Device for transmitting point cloud data, method for transmitting point cloud data, device for receiving point cloud data, and method for receiving point cloud data

Abstract

A method of transmitting point cloud data according to embodiments can include the steps of encoding the point cloud data, encapsulating the point cloud data, and transmitting the point cloud data. A method of receiving point cloud data according to embodiments can include the steps of receiving the point cloud data, decapsulating the point cloud data, and decoding the point cloud data.

Description

TECHNICAL FIELD

[0001] Embodiments relate to a method and apparatus for processing point cloud content. BACKGROUND

[0002] Point cloud content is content represented by a point cloud, which is a set of points belonging to a coordinate system representing a three-dimensional space. The point cloud content can represent a three-dimensional configured media and is used to provide various services, such as virtual reality (VR), augmented reality (AR), mixed reality (MR), XR (extended reality), and self-driving services. However, tens of thousands to hundreds of thousands of point data are required to represent the point cloud content. Therefore, a method of efficiently processing a large amount of point data is required. SUMMARY

[0003] TECHNICAL PROBLEM

[0004] Embodiments provide a device and method for efficiently processing point cloud data. Embodiments provide a point cloud data processing method and device for solving latency and encoding / decoding complexity.

[0005] Additional advantages, objects, and features of the disclosure will be set forth in part by the description that follows and will be apparent to those skilled in the art upon examination of the following, or can be learned from practice of the disclosure. The advantages and objects of the disclosure can be achieved and obtained by means of the instrumentalities particularly pointed out in the written description and claims and the appended drawings.

[0006] TECHNICAL SOLUTION

[0007] To achieve these objects and other advantages and in accordance with the purposes of the disclosure, as embodied and broadly described herein, a method of transmitting point cloud data can include the steps of encoding point cloud data, encapsulating the point cloud data, and transmitting the point cloud data. In another aspect of the disclosure, a method of receiving point cloud data can include the steps of receiving point cloud data, decapsulating the point cloud data, and decoding the point cloud data.

[0008] ADVANTAGEOUS EFFECTS

[0009] The point cloud data transmission method, point cloud data transmission apparatus, point cloud data reception method, and point cloud data reception apparatus according to the embodiments can provide a high-quality point cloud service.

[0010] The point cloud data transmission method, point cloud data transmission apparatus, point cloud data reception method, and point cloud data reception apparatus according to the embodiments can implement various video coding methods.

[0011] The point cloud data transmission method, point cloud data transmission apparatus, point cloud data reception method, and point cloud data reception apparatus according to the embodiments can provide a general point cloud content, such as an autonomous driving service. Attached Figure Description

[0012] The accompanying drawings are included to provide a further understanding of this disclosure and are incorporated in and constitute a part of this application. The drawings illustrate embodiments of the disclosure and, together with the description, serve to illustrate the principles of the disclosure. In the drawings:

[0013] Figure 1 An exemplary point cloud content providing system according to an embodiment is shown;

[0014] Figure 2 This is a block diagram illustrating the operation of providing point cloud content according to an embodiment;

[0015] Figure 3 An exemplary process for capturing point cloud video according to an embodiment is shown;

[0016] Figure 4 An exemplary block diagram of a point cloud video encoder according to an embodiment is shown;

[0017] Figure 5 An example of a voxel in 3D space according to an embodiment is shown;

[0018] Figure 6 An example of an octree and occupancy code according to an implementation method is shown;

[0019] Figure 7 An example of a neighboring node pattern according to an implementation method is shown;

[0020] Figure 8 Examples of point configurations for point cloud content of various LODs according to the implementation method are shown;

[0021] Figure 9 Examples of point configurations for point cloud content of various LODs according to the implementation method are shown;

[0022] Figure 10 An example block diagram of a point cloud video decoder according to an embodiment is shown;

[0023] Figure 11 An example of a point cloud video decoder according to an embodiment is shown;

[0024] Figure 12 The configuration for point cloud video encoding for a transmitting device according to an embodiment is shown;

[0025] Figure 13 The configuration of point cloud video decoding of the receiving device according to an embodiment is shown.

[0026] Figure 14 An architecture for storing and streaming G-PCC-based point cloud data is shown according to an embodiment.

[0027] Figure 15 An example of storage and sending of point cloud data according to an embodiment is shown;

[0028] Figure 16 An example of a receiving apparatus according to an embodiment is shown;

[0029] Figure 17 An example of an exemplary structure connectable in operation with a method / apparatus for sending and receiving point cloud data according to an embodiment is shown;

[0030] Figure 18 An operation of encapsulating a portion of a G-PCC bitstream according to an embodiment is shown;

[0031] Figure 19 A sequence parameter set according to an embodiment is shown;

[0032] Figure 20 A tile inventory (tile parameter set) according to an embodiment is shown;

[0033] Figure 21 A geometry parameter set according to an embodiment is shown;

[0034] Figure 22 An attribute parameter set according to an embodiment is shown;

[0035] Figure 23 A geometry slice header according to an embodiment is shown;

[0036] Figure 24 An attribute slice according to an embodiment is shown;

[0037] Figure 25 A structure of a G-PCC parameter set according to an embodiment is shown;

[0038] Figure 26 A sample structure for single track encapsulation according to an embodiment is shown;

[0039] Figure 27 A multi-track container according to an embodiment is shown;

[0040] Figure 28 A sample structure according to an embodiment is shown;

[0041] Figure 29 A parameter set contained in a timing metadata track according to an embodiment is shown;

[0042] Figure 30 A tile inventory according to an embodiment is shown;

[0043] Figure 31A G-PCC 3D tile information structure according to an embodiment is shown.

