions for which geometry encoding has not been performed. The attribute conversion processing unit 12009 performs the same or similar operation and/or method to the operation and/or method of the attribute conversion unit 40007 described in FIG. 4. Detailed description will be omitted. The prediction/lifting/RAHT transform processing unit 12010 according to embodiments may code transformed attributes by using any one or a combination of RAHT coding, predictive transform coding, and lifting transform coding. The prediction/lifting/RAHT conversion processing unit 12010 performs at least one of the same or similar operations as the RAHT conversion unit 40008, LOD generation unit 40009, and lifting conversion unit 40010 described in FIG. 4. do. In addition, descriptions of predictive transform coding, lifting transform coding, and RAHT transform coding are the same as those described in FIGS.

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

The transmission processor 12012 according to the embodiments transmits each bitstream including the encoded geometry and/or the encoded attribute, and metadata information, or transmits the encoded geometry and/or the encoded attribute, and the metadata information in one piece. It can be configured as a bitstream and transmitted. When the encoded geometry and/or encoded attribute and metadata information according to the embodiments are configured as one bitstream, the bitstream may include one or more sub-bitstreams. The bitstream according to the embodiments is a sequence parameter set (SPS) for signaling of a sequence level, a geometry parameter set (GPS) for signaling of geometry information coding, an attribute parameter set (APS) for signaling of attribute information coding, and a tile. It may include signaling information including TPS (Tile Parameter Set) for level signaling and slice data. Slice data may include information on one or more slices. One slice according to embodiments may include one geometry bitstream (Geom0 ⁰ ) and one or more attribute bitstreams (Attr0 ⁰ and Attr1 ⁰ ). The TPS according to the embodiments may include information about each tile (eg, coordinate value information and height/size information of a bounding box) with respect to one or more tiles. The geometry bitstream may include a header and a payload. The header of the geometry bitstream according to embodiments may include identification information of a parameter set included in GPS (geom_ parameter_set_id), a tile identifier (geom_tile_id), a slice identifier (geom_slice_id), and information about data included in the payload. I can. As described above, the metadata processing unit 12007 according to the embodiments may generate and/or process signaling information and transmit the generated and/or processed signaling information to the transmission processing unit 12012. Depending on embodiments, elements that perform geometry encoding and elements that perform attribute encoding may share data/information with each other as dotted line processing. The transmission processing unit 12012 according to the embodiments may perform the same or similar operation and/or transmission method as the operation and/or transmission method of the transmitter 10003. Detailed descriptions are the same as those described in FIGS. 1 to 2 and thus will be omitted.

13 is an example of a reception device according to embodiments.

The receiving device illustrated 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 device illustrated in FIG. 13 may perform at least one or more of the same or similar operations and methods as the operations and decoding methods of the point cloud decoder described in FIGS. 1 to 11.

The receiving apparatus according to the embodiments includes a receiving unit 13000, a receiving processing unit 13001, an arithmetic decoder 13002, an octree reconstruction processing unit 13003 based on an Occupancy code, 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 A /lifting/RAHT inverse transformation processing unit 13009, a color inverse transformation processing unit 13010, and/or a renderer 13011 may be included. Each component of decoding according to the embodiments may perform a reverse process of the component of encoding according to the embodiments.

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

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

The arithmetic decoder 13002, the ocupancy 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 in FIGS. 1 to 10, a detailed description will be omitted.

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

The ocupancy code-based octree reconstruction processing unit 13003 according to the embodiments may obtain an ocupancy code from a decoded geometry bitstream (or information on a geometry obtained as a result of decoding) to reconstruct the octree. The ocupancy code-based octree reconstruction processing unit 13003 performs the same or similar operation and/or method as the operation and/or the octree generation method of the octree synthesis unit 11001. When the trisoup geometry encoding is applied, the surface model processing unit 13004 according to the embodiments decodes the trisoup geometry based on the surface model method and reconstructs the related geometry (eg, triangle reconstruction, up-sampling, voxelization). Can be done. The surface model processing unit 13004 performs an operation identical or similar to that of the surface opoxidation 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 metadata to geometry decoding and/or attribute decoding. The detailed description of the metadata is the same as that described in FIG. 12 and thus will be omitted.

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

The Arismatic decoder 13007 according to the embodiments may decode the attribute bitstream through Arismatic coding. The arithmetic decoder 13007 may perform decoding of the attribute bitstream based on the reconstructed geometry. The Arismatic decoder 13007 performs the same or similar operation and/or coding as the operation and/or coding of the Arismatic 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 operation and/or method as the operation and/or the inverse quantization method of the inverse quantization unit 11006.

The prediction/lifting/RAHT inverse transform processing unit 13009 according to embodiments may process reconstructed geometry and inverse quantized attributes. The prediction/lifting/RAHT inverse transform processing unit 13009 is the same or similar to the operations and/or decodings of the RAHT transform unit 11007, the LOD generator 11008 and/or the inverse lifting unit 11009, and/or At least one or more of the decodings is performed. The color inverse transform processing unit 13010 according to embodiments performs inverse transform coding for inverse transforming a color value (or texture) included in the decoded attributes. The color inverse transform processing unit 13010 performs the same or similar operation and/or inverse transform coding as 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 upper part of FIG. 14 shows a process in which the transmission device (for example, the transmission device 10000, the transmission device of FIG. 12, etc.) described in FIGS. 1 to 13 processes and transmits the point cloud content.

As described with reference to FIGS. 1 to 13, the transmission device may obtain audio Ba of the point cloud content (Audio Acquisition), encode the acquired audio, and output audio bitstreams Ea. In addition, the transmission device acquires a point cloud (Bv) (or point cloud video) of the point cloud content (Point Acqusition), performs point cloud encoding on the acquired point cloud, and performs a point cloud video bitstream ( Eb) can be output. The point cloud encoding of the transmission device is the same as or similar to the point cloud encoding (for example, the encoding of the point cloud encoder of FIG. 4) described in FIGS.

The transmission device may encapsulate the generated audio bitstreams and video bitstreams into files and/or segments (File/segment encapsulation). The encapsulated file and/or segment (Fs, File) may include a file of a file format such as ISOBMFF or a DASH segment. Point cloud-related metadata according to embodiments may be included in an encapsulated file format and/or segment. Meta data may be included in boxes of various levels in the ISOBMFF file format or may be included in separate tracks in the file. According to an embodiment, the transmission device may encapsulate the metadata itself as a separate file. The transmission device according to the embodiments may deliver the encapsulated file format and/or segment through a network. Since the encapsulation and transmission processing method of the transmission device is the same as those described in FIGS. 1 to 13 (for example, the transmitter 10003, the transmission step 20002 of FIG. 2, etc.), detailed descriptions are omitted.

The bottom of FIG. 14 shows a process of processing and outputting point cloud content by the receiving device (for example, the receiving device 10004, the receiving device of FIG. 13, etc.) described in FIGS. 1 to 13.

According to embodiments, the receiving device includes a device that outputs final audio data and final video data (e.g., loudspeakers, headphones, display), and a point cloud player that processes point cloud content ( Point Cloud Player). The final data output device and the point cloud player may be configured as separate physical devices. The point cloud player according to the embodiments may perform Geometry-based Point Cloud Compression (G-PCC) coding and/or Video based Point Cloud Compression (V-PCC) coding and/or next-generation coding.