[0044] Figure 32 A structure of G-PCC 3D tile list information according to an embodiment is shown.

[0045] Figure 33 A G-PCC base track according to an embodiment is shown.

[0046] Figure 34 A method of transmitting point cloud data according to an embodiment is shown; and

[0047] Figure 35 A method of receiving point cloud data according to an embodiment is shown. DETAILED DESCRIPTION

[0048] Preferred embodiments of the embodiments will be described in detail with reference to the attached drawings. The following detailed description of the preferred embodiments is provided for the purpose of illustrating the embodiments of the embodiments, and is not intended to limit the embodiments to the embodiments described. The following detailed description includes details of the embodiments to provide a thorough understanding of the embodiments. However, it will be apparent to those skilled in the art that the embodiments can be practiced without these details.

[0049] Most of the terms used in the embodiments are widely used in the art. Although selected from general terms, some terms are arbitrarily selected by the applicant, and their meanings are described in detail in the following description as needed. Therefore, the embodiments should be understood based on the intended meaning of the terms rather than the simple name or meaning of the terms.

[0050] Figure 1 An exemplary point cloud content providing system according to an embodiment is shown.

[0051] Figure 1 The illustrated point cloud content providing system can include a transmitting device 10000 and a receiving device 10004. The transmitting device 10000 and the receiving device 10004 are capable of wired or wireless communication to transmit and receive point cloud data.

[0052] The point cloud data transmitting apparatus 10000 according to the embodiments can acquire and process a point cloud video (or point cloud content) and transmit the same. According to the embodiments, the transmitting apparatus 10000 can include a fixed station, a base transceiver system (BTS), a network, an artificial intelligence (AI) apparatus and / or system, a robot, an AR / VR / XR apparatus, and / or a server. According to the embodiments, the transmitting apparatus 10000 can include an apparatus configured to perform communication with a base station and / or other wireless apparatuses using a radio access technology (e.g., 5G new RAT (NR), long term evolution (LTE)), a robot, a vehicle, an AR / VR / XR apparatus, a portable apparatus, a home appliance, an internet of things (IoT) apparatus, and an AI apparatus / server.

[0053] The transmitting apparatus 10000 according to the embodiments includes a point cloud video acquisition unit 10001, a point cloud video encoder 10002, and / or a transmitter (or communication module) 10003.

[0054] The point cloud video acquisition unit 10001 according to the embodiments acquires a point cloud video through a processing procedure such as capturing, synthesizing, or generating. The point cloud video is a point cloud content represented by a point cloud, the point cloud is a set of points located in a 3D space, and can be referred to as point cloud video data. The point cloud video according to the embodiments can include one or more frames. One frame represents a still image / picture. Accordingly, the point cloud video can include point cloud images / frames / pictures, and can be referred to as a point cloud image, frame, or picture.

[0055] The point cloud video encoder 10002 according to the embodiments encodes the acquired point cloud video data. The point cloud video encoder 10002 can encode the point cloud video data based on point cloud compression encoding. The point cloud compression encoding according to the embodiments can include geometry-based point cloud compression (G-PCC) encoding and / or video-based point cloud compression (V-PCC) encoding or next generation encoding. The point cloud compression encoding according to the embodiments is not limited to the above-described embodiments. The point cloud video encoder 10002 can output a bitstream including the encoded point cloud video data. The bitstream can include not only the encoded point cloud video data but also signaling information related to encoding of the point cloud video data.

[0056] The transmitter 10003 according to the embodiments transmits a bitstream including the encoded point cloud video data. The bitstream according to the embodiments is encapsulated in a file or a segment (e.g., a streaming segment) and transmitted via various networks such as a broadcasting network and / or a broadband network. Although not shown in the drawing, the transmission device 10000 can include an encapsulator (or an encapsulation module) configured to perform an encapsulation operation. According to the embodiments, the encapsulator can be included in the transmitter 10003. According to the embodiments, the file or the segment can be transmitted to the reception device 10004 via a network or stored in a digital storage medium (e.g., USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.). The transmitter 10003 according to the embodiments is capable of wired / wireless communication with the reception device 10004 (or the receiver 10005) via a 4G, 5G, 6G, etc. network. In addition, the transmitter can perform necessary data processing operations according to a network system (e.g., a 4G, 5G, or 6G communication network system). The transmission device 10000 can transmit the encapsulated data in an on-demand manner.

[0057] The reception device 10004 according to the embodiments includes the receiver 10005, the point cloud video decoder 10006, and / or the Tenderer 10007. According to the embodiments, the reception device 10004 can include a device, a robot, a vehicle, an AR / VR / XR device, a portable device, a home appliance, an Internet of Things (IoT) device, and an AI device / server configured to perform communication with a base station and / or other wireless devices using a radio access technology (e.g., 5G New RAT (NR), Long Term Evolution (LTE)).

[0058] The receiver 10005 according to the 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 can perform necessary data processing according to a network system (e.g., a communication network system of 4G, 5G, 6G, etc.). The receiver 10005 according to the embodiments can decapsulate the received file / segment and output the bitstream. According to the embodiments, the receiver 10005 can include a decapsulator (or a decapsulation module) configured to perform a decapsulation operation. The decapsulator can be implemented as an element (or a component) separate from the receiver 10005.

[0059] The point cloud video decoder 10006 decodes the bitstream including the point cloud video data. The point cloud video decoder 10006 can decode the point cloud video data according to a method in which the point cloud video data is encoded (e.g., inverse processing of the operation of the point cloud video encoder 10002). Accordingly, the point cloud video decoder 10006 can decode the point cloud video data by performing point cloud decompression encoding, which is inverse processing of point cloud compression. The point cloud decompression encoding includes G-PCC encoding.

[0060] The renderer 10007 renders the decoded point cloud video data. The renderer 10007 can output the point cloud content by rendering not only the point cloud video data but also audio data. According to an embodiment, the renderer 10007 can include a display configured to display the point cloud content. According to an embodiment, the display can be implemented as a separate device or component, rather than being included in the renderer 10007.

[0061] The arrow indicated by the dotted line in the drawing represents a transmission path of feedback information acquired by the reception device 10004. The feedback information is information reflecting the interaction of a user consuming the point cloud content, and includes information about the user (e.g., head orientation information, viewport information, etc.). Specifically, when the point cloud content is content for a service requiring interaction with the user (e.g., a self-driving service, etc.), the feedback information can be provided to a content sender (e.g., the transmission device 10000) and / or a service provider. According to an embodiment, the feedback information can be used in the reception device 10004 as well as the transmission device 10000, or can not be provided.

[0062] The head orientation information according to an embodiment is information about the position, orientation, angle, motion, etc. of the head of the user. The reception device 10004 according to an embodiment can calculate the viewport information based on the head orientation information. The viewport information can be information about the region of the point cloud video that the user is watching. The viewpoint is a point through which the user watches the point cloud video, and can refer to the center point of the viewport region. That is, the viewport is a region centered on the viewpoint, and the size and shape of the region can be determined by the field of view (FOV). Thus, in addition to the head orientation information, the reception device 10004 can extract the viewport information based on the vertical or horizontal FOV supported by the device. In addition, the reception device 10004 performs gaze analysis, etc. to check the way in which the user consumes the point cloud, the region in which the user gazes in the point cloud video, the gaze time, etc. According to an embodiment, the reception device 10004 can transmit feedback information including the gaze analysis result to the transmission device 10000. The feedback information according to an embodiment can be acquired in the process of rendering and / or display. The feedback information according to an embodiment can be taken by one or more sensors included in the reception device 10004. According to an embodiment, the feedback information can be taken by the renderer 10007 or a separate external element (or device, component, etc.). Figure 1The dotted line in the middle indicates a process of transmitting feedback information acquired by the rendering device 10007. The point cloud content providing system can process (encode / decode) point cloud data based on the feedback information. Accordingly, the point cloud video decoder 10006 can perform a decoding operation based on the feedback information. The reception device 10004 can transmit the feedback information to the transmission device 10000. The transmission device 10000 (or the point cloud video encoder 10002) can perform an encoding operation based on the feedback information. Accordingly, the point cloud content providing system can efficiently process necessary data (e.g., point cloud data corresponding to the head position of the user) based on the feedback information, rather than processing (encoding / decoding) the entire point cloud data, and provide point cloud content to the user.

[0063] According to embodiments, the transmission device 10000 can be referred to as an encoder, a transmission device, a transmitter, a transmission system, or the like, and the reception device 10004 can be referred to as a decoder, a reception device, a receiver, a reception system, or the like.

[0064] According to embodiments, the point cloud content providing system of FIG. 1 can be referred to as a point cloud content providing system, a point cloud video providing system, or the like. Figure 1 Point cloud data processed in the point cloud content providing system of FIG. 1 (through a series of processes of acquisition / encoding / transmission / decoding / rendering) can be referred to as point cloud content data or point cloud video data. According to embodiments, the point cloud content data can be used as a concept that encompasses metadata or signaling information related to point cloud data.

[0065] Figure 1 The elements of the point cloud content providing system shown can be implemented by hardware, software, a processor, and / or a combination thereof.

[0066] Figure 2 is a block diagram illustrating a point cloud content providing operation according to an embodiment.

[0067] Figure 2 The block diagram of FIG. 2 illustrates Figure 1 operations of the point cloud content providing system described in FIG. 1. As described above, the point cloud content providing system can process point cloud data based on point cloud compression encoding (e.g., G-PCC).

[0068] The point cloud content providing system (e.g., the point cloud transmitting apparatus 10000 or the point cloud video acquisition unit 10001) according to the embodiments can acquire a point cloud video (20000). The point cloud video is represented by a point cloud belonging to a coordinate system for expressing a 3D space. The point cloud video according to the embodiments can include a Ply (Polygon file format or Stanford Triangle format) file. When the point cloud video has one or more frames, the acquired point cloud video can include one or more Ply files. The Ply file includes point cloud data such as point geometry and / or attributes. The geometry includes the position of a point. The position of each point can be represented by a parameter (e.g., the value of the X, Y, and Z axes) representing a three-dimensional coordinate system (e.g., a coordinate system composed of X, Y, and Z axes). The attributes include attributes of a point (e.g., information on the texture, color (YCbCr or RGB), reflectance r, transparency, etc. of each point). A point has one or more attributes. For example, a point can have an attribute that is a color or two attributes that are a color and reflectance. According to the embodiments, the geometry can be referred to as a position, geometry information, geometry data, etc., and the attributes can be referred to as attributes, attribute information, attribute data, etc. The point cloud content providing system (e.g., the point cloud transmitting apparatus 10000 or the point cloud video acquisition unit 10001) can acquire point cloud data from information (e.g., depth information, color information, etc.) related to the point cloud video acquisition process.