The receiving device according to the embodiments secures a file and/or segment (F', Fs') included in the received data (for example, a broadcast signal, a signal transmitted through a network, etc.) and decapsulation (File/ segment decapsulation). Since the reception and decapsulation method of the reception device is the same as that described in FIGS. 1 to 13 (for example, the receiver 10005, the reception unit 13000, the reception processing unit 13001, etc.), a detailed description is omitted.

The receiving device according to the embodiments secures an audio bitstream E'a and a video bitstream E'v included in a file and/or segment. As shown in the drawing, the receiving device outputs the decoded audio data B'a by performing audio decoding on the audio bitstream, and rendering the decoded audio data to final audio data. (A'a) is output through speakers or headphones.

In addition, the receiving device outputs decoded video data B'v by performing point cloud decoding on the video bitstream E'v. Since the point cloud decoding according to the embodiments is the same as or similar to the point cloud decoding described in FIGS. 1 to 13 (for example, decoding of the point cloud decoder of FIG. 11 ), a detailed description will be omitted. The receiving device may render the decoded video data and output the final video data through the display.

The receiving device according to the embodiments may perform at least one of decapsulation, audio decoding, audio rendering, point cloud decoding, and rendering operations based on metadata transmitted together. The description of the metadata is the same as that described with reference to FIGS. 12 to 13 and thus will be omitted.

As shown in the dotted line shown in the drawing, the receiving device according to the embodiments (for example, a point cloud player or a sensing/tracking unit (sensing/tracking) in a point cloud flare) may generate feedback information (orientation, viewport). . Feedback information according to embodiments may be used in a decapsulation process, a point cloud decoding process and/or a rendering process of a receiving device, or may be transmitted to a transmitting device. The description of the feedback information is the same as that described with reference to FIGS. 1 to 13 and thus will be omitted.

15 shows an example of a transmission device according to embodiments.

The transmission device of FIG. 15 is a device that transmits point cloud content, and the transmission device described in FIGS. 1 to 14 (for example, the transmission device 10000 of FIG. 1, the point cloud encoder of FIG. 4, the transmission device of FIG. 12, 14). Accordingly, the transmission device of FIG. 15 performs the same or similar operation to that of the transmission device described in FIGS. 1 to 14.

The transmission device according to embodiments may perform at least one or more of point cloud acquisition, point cloud encoding, file/segment encapsulation, and delivery. Can be done.

Since the operation of acquiring and transmitting the point cloud illustrated in the drawing is the same as described in FIGS. 1 to 14, detailed descriptions will be omitted.

As described with reference to FIGS. 1 to 14, the transmission device according to embodiments may perform geometry encoding and attribute encoding. Geometry encoding according to embodiments may be referred to as geometry compression, and attribute encoding may be referred to as attribute compression. As described above, one point may have one geometry and one or more attributes. Therefore, the transmission device performs attribute encoding for each attribute. The drawing shows an example in which a transmission device has performed one or more attribute compressions (attribute #1 compression, ...attribute #N compression). In addition, the transmission apparatus according to the embodiments may perform auxiliary compression. Additional compression is performed on the metadata. Description of the meta data is the same as that described with reference to FIGS. 1 to 14 and thus will be omitted. In addition, the transmission device may perform mesh data compression. Mesh data compression according to embodiments may include the trisoup geometry encoding described in FIGS. 1 to 14.

The transmission device according to the embodiments may encapsulate bitstreams (eg, point cloud streams) output according to point cloud encoding into files and/or segments. According to embodiments, a transmission device performs media track encapsulation for carrying data other than metadata (for example, media data), and metadata tracak for carrying meta data. encapsulation) can be performed. According to embodiments, metadata may be encapsulated as a media track.

1 to 14, the transmitting device receives feedback information (orientation/viewport metadata) from the receiving device, and based on the received feedback information, at least one of point cloud encoding, file/segment encapsulation, and transmission operations. Any one or more can be performed. Detailed descriptions are the same as those described with reference to FIGS.

16 shows an example of a receiving device according to embodiments.

The receiving device of FIG. 16 is a device that receives point cloud content, and the receiving device described in FIGS. 1 to 14 (for example, the receiving device 10004 of FIG. 1, the point cloud decoder of FIG. 11, the receiving device of FIG. 13, 14). Accordingly, the receiving device of FIG. 16 performs the same or similar operation to that of the receiving device described in FIGS. 1 to 14. In addition, the receiving device of FIG. 16 may receive a signal transmitted from the transmitting device of FIG. 15, and may perform a reverse process of the operation of the transmitting device of FIG.

The receiving device according to embodiments may perform at least one or more of delivery, file/segement decapsulation, point cloud decoding, and point cloud rendering. Can be done.

Since the point cloud reception and point cloud rendering operations shown in the drawings are the same as those described in FIGS. 1 to 14, detailed descriptions are omitted.

As described with reference to FIGS. 1 to 14, the reception device according to the embodiments performs decapsulation on a file and/or segment acquired from a network or a storage device. According to embodiments, the receiving device performs media track decapsulation carrying data other than meta data (for example, media data), and metadata track decapsulation carrying meta data. decapsulation) can be performed. According to embodiments, when metadata is encapsulated into a media track, the metadata track decapsulation is omitted.

As described with reference to FIGS. 1 to 14, the receiving device may perform geometry decoding and attribute decoding on bitstreams (eg, point cloud streams) secured through decapsulation. Geometry decoding according to embodiments may be referred to as geometry decompression, and attribute decoding may be referred to as attribute decompression. As described above, a point may have one geometry and one or more attributes, and are each encoded. Therefore, the receiving device performs attribute decoding for each attribute. The drawing shows an example in which the receiving device performs one or more attribute decompressions (attribute #1 decompression, ...attribute #N decompression). In addition, the reception device according to the embodiments may perform auxiliary decompression. Additional decompression is performed on the metadata. The description of the meta data is the same as that described with reference to FIGS. 1 to 14 and thus will be omitted. Also, the receiving device may perform mesh data decompression. The mesh data decompression according to embodiments may include decoding the trisoup geometry described with reference to FIGS. 1 to 14. The reception device according to the embodiments may render the output point cloud data according to the point cloud decoding.

As described in FIGS. 1 to 14, the receiving device secures orientation/viewport metadata using a separate sensing/tracking element, etc., and transmits feedback information including the same to a transmission device (for example, the transmission device of FIG. 15). Can be transmitted. In addition, the receiving device may perform at least one or more of a receiving operation, file/segment decapsulation, and point cloud decoding based on the feedback information. Detailed descriptions are the same as those described with reference to FIGS.

The structure of FIG. 17 includes at least one of a server 1760, a robot 1710, an autonomous vehicle 1720, an XR device 1730, a smartphone 1740, a home appliance 1750, and/or an HMD 1770. A configuration connected to the cloud network 1710 is shown. The robot 1710, the autonomous vehicle 1720, the XR device 1730, the smartphone 1740, the home appliance 1750, and the like are referred to as devices. In addition, the XR device 1730 may correspond to a point cloud data (PCC) device according to embodiments or may be interlocked with a PCC device.

The cloud network 1700 may constitute a part of a cloud computing infrastructure or may mean a network that exists in the cloud computing infrastructure. Here, the cloud network 1700 may be configured using a 3G network, a 4G or long term evolution (LTE) network, or a 5G network.