[0069] The point cloud content providing system (e.g., the transmitting apparatus 10000 or the point cloud video encoder 10002) according to the embodiments can encode point cloud data (20001). The point cloud content providing system can encode the point cloud data based on point cloud compression encoding. As described above, the point cloud data can include the geometry and attributes of a point. Accordingly, the point cloud content providing system can perform geometry encoding that encodes the geometry and output a geometry bitstream. The point cloud content providing system can perform attribute encoding that encodes the attributes and output an attribute bitstream. According to the embodiments, the point cloud content providing system can perform the attribute encoding based on the geometry encoding. The geometry bitstream and the attribute bitstream according to the embodiments can be multiplexed and output as one bitstream. The bitstream according to the embodiments can further include signaling information related to the geometry encoding and the attribute encoding.

[0070] The point cloud content providing system (e.g., the transmitting apparatus 10000 or the transmitter 10003) according to the embodiments can transmit the encoded point cloud data (20002). As shown in FIG. 20B, the encoded point cloud data can be represented by a geometry bitstream and an attribute bitstream. In addition, the encoded point cloud data can be transmitted in the form of a bitstream together with signaling information (e.g., signaling information related to the geometry encoding and the attribute encoding) related to the encoding of the point cloud data. The point cloud content providing system can encapsulate the bitstream carrying the encoded point cloud data and transmit it in the form of a file or a segment. Figure 1 The point cloud content providing system (e.g., the transmitting apparatus 10000 or the transmitter 10003) according to the embodiments can transmit the encoded point cloud data (20002). As shown in FIG. 20B, the encoded point cloud data can be represented by a geometry bitstream and an attribute bitstream. In addition, the encoded point cloud data can be transmitted in the form of a bitstream together with signaling information (e.g., signaling information related to the geometry encoding and the attribute encoding) related to the encoding of the point cloud data. The point cloud content providing system can encapsulate the bitstream carrying the encoded point cloud data and transmit it in the form of a file or a segment.

[0071] A point cloud content providing system (e.g., reception device 10004 or receiver 10005) according to an embodiment can receive a bitstream including encoded point cloud data. In addition, the point cloud content providing system (e.g., reception device 10004 or receiver 10005) can demultiplex the bitstream.

[0072] A point cloud content providing system (e.g., reception device 10004 or point cloud video decoder 10005) can decode encoded point cloud data (e.g., geometry bitstream, attribute bitstream) transmitted in a bitstream. The point cloud content providing system (e.g., reception device 10004 or point cloud video decoder 10005) can decode point cloud video data based on signaling information included in the bitstream in relation to encoding of the point cloud video data. The point cloud content providing system (e.g., reception device 10004 or point cloud video decoder 10005) can decode a geometry bitstream to reconstruct positions (geometry) of points. The point cloud content providing system can reconstruct attributes of points by decoding an attribute bitstream based on the reconstructed geometry. The point cloud content providing system (e.g., reception device 10004 or point cloud video decoder 10005) can reconstruct a point cloud video based on positions from the reconstructed geometry and the decoded attributes.

[0073] A point cloud content providing system (e.g., reception device 10004 or renderer 10007) according to an embodiment can render decoded point cloud data (20004). The point cloud content providing system (e.g., reception device 10004 or renderer 10007) can render geometry and attributes decoded through a decoding process using various rendering methods. Points in the point cloud content can be rendered as a vertex having a certain thickness, a cube having a certain minimum size centered on a corresponding vertex position, or a circle centered on a corresponding vertex position. All or part of the rendered point cloud content is provided to a user through a display (e.g., VR / AR display, general display, etc.).

[0074] A point cloud content providing system (e.g., reception device 10004) according to an embodiment can obtain feedback information (20005). The point cloud content providing system can encode and / or decode point cloud data based on the feedback information. The feedback information and operations of the point cloud content providing system according to an embodiment are the same as those of the point cloud content providing system 10000 described with reference to FIG. 1, and thus a detailed description thereof is omitted. Figure 1 The feedback information and operations described are the same, and thus a detailed description thereof is omitted.

[0075] Figure 3 An exemplary process of capturing a point cloud video according to an embodiment is illustrated.

[0076] Figure 3 Reference is made to Figure 1-2An exemplary point cloud video capturing process of the described point cloud content providing system.

[0077] Point cloud content includes point cloud videos (images and / or videos) representing objects and / or environments located in various 3D spaces (e.g., 3D spaces representing real environments, 3D spaces representing virtual environments, etc.). Accordingly, the point cloud content providing system according to embodiments can capture point cloud videos using one or more cameras (e.g., infrared cameras capable of taking depth information, RGB cameras capable of extracting color information corresponding to depth information, etc.), projectors (e.g., infrared pattern projectors taking depth information), lidars, etc. The point cloud content providing system according to embodiments can extract a geometry consisting of points in a 3D space from depth information and extract attributes of respective points from color information to take point cloud data. The images and / or videos according to embodiments can be captured based on at least one of an inside-out technique and an outside-in technique.

[0078] Figure 3 The left portion of FIG. 1 illustrates an inside-out technique. The inside-out technique refers to a technique of capturing images of a central object using one or more cameras (or camera sensors) positioned around the central object. The inside-out technique can be used to generate point cloud content providing a 360-degree image of a key object to a user (e.g., VR / AR content providing a 360-degree image of an object (e.g., a key object such as a character, a player, an object, or an actor) to a user).