The server 1760 includes at least one of a robot 1710, an autonomous vehicle 1720, an XR device 1730, a smartphone 1740, a home appliance 1750, and/or an HMD 1770, and a cloud network 1700. The connected devices 1710 to 1770 may be connected through, and may help at least part of the processing of the connected devices.

The HMD (Head-Mount Display) 1770 represents one of 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 1710 to 1750 to which the above-described technology is applied will be described. Here, the devices 1710 to 1750 shown in FIG. 17 may be interlocked/coupled with the point cloud data transmission/reception apparatus according to the above-described embodiments.

<PCC+XR>

The XR/PCC device 1730 is applied with PCC and/or XR (AR+VR) technology to provide a head-mount display (HMD), a head-up display (HUD) provided in a vehicle, a television, a mobile phone, a smart phone, It may be implemented as a computer, wearable device, home appliance, digital signage, vehicle, fixed robot or mobile robot.

The XR/PCC device 1730 analyzes 3D point cloud data or image data acquired through various sensors or from an external device to generate position data and attribute data for 3D points, thereby Information can be obtained, and the XR object to be output can be rendered and output. For example, the XR/PCC device 1730 may output an XR object including additional information on the recognized object in correspondence with the recognized object.

<PCC+Autonomous Driving+XR>

The autonomous vehicle 1720 may be implemented as a mobile robot, a vehicle, or an unmanned aerial vehicle by applying PCC technology and XR technology.

The autonomous driving vehicle 1720 to which the XR/PCC technology is applied may refer to an autonomous driving vehicle having a means for providing an XR image, an autonomous driving vehicle that is an object of control/interaction within the XR image. In particular, the autonomous vehicle 1720, which is the object of control/interaction in the XR image, is distinguished from the XR device 1730 and may be interlocked with each other.

The autonomous vehicle 1720 having a means for providing an XR/PCC image may acquire sensor information from sensors including a camera, and may output an XR/PCC image generated based on the acquired sensor information. For example, the autonomous vehicle 1720 may provide an XR/PCC object corresponding to a real object or an object in a 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 part of the XR/PCC object may be output to overlap the actual object facing the occupant's gaze. On the other hand, when the XR/PCC object is output on a display provided inside the autonomous vehicle, at least a part of the XR/PCC object may be output to overlap the object in the screen. For example, the autonomous vehicle 1220 may output XR/PCC objects corresponding to objects such as lanes, other vehicles, traffic lights, traffic signs, motorcycles, 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 can 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 virtually created CG image on a real object image. Furthermore, MR technology is similar to the AR technology described above in that virtual objects are mixed and combined in the real world. However, in AR technology, the distinction between real objects and virtual objects made from CG images is clear, and virtual objects are used in a form that complements the real objects, whereas in MR technology, the virtual objects are regarded as having the same characteristics as the real objects. It is distinct from technology. More specifically, for example, it is a hologram service to which the aforementioned MR technology is applied.

However, recently, VR, AR, and MR technologies are sometimes referred to as XR (extended reality) technology rather than clearly distinguishing between them. Therefore, embodiments of the present invention are applicable to all of VR, AR, MR, and XR technologies. This technology can be applied to encoding/decoding based on PCC, V-PCC, and G-PCC technology.

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

Vehicles providing autonomous driving service are connected to PCC devices to enable wired/wireless communication.

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

Point cloud data according to embodiments may represent a three-dimensional space based on XYZ coordinates in an encoding/decoding unit, for example, a unit of a cube.

The method/apparatus according to the embodiments may encode/decode point cloud data based on an encoding/decoding unit.

The encoding/decoding unit 18000 according to the embodiments represents a unit of space. The encoding/decoding unit may have a box shape of a cube based on 3D coordinates.

The sub-geometric information or geometric information 18001 according to the embodiments represents an information unit obtained by dividing an encoding/decoding unit. According to embodiments, such a box may be referred to as sub-geometric information and/or geometric information.

Point 18002 according to the embodiments represents a point representing point cloud data. Point represents point cloud content and represents geometric information.

The bounding box 18003 according to embodiments represents a box including sub geometric information and/or geometric information. It refers to a bounding box expressing a space in which points including geometric information are distributed. Depending on the embodiments, the bounding box may be a box that divides or includes a coding/decoding unit, and may include sub-geometric information or geometric information, or may refer to a box corresponding to sub-geometric information or geometric information.

The offset 18004 according to the embodiments represents a value representing the position of the sub-geometric information, the geometric information, and/or the bounding box on a three-dimensional space coordinate. The bounding box may be positioned at a position expressed based on x offset, y offset, and z offset based on the origin of the XYZ coordinate system.

The bounding box width, the bounding box height, and the bounding box depth 17005 according to the embodiments represent the width, height, and depth of the bounding box, respectively.

Point cloud data or point cloud content according to embodiments represents 3D point cloud data.

A method/apparatus according to embodiments represents a method/apparatus for transmitting and/or receiving point cloud data according to the embodiments.

The method/apparatus according to the embodiments relates to a method for improving compression of location information (geometric information) of points in Geometry-based Point Cloud Compression (G-PCC) for compressing 3D point cloud data. .

The method/apparatus according to the embodiments proposes a method of dividing the geometric information of a point cloud and effectively predicting it in order to effectively compress the geometric information of a point constituting the point cloud data.

As shown in the drawing, points constituting the point cloud content may exist while being biased to a partial area in space. In this case, occupancy information indicating that the point does not exist should be encoded and transmitted to the region where the point does not exist on the oct tree indicating the geometric information of the points.

The scheme proposed by the embodiments proposes a scheme that can be derived from a decoder instead of encoding and transmitting occupancy information of points in an area where points of point cloud content do not exist.

Through this, it is possible to improve compression of the location information (geometric information) of the point cloud by reducing the amount of information that must be performed to hatch the geometric information.

Point cloud data according to embodiments may be represented by dividing a space into an encoding/decoding unit to represent a space.

For example, the encoding/decoding unit may be in the form of a rectangular parallelepiped (or a cube, etc.).

The method/apparatus according to the embodiments proposes a method that can be derived from a decoder instead of encoding and transmitting occupancy information of points in an area where points of point cloud content do not exist. Through this, by reducing the amount of information to be encoded for the geometric information, it is possible to improve compression of the location information (geometric information) of the point cloud.

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

The encoder according to the embodiments may be referred to as a PCC encoder, and as shown in the drawing, may be configured with a geometric information encoder or/and an attribute information encoder.

Each component according to embodiments of the encoder according to the embodiments will be described as follows.

The space division unit 19000 receives PCC data. The spatial division unit may spatially divide PCC data into 3D blocks. According to embodiments, the spatial division unit may spatially divide point cloud data (PCC data) based on a bounding box and/or a sub-bounding box, and the method/device according to the embodiments is based on a divided unit (box). Thus, encoding/decoding can be performed.

The geometry information encoding unit 19001 may encode geometry information (or geometry information). The encoder may generate a bitstream including the encoded geometry information. The encoding unit may generate reconstructed (reconstructed) geometry information.

The attribute information encoding unit 19002 may receive spatially divided PCC data and reconstructed geometric information. The encoder may generate a bitstream including attribute information (or attribute information) by encoding the received data. The attribute information encoder may encode attribute information based on the reconstructed geometric information.

According to embodiments, the spatial division unit, the geometric information encoding unit, and the attribute information encoding unit may correspond to hardware, software, a processor, and/or a combination thereof.

The PCC data according to embodiments may be composed of geometric information or/and attribute information of a point.

The attribute information according to the embodiments is one or more, such as a vector representing the color of a point (R, G, B) or/and a brightness value or/and a reflection coefficient of a lidar or/and a temperature value obtained from a thermal imaging camera. It may be a vector of values obtained from two sensors.

The spatial division unit according to embodiments may divide the input PCC data into at least one 3D block. In this case, the block may mean a coding unit (CU), a prediction unit (PU), or a transformation unit (TU). The partitioning may be performed based on at least one of an octree, a quadtree, a binary tree, a triple tree, and a k-d tree. Point cloud data (PCC data) according to embodiments may have a form in which points are variously distributed in a 3D space. The method/apparatus according to the embodiments may process variously distributed points based on a divided spatial box for efficient encoding/decoding of point cloud data.

The geometric information encoding unit according to embodiments generates an encoded geometric information bitstream and reconstructed geometric information from the received geometric information. The generated bitstream may be transmitted to the PCC decoder. In addition, the generated reconstructed geometric information may be input to the attribute information encoding unit.

The attribute information encoding unit according to embodiments receives the received attribute information and generates an attribute information bitstream. The generated attribute information bitstream may be transmitted to the PCC decoder.

Method/apparatus according to embodiments, for example, the spatial division unit, which can be derived from a decoder, instead of encoding and delivering occupancy information of points in an area where points of point cloud content do not exist based on the spatial division process. Suggest a plan. Through this, by reducing the amount of information to be encoded for the geometric information, it is possible to improve compression of the location information (geometric information) of the point cloud.

The point cloud data transmission method according to the embodiments may further include spatially dividing the point cloud data.

The method of receiving point cloud data according to embodiments may further include the step of spatially dividing the point cloud data.

Each component according to embodiments of the geometric information encoder according to embodiments will be described as follows.

The coordinate system conversion unit 2001 may receive geometric information corresponding to the location information and convert a coordinate system of the geometric information.

The geometric information conversion quantization unit 2002 can quantize geometric information.

The residual geometric information quantization unit 2003 may quantize the quantized geometric information and/or the residual geometric information generated based on the predicted geometric information. For example, the residual value can be generated by subtracting the predicted geometric information from the geometric information.

The geometric information entropy encoder 2004 may encode geometric information based on an entropy encoding method. The geometric information entropy encoding unit may generate a bitstream including geometric information.

The residual geometric information inverse quantization unit (2005) may inversely quantize the residual geometric information.

The filtering unit 1906 may perform filtering based on the inverse quantized geometric information and the predicted geometric information. For example, it is possible to filter the generated data by summing the predicted geometric information and residual geometric information.

The memory unit 2007 may store geometric information based on the filtered data. The memory unit may generate the restored geometric information based on the stored geometric information.

The geometric information prediction unit 2008 may predict geometric information based on the geometric information stored in the memory. The geometric information prediction unit may transmit the predicted data to the residual geometric information quantization unit and/or the full vehicle geometric information inverse quantization unit.

Each element of the geometric information encoder according to the embodiments may correspond to hardware, software, a processor, and/or a combination thereof.

The geometric information encoding unit according to embodiments may include a coordinate system transforming unit, a geometric information transforming quantization unit, a residual geometric information quantizing unit, a geometric information entropy encoding unit, a residual geometric information inverse quantizing unit, a memory, and a geometric information predicting unit.

The coordinate system conversion unit according to the embodiments may receive geometric information as an input and convert it into a coordinate system different from the existing coordinate system. Alternatively, coordinate system transformation may not be performed. The geometric information converted by the coordinate system may be input to the geometric information conversion quantization unit.

Whether the coordinate system is transformed and the coordinate system information according to embodiments may be signaled in units such as a sequence, frame, tile, slice, or block, or whether the coordinate system of neighboring blocks is transformed or not , It can be derived using the location of the unit, and the distance between the unit and the origin.

If the coordinate system information to be converted according to the embodiments is converted to the coordinate system after checking whether the coordinate system is converted, the coordinate system information may be signaled in units such as sequence, frame, tile, slice, block, etc. It can be derived using the size, number of points, quantization value, block division depth, unit location, and distance between the unit and the origin.

The geometric information transform quantization unit according to the embodiments receives geometric information as input, applies one or more transforms such as position transform or/and rotation transform, divides the geometric information by a quantization value, and quantizes the transformed quantized geometric information. . The transformed quantized geometric information may be input to a geometric information entropy encoding unit and a residual geometric information quantizing unit.

The geometric information prediction unit according to embodiments predicts geometric information through geometric information of points in a memory and generates predicted geometric information. The prediction information used for prediction may be encoded by performing entropy encoding.

The residual geometric information quantization unit according to embodiments receives residual geometric information obtained by differentiating the transformed-quantized geometric information and the predicted geometric information, and quantizes it into a quantized value to generate quantized residual geometric information. Quantized residual geometric information may be input to a geometric information entropy encoding unit and a residual geometric information inverse quantization unit.

The geometric information entropy encoding unit according to embodiments may receive quantized residual geometric information or geometric information and perform entropy encoding. Entropy coding may use various coding methods such as Exponential Golomb, Context-Adaptive Variable Length Coding (CAVLC), and Context-Adaptive Binary Arithmetic Coding (CABAC).

The residual geometric information inverse quantization unit according to embodiments receives the quantized residual geometric information and restores the residual geometric information by scaling the quantized value. The restored residual geometric information may be restored to geometric information in addition to the predicted geometric information.

The filtering unit according to embodiments may perform filtering on the reconstructed geometric information. Information related to filtering may be encoded by performing entropy encoding.

The memory according to embodiments may store geometric information calculated through a filtering unit. The stored geometric information may be provided to the geometric information prediction unit when performing prediction.

The geometric information according to the embodiments may be divided based on the bounding box according to the embodiments.

The bounding box 2100 according to the embodiments refers to an area in the form of a rectangular parallelepiped (or a cube, etc.) having a minimum size including geometric information.

For example, a bounding box including geometric information may be divided into a box including four sub-geometric information.

The sub-bounding box 2101 according to the embodiments refers to a sub-box including geometric information included in the bounding box.

The sub/decoding unit 2102 according to the embodiments may include one or more sub geometric information or geometric information, and may include one or more bounding boxes and/or sub bounding boxes.

The method/apparatus according to the embodiments may efficiently encode/decode points and geometric information distributed in space based on the bounding box.

The method/apparatus according to the embodiments may provide a technique for dividing geometric information of point cloud content.

As shown in the figure, the geometric information of the point cloud content can be divided into a bounding box (the bounding box may refer to a rectangular area of the smallest size including the geometric information).

The bounding box according to the embodiments may be divided into one or a plurality of sub-bounding boxes of equal or non-uniform size and may be encoded/decoded independently or dependent on each other.

The encoding/decoding unit may be configured in a square shape having a minimum size including a sub-bounding box or a bounding box according to embodiments. When the geometric information is not divided into sub-geometric information, the encoding/decoding unit may be configured in a square shape having the smallest size including the geometric information.

Geometry information may be expressed by dividing the sub/decoding unit according to the embodiments into an oct tree structure.