[0079] Figure 3 The right portion of FIG. 1 illustrates an outside-in technique. The outside-in technique refers to a technique of capturing images of an environment of a central object, not the central object, using one or more cameras (or camera sensors) positioned around the central object. The outside-in technique can be used to generate point cloud content providing a surrounding environment appearing from a user's perspective (e.g., content representing an external environment that can be provided to a user of a self-driving vehicle).

[0080] As illustrated, point cloud content can be generated based on a capturing operation of one or more cameras. In this case, coordinate systems can be different between cameras, and thus the point cloud content providing system can calibrate the one or more cameras to set a global coordinate system before the capturing operation. In addition, the point cloud content providing system can generate point cloud content by synthesizing arbitrary images and / or videos with images and / or videos captured through the above-described capturing techniques. The point cloud content providing system can not perform the capturing operation described in FIG. 2 when generating point cloud content representing a virtual space. The point cloud content providing system according to embodiments can perform post-processing on captured images and / or videos. In other words, the point cloud content providing system can remove unwanted areas (e.g., a background), identify a space to which captured images and / or videos are connected, and perform an operation of filling a space hole when there is a space hole. Figure 3 As illustrated, point cloud content can be generated based on a capturing operation of one or more cameras. In this case, coordinate systems can be different between cameras, and thus the point cloud content providing system can calibrate the one or more cameras to set a global coordinate system before the capturing operation. In addition, the point cloud content providing system can generate point cloud content by synthesizing arbitrary images and / or videos with images and / or videos captured through the above-described capturing techniques. The point cloud content providing system can not perform the capturing operation described in FIG. 2 when generating point cloud content representing a virtual space. The point cloud content providing system according to embodiments can perform post-processing on captured images and / or videos. In other words, the point cloud content providing system can remove unwanted areas (e.g., a background), identify a space to which captured images and / or videos are connected, and perform an operation of filling a space hole when there is a space hole.

[0081] The point cloud content providing system can generate one piece of point cloud content by performing coordinate transformation on points of the point cloud video taken from the respective cameras. The point cloud content providing system can perform coordinate transformation on the points based on position coordinates of the respective cameras. Accordingly, the point cloud content providing system can generate point cloud content representing a wide range of content, or can generate point cloud content having a high density of points.

[0082] Figure 4 An exemplary point cloud video encoder according to an embodiment is illustrated.

[0083] Figure 4 An exemplary point cloud video encoder 10002 is illustrated. Figure 1 The point cloud video encoder reconstructs and encodes point cloud data (e.g., positions and / or attributes of points) to adjust quality (e.g., lossless, lossy, or close to lossless) of the point cloud content according to network conditions or applications. When the total size of the point cloud content is large (e.g., 60 Gbps of point cloud content is given for 30 fps), the point cloud content providing system can not be able to stream the content in real time. Accordingly, the point cloud content providing system can reconstruct the point cloud content based on a maximum target bitrate to provide the point cloud content according to network environments, etc.

[0084] As described with reference to Figure 1-2 The point cloud video encoder can perform geometry encoding and attribute encoding. The geometry encoding is performed before the attribute encoding.

[0085] The point cloud video encoder according to an embodiment includes a coordinate transformer (transform coordinates) 40000, a quantizer (quantize and remove points (voxelize)) 40001, an octree analyzer (analyze octree) 40002 and a surface approximation analyzer (analyze surface approximation) 40003, an arithmetic encoder (arithmetic encode) 40004, a geometry reconstructor (reconstruct geometry) 40005, a color transformer (transform color) 40006, an attribute transformer (transform attribute) 40007, a RAHT transformer (RAHT) 40008, a LOD generator (generate LOD) 40009, a lifting transformer (lift) 40010, a coefficient quantizer (quantize coefficients) 40011, and / or an arithmetic encoder (arithmetic encode) 40012.

[0086] The coordinate transformer 40000, the quantizer 40001, the octree analyzer 40002, the surface approximation analyzer 40003, the arithmetic encoder 40004, and the geometry reconstructor 40005 can perform geometry encoding. The geometry encoding according to an embodiment can include octree geometry encoding, direct encoding, trisoup geometry encoding, and entropy encoding. The direct encoding and the trisoup geometry encoding are selectively or in combination applied. The geometry encoding is not limited to the above-described examples.

[0087] As illustrated, the coordinate converter 40000 according to the embodiment receives a position and converts it into a coordinate. For example, the position can be converted into position information in a three-dimensional space (for example, a three-dimensional space represented by an XYZ coordinate system). The position information in the three-dimensional space according to the embodiment can be referred to as geometry information.

[0088] The quantizer 40001 according to the embodiment quantizes geometry. For example, the quantizer 40001 can quantize points based on minimum position values (for example, minimum values on each of the X, Y, and Z axes) of all points. The quantizer 40001 performs a quantization operation of multiplying a difference between the minimum position value and a position value of each point by a preset quantization scale value and then finding a nearest integer value by rounding the value obtained through the multiplication. Accordingly, one or more points can have the same quantized position (or position value). The quantizer 40001 according to the embodiment performs voxelization based on the quantized position to reconstruct quantized points. Voxelization refers to a minimum unit representing position information in a 3D space. Points of a point cloud content (or a 3D point cloud video) according to the embodiment can be included in one or more voxels. As a compound of volume and pixel, the term voxel refers to a 3D cubic space generated when a 3D space is divided into units (unit = 1.0) based on axes (for example, X, Y, and Z axes) representing the 3D space. The quantizer 40001 can match a group of points in the 3D space to a voxel. According to the embodiment, one voxel can include only one point. According to the embodiment, one voxel can include one or more points. In order to represent one voxel as one point, a position of a center point of the voxel can be set based on positions of one or more points included in the voxel. In this case, attributes of all positions included in one voxel can be combined and assigned to the voxel.