Coordinates of coding/decoding units, size of coding/decoding units, number of coding/decoding units, minimum node size, maximum node division depth, coordinates of bounding box, width, height, depth information of the bounding box, according to embodiments Information about the number of points, etc. may exist in the upper level information.

The method/apparatus according to the embodiments proposes a method that can be derived from a decoder instead of encoding and transmitting occupancy information of points in an area where points of point cloud content do not exist based on a bounding box of geometric information. Through this, by reducing the amount of information to be encoded for the geometric information, it is possible to improve compression of the location information (geometric information) of the point cloud.

The spatial division unit according to the embodiments parses higher-level information (2200), divides the bounding box into sub-bounding boxes, configures a coding/decoding unit (2201), and/or parses or derives divided information of a sub/decoding unit (2202). You can do it. Each component according to the embodiments will be described as follows.

The high-level information parsing 2200 represents a process in which the receiving method/device according to the embodiments parses signaling information (or parameter set) included in the point cloud data. For example, the high-level information means signaling information or a parameter set according to embodiments, and may include information related to a spatial division unit according to embodiments. The transmission method/apparatus according to the embodiments may perform spatial partitioning and include signaling information related to spatial partitioning as higher level information in higher level information.

The division of the bounding box into sub-bounding boxes and the configuration of a decoding/decoding unit 2201 represents a process in which the transmission/reception method/device according to the embodiments divides and represents the space of the point cloud data in the space division process. According to embodiments, points of point cloud data may be divided into bounding boxes. According to embodiments, the bounding box may be divided into one or more sub bounding boxes. According to embodiments, the bounding box/subbounding box may constitute an encoding/decoding unit. The method/apparatus according to embodiments provides an effect of efficiently encoding/decoding points distributed based on a bounding box/subbounding box in a coding/decoding unit.

Parsing or deriving split information 2202 of a sub/decoding unit is a process in which a transmission method/device according to embodiments acquires coding units and/or split information necessary for encoding (encoding), or reception according to embodiments. It represents a process in which a method/apparatus obtains a decoding unit and/or split information required for decoding (decoding).

As shown in the drawing, the spatial division unit for dividing geometric information according to embodiments includes a step of parsing high-level information, division of geometric information into sub-geometric information, parsing or deriving encoding/decoding unit information, and division of encoding/decoding units. It may involve parsing or deriving information. The spatial division unit may be applied in the octtree coding step according to the embodiments.

In the decoder according to the embodiments, coordinates of encoding/decoding units, size of encoding/decoding units, number of encoding/decoding units, minimum node size, maximum node division depth, coordinates of bounding box, width, height, depth of region The number of information, geometric information, etc. may be parsed or derived from higher level information.

The bounding box according to the embodiments may be divided into sub bounding boxes. By parsing the coordinates, size, and number of sub-bounding boxes from high-level information, division information of sub-geometric information can be found. The encoding/decoding unit can be set in the form of a rectangular parallelepiped having a minimum size including a subbounding box.

According to embodiments, whether or not a coding/decoding unit is divided, a division method, and whether or not geometric information exists within a node may be parsed or derived to find out. When a node is divided, child nodes can be created by division, and node division can be repeatedly performed until division of the node is stopped. If the regions of the node and the sub-bounding box do not overlap, it is determined that geometric information does not exist in the node, and whether or not the node is divided may be derived.

It is possible to determine whether or not the node and the bounding box area overlap using the coordinate information of the node, the size of the node, the coordinate information of the subbounding box, and the size of the subbounding box according to the embodiments. In the following cases, it can be determined that the regions of the node and the bounding box do not overlap.

When the minimum x-coordinate of the node according to the embodiments is greater than the maximum x-coordinate of the subbounding box or

When the minimum y coordinate of the node according to the embodiments is greater than the maximum y coordinate of the subbounding box or

When the minimum z-coordinate of the node according to the embodiments is greater than the maximum z-coordinate of the subbounding box or

If the maximum x-coordinates of the nodes according to the embodiments are all less than the minimum x-coordinates of the subbounding box or

When the maximum y coordinates of the nodes according to the embodiments are all less than the minimum y coordinates of the subbounding box or

When the maximum z-coordinates of the nodes according to the embodiments are all less than the minimum z-coordinates of the subbounding box

When the regions of the node and the bounding box according to the embodiments overlap, it is possible to parse or derive whether the node is divided, the division method, and whether or not geometric information exists in the node. When the regions of the node and the bounding box overlap and the size of the node is the same as the minimum node size, whether or not the node is divided can be derived, meaning that the division of the node is stopped, and the existence of a point in the node can be derived or parsed.

Points constituting the point cloud content according to the embodiments may exist in a partial area in a space. In this case, occupancy information indicating non-existence for a region that does not exist in the octtree of points must be encoded by the geometry information encoder. However, as shown in the figure below, it is possible to transmit the bounding box information for the area where the actual data exists, and perform geometric information encoding only for the geometric information oct tree corresponding to the points existing in the corresponding bounding box area. One point cloud content may be divided into one or more bounding boxes, and geometric information may be encoded in units of each bounding box or sub bounding box.

In the spatial dividing unit or the spatial dividing step of the method/apparatus according to the embodiments, instead of encoding and transmitting occupancy information of points in a region where points of point cloud content do not exist, a method that can be derived from a decoder is proposed. Through this, by reducing the amount of information to be encoded for the geometric information, it is possible to improve compression of the location information (geometric information) of the point cloud.

A method of transmitting point cloud data according to embodiments may include a step of dividing a bounding box in relation to a process of dividing the point cloud data into bounding boxes.

The encoding step according to embodiments may encode points of point cloud data included in the bounding box related to the spatial division.

A method of receiving point cloud data according to embodiments may include a step of dividing a bounding box in relation to a process of dividing the point cloud data into bounding boxes.

In the decoding step according to embodiments, a point of point cloud data included in a bounding box related to spatial division may be decoded.

The point cloud data according to embodiments may include parameters including the number of bounding boxes related to spatial division, spatial coordinate information of the bounding box, and width, height, and depth of the bounding box.

The octree (or octtree) according to the embodiments may basically convey information in the form of, for example, 8 bits.

The method/apparatus according to the embodiments may divide a space in which points of point cloud data are distributed. For example, embodiments may represent points based on an octree structure. Since the spatial distribution map of the points varies, it may be inefficient to encode/decode all spaces. In order to solve this problem, embodiments may hierarchically divide points (or geometric information) into sub-bounding boxes, bounding boxes, and sub/decoding units as shown in the drawing, thereby efficiently expressing an area where points are located. .

For example, an octree (or octtree) includes a node, and may have a structure between a parent node and a child node. The parent node may include 8-bit child nodes. Depending on the presence or absence of points (or point cloud data, PCC data, geometric information, etc.) in the region, the embodiments may express the octtree using bits of 0 and 1.

According to embodiments, an example of performing octtree division information and derivation information of point cloud geometric information will be described as follows.

For example, referring to the node relationship of (1)-((10001(0000)) of the octtree, (1) and (0000) are implicit in the decoder (decoder, receiver, etc.) according to the embodiments. The derived information on whether to be split may be indicated, reference 1001 may indicate information on whether or not to be split provided from an encoder (encoder, transmission device, etc.) according to embodiments.