[0089] The octree analyzer 40002 according to the embodiment performs octree geometry encoding (or octree encoding) to present voxels in an octree structure. The octree structure represents points matched to voxels based on an octal tree structure.

[0090] The surface approximation analyzer 40003 according to the embodiment can analyze and approximate an octree. The octree analysis and approximation according to the embodiment is a process of analyzing a region including a plurality of points to efficiently provide an octree and voxelization.

[0091] The arithmetic encoder 40004 according to the embodiment performs entropy encoding on the octree and / or the approximated octree. For example, the encoding scheme includes arithmetic encoding. As a result of encoding, a geometry bitstream is generated.

[0092] The color transformer 40006, the attribute transformer 40007, the RAHT transformer 40008, the LOD generator 40009, the lifting transformer 40010, the coefficient quantizer 40011, and / or the arithmetic encoder 40012 perform attribute encoding. As described above, one point can have one or more attributes. The attribute encoding according to the embodiments is equally applied to the attributes that one point has. However, when an attribute (e.g., color) includes one or more elements, the attribute encoding is independently applied to each element. The attribute encoding according to the embodiments includes color transform encoding, attribute transform encoding, region-adaptive hierarchical transform (RAHT) encoding, interpolation-based hierarchical nearest neighbor prediction (prediction transform) encoding, and interpolation-based hierarchical nearest neighbor prediction encoding with an update / lifting step (lifting transform) encoding. The above-described RAHT encoding, prediction transform encoding, and lifting transform encoding can be selectively used according to the point cloud content, or a combination of one or more encoding schemes can be used. The attribute encoding according to the embodiments is not limited to the above-described examples.

[0093] The color transformer 40006 according to the embodiments performs color transform encoding that transforms a color value (or texture) included in an attribute. For example, the color transformer 40006 can transform the format of color information (e.g., from RGB to YCbCr). Alternatively, the operation of the color transformer 40006 according to the embodiments can be applied according to the color value included in the attribute.

[0094] The geometry reconstructor 40005 according to the embodiments reconstructs (decompresses) an octree and / or an approximate octree. The geometry reconstructor 40005 reconstructs an octree / voxel based on the result of analyzing the distribution of points. The reconstructed octree / voxel can be referred to as reconstructed geometry (restored geometry).

[0095] The attribute transformer 40007 according to the embodiments performs attribute transform to transform an attribute based on the reconstructed geometry and / or a position for which geometry encoding is not performed. As described above, since an attribute is dependent on geometry, the attribute transformer 40007 can transform an attribute based on the reconstructed geometry information. For example, based on the position value of a point included in a voxel, the attribute transformer 40007 can transform the attribute of the point at the position. As described above, when the center position of a voxel is set based on the positions of one or more points included in the voxel, the attribute transformer 40007 transforms the attributes of the one or more points. When trisoup geometry encoding is performed, the attribute transformer 40007 can transform an attribute based on the trisoup geometry encoding.

[0096] The attribute transformer 40007 can perform an attribute transform by calculating an average of attributes or attribute values (e.g., colors or reflectances of respective points) of neighboring points within a certain position / radius from a center position (or position value) of each voxel. The attribute transformer 40007 can apply a weight according to a distance from the center to each point when calculating the average. Accordingly, each voxel has a position and a calculated attribute (or attribute value).

[0097] The attribute transformer 40007 can search for neighboring points existing within a certain position / radius from a center position of each voxel based on a K-D tree or a Morton code. The K-D tree is a binary search tree and supports a data structure capable of managing points based on positions, so that a nearest neighbor search (NNS) can be quickly performed. The Morton code is generated by presenting coordinates (e.g., (x, y, z)) representing 3D positions of all points as bit values and mixing the bits. For example, when the coordinates representing the positions of points are (5, 9, 1), the bit values of the coordinates are (0101, 1001, 0001). The bit values are mixed according to bit indexes in the order of z, y, and x to produce 010001000111. This value is represented as a decimal number 1095. That is, the Morton code value of the point having the coordinates (5, 9, 1) is 1095. The attribute transformer 40007 can sort points based on the Morton code values and perform an NNS through a depth-first traversal process. The K-D tree or the Morton code is used when an NNS is required in another transform process for attribute encoding after the attribute transform operation.

[0098] As illustrated, the transformed attribute is input to the RAHT transformer 40008 and / or the LOD generator 40009.

[0099] The RAHT transformer 40008 according to an embodiment performs RAHT encoding for predicting attribute information based on reconstructed geometry information. For example, the RAHT transformer 40008 can predict attribute information of a node at a higher level in an octree based on attribute information associated with a node at a lower level in the octree.

[0100] The LOD generator 40009 according to an embodiment generates a level of detail (LOD). The LOD according to an embodiment is a degree of detail of point cloud content. As an LOD value decreases, a degree of detail of the point cloud content is indicated to deteriorate. As an LOD value increases, a degree of detail of the point cloud content is indicated to enhance. Points can be classified by LOD.

[0101] The lifting transformer 40010 according to an embodiment performs lifting transform encoding that transforms point cloud attributes based on weights. As described above, the lifting transform encoding can be optionally applied.

[0102] The coefficient quantizer 40011 according to an embodiment quantizes attributes of attribute encoding based on coefficients.

[0103] The arithmetic encoder 40012 according to the embodiment encodes the quantized attribute based on arithmetic encoding.

[0104] Although not shown in the drawings, Figure 4 The elements of the point cloud video encoder of the above-described Figure 4 may be implemented by hardware, software, firmware, or a combination thereof including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud content providing apparatus. The one or more processors can perform at least one of the operations and / or functions of the elements of the point cloud video encoder of the above-described Figure 4 Additionally, the one or more processors can operate or execute a software program and / or a set of instructions for performing the operations and / or functions of the elements of the point cloud video encoder of the above-described

[0105] Figure 5 An example of a voxel according to the embodiment is illustrated.

[0106] Figure 5 A voxel located in a 3D space represented by a coordinate system composed of three axes (X-axis, Y-axis, and Z-axis) is illustrated. As described with reference to Figure 4 The point cloud video encoder (e.g., quantizer 40001) can perform voxelization. A voxel refers to a 3D cubic space generated when a 3D space is divided into units (unit = 1.0) based on axes (e.g., X-axis, Y-axis, and Z-axis) representing the 3D space. Figure 5 An example of a voxel generated through an octree structure in which a cubic axis-aligned bounding box defined by two extreme points (0, 0, 0) and (2 d ,2 d ,2 d ) is recursively subdivided is illustrated. One voxel includes at least one point. The spatial coordinates of the voxel can be estimated from the positional relationship with a group of voxels. As described above, a voxel has attributes (e.g., color or reflectance) similar to pixels of a 2D image / video. Details of the voxel are the same as those described with reference to Figure 4 and thus a description thereof is omitted.

[0107] Figure 6 An example of an octree and an occupancy code according to the embodiment is illustrated.

[0108] As described with reference to Figure 1-4As described, the point cloud content delivery system (point cloud video encoder 10002) or the octree analyzer 40002 of the point cloud video encoder performs octree geometric coding (or octree coding) based on an octree structure to efficiently manage the regions and / or locations of voxels.

[0109] Figure 6 The upper part shows an octree structure. The 3D space of the point cloud content according to the embodiment is represented by the axes of a coordinate system (e.g., the X, Y, and Z axes). This is achieved by using two poles (0,0,0) and (2... d ,2 d ,2 d An octree structure is created by recursively subdividing the bounding box with the cubic axis aligned to the bounding box. Here, 2 d This can be set to the value of the minimum bounding box that constitutes all points surrounding the point cloud content (or point cloud video). Here, d represents the depth of the octree. The value of d is determined in the following formula. In the following formula, (x int n ,y int n ,z int n ) indicates the position (or position value) of the quantized point.

[0110]

[0111] like Figure 6 As shown in the upper center, the entire 3D space can be divided into eight spaces. Each divided space is represented by a cube with six faces. (See diagram below.) Figure 6 As shown in the upper right, each of the eight spaces is further subdivided based on the coordinate system axes (e.g., the X, Y, and Z axes). Thus, each space is divided into eight smaller spaces. These smaller spaces are also represented by cubes with six faces. This partitioning scheme is applied until the leaf nodes of the octree become voxels.

[0112] Figure 6 The lower part shows the octree occupancy code. The occupancy code generates the octree to indicate whether each of the eight partitions generated by dividing a space includes at least one node. Therefore, a single occupancy code is represented by eight child nodes. Each child node represents the occupancy of a partitioned space, and each child node has a 1-bit value. Therefore, the occupancy code is represented as an 8-bit code. That is, when the space corresponding to a child node includes at least one node, the node is assigned a value of 1. When the space corresponding to a child node does not include any node (the space is empty), the node is assigned a value of 0. Since... Figure 6The occupancy code shown is 00100001, so it indicates that each of the spaces corresponding to the third and eighth child nodes among the eight child nodes includes at least one point. As shown, each of the third and eighth child nodes has eight child nodes, and the child nodes are represented by 8-bit occupancy codes. The accompanying drawing shows that the occupancy code of the third child node is 10000111, and the occupancy code of the eighth child node is 01001111. A point cloud video encoder (e.g., the arithmetic encoder 40004) according to the embodiments can perform entropy encoding on the occupancy codes. To increase compression efficiency, the point cloud video encoder can perform intra / inter encoding on the occupancy codes. A receiving device (e.g., the receiving device 10004 or the point cloud video decoder 10006) according to the embodiments reconstructs the octree based on the occupancy codes.

[0113] A point cloud video encoder (e.g., the octree analyzer 40002) according to the embodiments can perform voxelization and octree encoding to store point positions. However, points are not always uniformly distributed in 3D space, so there can be a certain region in which fewer points exist. Therefore, it is inefficient to perform voxelization on the entire 3D space. For example, when a certain region includes very few points, voxelization does not need to be performed in the certain region.

[0114] Therefore, for the above certain region (or a node other than a leaf node of the octree), a point cloud video encoder according to the embodiments can skip voxelization and perform direct encoding to directly encode point positions included in the certain region. The coordinates of the points directly encoded according to the embodiments are referred to as direct coding mode (DCM). A point cloud video encoder according to the embodiments can also perform trisoup geometry encoding based on a surface model, which is to reconstruct point positions in the certain region (or node) based on voxels. Trisoup geometry encoding is a geometry encoding that represents an object as a series of triangular meshes. Therefore, a point cloud video decoder can generate a point cloud from a mesh surface. Direct encoding and trisoup geometry encoding according to the embodiments can be selectively performed. In addition, direct encoding and trisoup geometry encoding according to the embodiments can be performed in combination with octree geometry encoding (or octree encoding).