(1001) means that data exists, and (0000) means that data does not exist. The method/apparatus according to the embodiments may encode/transmit/receive/decode only a portion where data exists without the need to encode/transmit/receive/decode all 8 bits. For example, an area in which 0 is continuously present is an area in which data does not exist, and the transmission method/apparatus according to the embodiments may transmit only 1 bit to 4 bits in which data exists.

By setting a bounding box and a bounding box range, it is possible to provide an effect of providing high gain by processing only an area in which data exists in the box. Accordingly, the embodiments can provide high encoding/decoding performance by reducing data throughput compared to a method of transmitting information of all spaces by expressing a space of point cloud data as a cube or a rectangular parallelepiped.

According to embodiments, various point cloud data, such as lidar data, may be encoded/decoded using a partitioning method according to the embodiments.

For example, the receiving method/apparatus according to the embodiments does not actually receive an area corresponding to (0000) in the drawing, but restores and decodes based on the received point cloud data and parameter information included in the point cloud data. I can.

The method/apparatus or space dividing unit according to embodiments may divide the point cloud data into one or more bounding boxes. In order for the receiving method/device according to the embodiments to restore data based on the bounding box, the method/device or the spatial division unit according to the embodiments sets the coordinate (range) of the bounding box or offset information on the coordinate system, You can set the width, height, and depth of the bounding box. Additionally, the method/apparatus according to the embodiments may signal the number of points included in the bounding box range.

Depending on embodiments, the bounding box may be divided into one or more sub-bounding boxes. When applying the sub-bounding box, the method/apparatus according to the embodiments sets the number of sub-bounding boxes and obtains information such as coordinates, offsets, width, height, and depth to determine the range of each sub-bounding box. Can signal.

Therefore, the receiving method/device according to the embodiments does not need to receive all the bits of the spatially divided tree structure of point cloud data, and points are based on the bounding box/subbounding box/related signaling information (parameters), etc. By implicitly inducing data for a region that does not actually exist (eg, consecutive 0 nodes), it is possible to effectively increase the encoding/decoding gain.

For example, as shown in the drawing, only information marked in yellow may be encoded and transmitted through a geometric information encoder. The PCC decoder can implicitly derive the information of the part marked in blue by using the bounding box information, etc., and reconstruct the geometric information octtree of the point by combining the derived information and the transmitted information.

In this regard, the following parameters may be included in the bit stream and transmitted.

Coordinates in space of the encoding/decoding unit (x offset, y offset, z offset),

The size of the encoding/decoding unit (for example, whether it is in a bounding box unit or a subbounding box unit, etc.)

Number of encoding/decoding units (number of encoding/decoding units based on the size of the encoding/decoding unit)

Minimum node size, maximum node splitting depth

Number of bounding boxes

About bounding box

Sub-bounding, such as coordinates (x offset, y offset, z offset) in space of each bounding box, width, height, depth, number of points included in the bounding box, etc. The presence of a box,

If there is a sub-bounding box in the bounding box, the following information related to the sub-bounding box included in the bounding box

The number of sub-bounding boxes included in the bounding box,

Coordinates (x offset, y offset, z offset) in the space of each subbounding box, width, height, depth information, etc. of each subbounding box

The decoder according to the embodiments implicitly induces portions corresponding to information about whether to be temporarily derived from the decoder, the number of bounding boxes, the number of subbounding boxes included in the bounding box, and the number of subbounding boxes. At least one or more of coordinates in space, width, height, and depth information of each subbounding box may be required. For example, bounding box/sub bounding box and coordinate information may be required.

Point cloud data according to embodiments may have a bitstream form as shown in the drawing. The point cloud data may include a sequence parameter set (SPS), a geometry parameter set (GPS), an attribute parameter set (APS), and a tile parameter set (TPS) including signaling information according to embodiments. Point cloud data may include one or more geometry and/or attributes. The point cloud data may include geometry and/or attributes in units of one or more slices. The geometry may have a structure of a geometry slice header and geometry slice data. For example, the TPS including signaling information is Tile(0). It may include tile_bounding_box_xyz0, Tile(0)_tile_bounding_box_whd, and the like. The geometry may include geom_geom_parameter_set_id, geom_tile_id, geom_slice_id, geomBoxOrigin, geom_box_log2_scale, geom_max_node_size_log2, geom_num_points, and the like.

The parameters according to the above-described embodiments may be added to and transmitted to SPS, GPS, or APS, or may be added to and transmitted to TPS or Geom for each Slice or Attr for each Slice.

Each abbreviation means:

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: Attrobite bitstream = attribute blick header + attribute brick data

Depending on embodiments, different partitioning techniques may be applied for each tile or slice. For example, the width, height, depth, and number of points included in the bounding box may be different for each slice. In this case, the parameter may be added and transmitted to the TPS or Geom for each Slice or Attr for each Slice.

In the receiving method/apparatus according to the embodiments, for example, a decoder (decoder) may perform geometric information decoding, and the geometric information decoding may inverse quantize the geometric information. Each step according to the embodiments may be as follows.

Parsing or deriving geometric information dequantization value 2500 refers to a process of parsing or deriving a value for dequantizing geometric information of point cloud data. The geometric information quantized at the transmission or encoder side according to the embodiments may be inverse quantized by a reception or decoder according to the embodiments in a reverse process. According to embodiments, the inverse quantization value may be parsed or derived based on point cloud data, parameter set and/or signaling information.

Performing geometric information inverse quantization 2501 refers to a process in which a receiving method/device, for example, a geometric information decoder, inverse quantizes geometric information based on a parsed or derived inverse quantization value.

Regarding the inverse quantization of geometric information according to embodiments, as shown in the drawing, a geometric information inverse quantization value may be parsed or derived from a geometric information decoder, and the geometric information inverse quantization may be performed based on this.

According to embodiments, inverse quantization values in the width, height, and depth directions may be parsed or derived. One or more parsed inverse quantization values can be shared in the width, height, or depth direction. The inverse quantization value may be derived using a value or ratio of the width or depth or height of the bounding box. Inverse quantization information can be shared in units of bounding boxes or subbounding boxes.

According to embodiments, when inverse quantization, the scale values of the width, height, and depth may be the same or may be different from each other.

According to embodiments, inverse quantization may be performed by multiplying the restored geometric information by scale values in the width, height, and depth directions, respectively.

Geometric information according to embodiments may have a width, a depth and/or a height.

The geometric information decoder according to embodiments may inverse quantize the geometric information. The width, depth and/or height of the geometric information can be scaled by each value. According to embodiments, a width scale value, a depth scale value, and a height scale value may be the same or different.

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

Each component according to embodiments of the point cloud data receiving method/device or decoder (decoder) according to the embodiments is as follows.

The spatial dividing unit 2700 may receive the geometric information bitstream and divide the space of the geometric information. Based on the information obtained by spatially dividing the geometric information by the transmission method/device according to the embodiments, the decoder may also divide the space of the point cloud data.

The geometry information decoding unit 2701 may decode a geometry information bitstream.

The attribute information decoding unit 2702 may decode attribute information of the attribute information bitstream based on the restored geometric information. Geometric information and/or attribute information included in the point cloud data may be decoded and restored PCC data.

The PCC decoder according to the embodiments may include a geometric information decoding unit and an attribute information decoding unit.

The spatial division unit according to embodiments may divide a space based on division information provided from an encoder or derived from a decoder.

The geometric information decoding unit according to embodiments restores the geometric information by decoding the received geometric information bitstream. The restored geometric information may be input to the attribute information decoding unit.

The attribute information decoding unit according to embodiments receives the received attribute information bitstream and restored geometric information received from the geometry information decoding unit and restores attribute information. The restored attribute information can be composed of restored PCC data along with the restored geometric information.

The geometric information decoder according to the embodiments includes a geometric information entropy decoding unit 2800, a residual geometric information inverse quantization unit 2801, a geometric information prediction unit 2802, a filtering unit 2803, a memory unit 2804, and/or A coordinate system inverse transform unit 2805 may be included.

The geometric information entropy decoder 2800 according to embodiments may receive a bitstream including geometric information and decode the geometric information. For example, it can be decoded based on an entropy method.

The residual geometric information inverse quantization unit 2801 according to embodiments may inversely quantize the residual geometric information.

The geometric information prediction unit 2802 according to embodiments may predict geometric information. For example, the geometric information prediction unit may predict geometric information based on the geometric information stored in the memory.

The filtering unit 2803 according to embodiments may filter data generated based on inverse quantized residual geometric information and/or predicted geometric information. For example, data may be generated by the apparatus according to the embodiments by summing the inverse quantized residual geometric information and/or the predicted geometric information.

The memory unit 2804 according to embodiments may store filtered data.

The coordinate system inverse transform unit 2805 according to the embodiments may receive geometric information stored in a memory and convert the coordinate system of the geometric information into an inverse manner. The inverse coordinate system transform unit may generate geometric information.

The apparatus for decoding geometric information according to embodiments may include a geometric information entropy decoding unit, a residual geometric information inverse quantization unit, a geometric information prediction unit, and an inverse coordinate system transform unit.

The geometric information entropy decoder according to embodiments may perform entropy decoding on an input bitstream. For example, for entropy decoding, various methods such as Exponential Golomb, Context-Adaptive Variable Length Coding (CAVLC), and Context-Adaptive Binary Arithmetic Coding (CABAC) may be applied. The geometric information entropy decoder may decode information related to geometric information prediction performed by the encoding apparatus. Quantized residual geometric information generated through entropy decoding may be input to the residual geometric information inverse quantization unit.

The residual geometric information inverse quantization unit according to embodiments may perform inverse quantization based on a quantization parameter and the received quantized residual geometric information to generate residual geometric information or geometric information.

The geometric information prediction unit according to embodiments may generate predicted geometric information based on information related to generation of predicted geometric information provided from the geometric information entropy decoder and previously decoded geometric information provided from a memory. The geometric information prediction unit may include an inter prediction unit and an intra prediction unit. The inter prediction unit uses information required for inter prediction of the current prediction unit provided by the encoding device, and determines the current prediction unit based on information included in at least one of a space before or after the current space including the current prediction unit. Inter prediction can be performed. The intra prediction unit may generate predicted geometric information based on geometric information of a point in the current space. When the prediction unit performs intra prediction, intra prediction may be performed based on intra prediction mode information of the prediction unit provided by the encoding device. The reconstructed geometric information may be generated by adding the reconstructed residual geometric information to the predicted geometric information.

The reconstructed geometric information according to the embodiments may be provided to the filtering unit. The filtering unit may perform filtering based on the filtering-related information provided from the decoder or the characteristics of the reconstructed geometric information derived from the decoder.

The memory according to embodiments may store the reconstructed geometric information calculated through the filtering unit.

The inverse coordinate system transform unit according to embodiments may perform inverse coordinate system transformation based on information related to coordinate system transformation provided from the geometric information entropy decoding unit and restored geometric information stored in a memory.

29 shows an example of occupancy derivation according to embodiments.

Instead of signaling the zero-occupancy codes of the outside region of the bounding box (or sub-bounding box) according to the embodiments, the method/apparatus according to the embodiments performs accupancy delivery. Can be done. Regarding the space division according to the embodiments, the method/apparatus according to the embodiments may perform the following process.

1) It is checked whether the node area of the octree is included in the area related to the bounding box.

2) When completely included, encoding/decoding according to embodiments is performed.

3) If not completely included, it is checked whether each child node of the current node overlaps the bounding box area.

4) When not overlapping, the accupancy of the child node is not encoded/decoded, and is derived to 0.

For example, the method/apparatus according to the embodiments determines whether each child node of the current node is included in the bounding box (subbounding box) when the current node is not completely included in the bounding box (subbounding box). Check, and encode/decode only the accupancy of child nodes that overlap with the bounding box (subbounding box).

For this reason, the method/apparatus according to the embodiments has an effect of removing unnecessary zero-acupancy signaling when an empty area occupies space in relation to a bounding box (subbounding box).

Point cloud data according to embodiments may include signaling information related to a geometry node.

single_occupancy_flag represents a flag for single accupancy.

occupancy_idx represents identification information of accupancy. When the geometry node corresponds to a single accupant, the method/apparatus according to the embodiments refers to the accupant identification information.

overlapped_occupancy indicates a bitmap that identifies overlapped with contents bounding box and occupied child nodes of the current node, which overlap with the point cloud content bounding box. The method/apparatus according to the embodiments may refer to this value when the current node is not completely included in the bounding box (subbounding box).

occupancy_map represents an accupancy map. When the current node is completely included in the bounding box (subbounding box), the method/apparatus according to the embodiments refers to the accufancy map.

occupancy_byte represents byte information during accupancy.

num_points_eq1_flag represents flag information related to the number of points.

num_points_minus2 represents information related to the number of points.

direct_mode_flag represents flag information related to the direct mode.

num_direct_points_minus1 represents information related to the number of direct points.

point_rem_x[ i ][ j ], point_rem_y[ i ][ j ], point_rem_z[ i ][ j] represent the x, y, and z information of the point.

Based on the signaling information according to the embodiments, the method/apparatus according to the embodiments may perform a space division operation, and may efficiently encode/decode point cloud data without wasting space.

Point cloud data transmission method according to embodiments (S31000) acquiring point cloud data; (S31001) encoding point cloud data; And/or (S31002) transmitting point cloud data; It may include. Each step will be described in the embodiments as follows.

Regarding S31000, the method/apparatus according to the embodiments may acquire point cloud data. The process of obtaining point cloud data according to embodiments may include the process described in FIGS. 1 to 3, and the like.

With respect to S31001, the method/apparatus according to the embodiments may encode point cloud data. The process of encoding point cloud data according to embodiments may include the processes described in FIGS. 1 to 2, 3, 5-9, 12, 18 to 23, and the like.

In relation to S31002, the method/apparatus according to the embodiments may transmit point cloud data. The process of transmitting point cloud data according to embodiments may include the process described in FIGS. 1 to 2, 23, 14-16, and the like.

The point cloud data transmission method/apparatus according to the embodiments can be combined with the above-described embodiments, and divides the geometric information of the point cloud to effectively compress the geometric information of the points constituting the point cloud data. It can provide an effect that can effectively predict this. In addition. The method/apparatus for transmitting point cloud data according to the embodiments is an accue indicating that points do not exist for an area where a point does not exist in the oct tree in consideration of the presence area and/or the non-existence area when points are distributed in space. Fansi information can be transmitted, and data transmission efficiency can be increased based on a bounding box (or subbounding box).

32 illustrates a method of receiving point cloud data according to embodiments.

In embodiments, a method for receiving point cloud data includes (S32000) receiving point cloud data; (S32001) decoding the point cloud data; And/or (S32002) rendering the point cloud data; It may include.

In relation to S32000, the method/apparatus according to the embodiments may receive point cloud data. The process of receiving point cloud data according to embodiments may include the process described in FIGS. 1 to 2, 10-11, 13-16, and the like.

With respect to S32001, the method/apparatus according to the embodiments may decode the point cloud data. The decoding process of point cloud data according to embodiments may include the process described in FIGS. 1-2, 5-9, 10-11, 13, 18, 21-28, and the like.

With respect to S32002, the method/apparatus according to the embodiments may render point cloud data. The rendering process according to embodiments may include the process described in FIGS. 1-2, 23, 14-16, and the like.

Each of the above-described parts, modules or units may be software, processor, or hardware parts that execute successive processes stored in a memory (or storage unit). Each of the steps described in the above-described embodiment may be performed by processor, software, and hardware parts. Each module/block/unit described in the above-described embodiment may operate as a processor, software, or hardware. In addition, methods suggested by the embodiments may be executed as code. This code can be written to a storage medium that can be read by the processor, and thus can be read by a processor provided by the apparatus.

For convenience of explanation, each drawing has been described separately, but it is also possible to design a new embodiment by merging the embodiments described in each drawing. In addition, designing a computer-readable recording medium in which a program for executing the previously described embodiments is recorded is also within the scope of the rights of the embodiments according to the needs of the skilled person.

The apparatus and method according to the embodiments are not limitedly applicable to the configuration and method of the described embodiments as described above, but the above-described embodiments are all or part of each of the embodiments so that various modifications can be made. It may be configured in combination.

On the other hand, it is possible to implement the method proposed by the embodiments as a code readable by the processor on a recording medium readable by the processor provided in the network device. The processor-readable recording medium includes all types of recording devices that store data that can be read by the processor. Examples of recording media that can be read by the processor include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage, etc., and also include those implemented in the form of carrier waves such as transmission through the Internet. . Further, the processor-readable recording medium is distributed over a computer system connected through a network, so that the processor-readable code can be stored and executed in a distributed manner.

In addition, although preferred embodiments of the embodiments have been illustrated and described above, the embodiments are not limited to the specific embodiments described above, and the technical field to which the present invention belongs without departing from the gist of the embodiments claimed in the claims. In addition, various modifications can be implemented by those of ordinary skill in the art, and these modifications should not be understood individually from the technical idea or prospect of the embodiments.

It is understood by those skilled in the art that various changes and modifications are possible in the embodiments without departing from the spirit or scope of the embodiments. Accordingly, the embodiments are intended to cover variations and modifications of the embodiments provided within the appended claims and their equivalents.

In the present specification, both apparatus and method inventions are mentioned, and descriptions of both apparatus and method inventions may be applied to complement each other.

In this document, “/” and “,” are interpreted as “and/or”. For example, “A/B” is interpreted as “A and/or B”, and “A, B” is interpreted as “A and/or B”. Additionally, “A/B/C” means “at least one of A, B and/or C”. In addition, “A, B, C” also means “at least one of A, B and/or C”. (In this document, the term “/”and “,”should be interpreted to indicate "and/or". For instance, the expression "A/B" may mean "A and/or B". Further, "AB" may mean "A and/or B". Further, "A,B" may mean "A and/or B". Further, "A/B/C" may mean "at least one one of A, B, and/ or C". Also, "A/B/C" may mean "at least one of A, B, and/or C.")

Additionally, in this document “or” is to be interpreted 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”. (Further, in the document, the term “or” should be interpreted to indicate “and/or.” For instance, the expression “A or B” may comprise 1) only A, 2) only B, and/or 3) both A and B. In other words, the term "or" in this document should be interpreted to indicate "additionally or alternatively".)

Various elements of the embodiments may be performed by hardware, software, firmware, or a combination thereof. Various elements of the embodiments may be implemented on a single chip such as a hardware circuit. Depending on the embodiments, the embodiments may optionally be performed on individual needles. Depending on the embodiments, at least one of the elements of the embodiments may be executed in one or more processors including instructions for performing operations according to the embodiments.

Terms such as first and second are used to describe various elements of the embodiments. These terms do not limit the interpretation of the elements of the embodiments. These terms are used to distinguish between one element and another. For example, a 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. These terms can be interpreted within the scope of the embodiments. Both the first user input signal and the second user input signal are user input signals, and do not mean the same user input signals unless clearly indicated in context.

The terms used to describe the embodiments are used for the purpose of describing specific embodiments, and are not intended to limit the embodiments. As used in the description of the embodiments and in the claims, the singular is intended to include the plural unless the context clearly indicates. And/or the expression is used in a sense including all possible combinations between terms. The include expression describes the existence of features, numbers, steps, elements, and/or components, and does not imply that no additional features, numbers, steps, elements, and/or components are included. .

Conditional expressions such as when, when, and when used to describe the embodiments are not limited to an optional case. When a specific condition is satisfied, it is intended to perform a related operation in response to a specific condition or to interpret the related definition.

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

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

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

Embodiments may include changes/modifications, and changes/modifications do not depart from the scope of the claims and the same.

Claims

Encoding the point cloud data; And

Transmitting a bitstream including the point cloud data; Containing,

Point cloud data transmission method.
The method of claim 1, wherein the method comprises:

Further comprising the step of dividing the space of the point cloud data,

Point cloud data transmission method.
The method of claim 2,

The point cloud data is divided based on a bounding box,

Point cloud data transmission method.
The method of claim 3,

The point cloud data is encoded based on the bounding box,

Point cloud data transmission method.
The method of claim 3,

The bitstream includes parameters including the number of bounding boxes, spatial coordinate information of the bounding box, width, height, and depth of the bounding box,

Point cloud data transmission method.
An encoder for encoding point cloud data; And

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

Point cloud data transmission device.
The method of claim 6, wherein the device

Further comprising a space dividing unit for dividing the space of the point cloud data,

Point cloud data transmission device.
The method of claim 7,

The point cloud data is divided based on a bounding box,

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

The point cloud data is encoded based on the bounding box,

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

The bitstream includes parameters including the number of bounding boxes, spatial coordinate information of the bounding box, width, height, and depth of the bounding box,

Point cloud data transmission device.
Receiving a bitstream including point cloud data;

Decoding the point cloud data; And

Rendering the point cloud data; Containing,

How to receive point cloud data.
The method of claim 11, wherein the method comprises:

Further comprising the step of spatially dividing the point cloud data,

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

The point cloud data is divided based on a bounding box,

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

The point cloud data is decoded based on the bounding box,

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

The bitstream includes a parameter including the number of bounding boxes related to the spatial division, spatial coordinate information of the bounding box, and a width, height, and depth of the bounding box,

How to receive point cloud data.
A receiving unit receiving a bitstream including point cloud data;

A decoder for decoding the point cloud data; And

A renderer for rendering the point cloud data; Containing,

Point cloud data receiving device.
The method of claim 16, wherein the device

Further comprising a space division unit for spatially dividing the point cloud data,

Point cloud data receiving device.
The method of claim 17,

The point cloud data is divided into bounding boxes,

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

The point cloud data is decoded based on the bounding box,

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

The bitstream includes parameters including the number of bounding boxes, spatial coordinate information of the bounding box, width, height, and depth of the bounding box,

Point cloud data receiving device.