[0115] To perform direct encoding, an option to use a direct mode to apply direct encoding should be enabled. The node to which direct encoding is to be applied is not a leaf node, and there should be fewer points than a threshold within the certain node. In addition, the total number of points to which direct encoding is to be applied should not exceed a preset threshold. When the above conditions are satisfied, a point cloud video encoder (or the arithmetic encoder 40004) according to the embodiments can perform entropy encoding on point positions (or position values).

[0116] A point cloud video encoder (e.g., surface approximation analyzer 40003) according to embodiments can determine a certain level of an octree (a level less than the depth d of the octree), and can start using a surface model from the level to perform trisoup geometry coding to reconstruct point positions in a node region based on voxels (Trisoup mode). A point cloud video encoder according to embodiments can specify a level to which trisoup geometry coding is to be applied. For example, when the certain level is equal to the depth of the octree, the point cloud video encoder does not operate in the trisoup mode. In other words, only when the specified level is less than the depth value of the octree, a point cloud video encoder according to embodiments can operate in the trisoup mode. A 3D cubical region of a node according to embodiments of the specified level is referred to as a block. One block can include one or more voxels. A block or a voxel can correspond to a brick. Geometry is represented as surfaces within individual blocks. A surface according to embodiments can intersect at most once with each edge of a block.

[0117] One block has 12 edges, and thus there are at least 12 intersection points in one block. Each intersection point is referred to as a vertex. When there is at least one occupied voxel adjacent to an edge among all blocks sharing the edge, a vertex existing along the edge is detected. An occupied voxel according to embodiments refers to a voxel including a point. A vertex position detected along an edge is an average position of edges of all voxels adjacent to the edge among all blocks sharing the edge.

[0118] Once a vertex is detected, a point cloud video encoder according to embodiments can perform entropy coding on a start point of an edge (x, y, z), a direction vector of an edge (Δx, Δy, Δz), and a vertex position value (a relative position value within an edge). When trisoup geometry coding is applied, a point cloud video encoder according to embodiments (e.g., geometry reconstructor 40005) can generate a restored geometry (reconstructed geometry) by performing a triangle reconstruction, an upsampling, and a voxelization process.

[0119] A vertex located at an edge of a block determines a surface passing through the block. A surface according to embodiments is a non-planar polygon. In a triangle reconstruction process, a surface represented by a triangle is reconstructed based on a start point of an edge, a direction vector of an edge, and a position value of a vertex. The triangle reconstruction process is performed as follows: ① a centroid value of each vertex is calculated, ② a center value is subtracted from each vertex value, and ③ a sum of squares of values obtained by the subtraction is estimated.

[0120]

[0121] Then, the minimum of the sum is estimated, and a projection process is performed according to the axis having the minimum value. For example, when the element x is the smallest, the respective vertices are projected on the x-axis with respect to the center of the block, and on the (y, z) plane. When the value obtained by the projection on the (y, z) plane is (ai, bi), the value of θ is estimated by atan2(bi, ai), and the vertices are ordered based on the value of θ. The following table shows vertex combinations creating triangles according to the number of vertices. The vertices are ordered from 1 to n. The following table shows that for 4 vertices, two triangles can be constructed according to the vertex combinations. The first triangle can consist of vertices 1, 2, and 3 among the ordered vertices, and the second triangle can consist of vertices 3, 4, and 1 among the ordered vertices.

[0122] Table 2-1. Triangles formed from vertices ordered by 1, …, n

[0123] n Triangles

[0124] 3 (1,2,3)

[0125] 4 (1,2,3), (3,4,1)

[0126] 5 (1,2,3), (3,4,5), (5,1,3)

[0127] 6 (1,2,3), (3,4,5), (5,6,1), (1,3,5)

[0128] 7 (1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)

[0129] 8 (1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1)

[0130] 9 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7,9,3)

[0131] 10 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,1), (1,3,5), (5,7,9), (9,1,5)

[0132] 11 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,1,3), (3,5,7), (7,9,11), (11,3,7)

[0133] 12 (1,2,3),(3,4,5),(5,6,7),(7,8,9),(9,10,11),(11,12,1),(1,3,5),(5,7,9),(9,11,1),(1,5,9)

[0134] An upsampling process is performed to add points along the edges of the triangle at the center, and voxelization is then performed. The added points are generated based on the upsampling factor and the width of the block. These added points are called thinned vertices. A point cloud video encoder according to an implementation can voxelize the thinned vertices. Additionally, the point cloud video encoder can perform attribute encoding based on the voxelized positions (or position values).

[0135] Figure 7 An example of a neighboring node pattern according to an implementation method is shown.

[0136] To increase the compression efficiency of point cloud videos, the point cloud video encoder according to the implementation method can perform entropy coding based on context-adaptive arithmetic coding.

[0137] For reference Figure 1-6 As described, Figure 1 The point cloud content providing system or point cloud video encoder or Figure 4 The point cloud video encoder or arithmetic encoder 40004 can immediately perform entropy coding on the occupancy code. Alternatively, the point cloud content providing system or point cloud video encoder can perform entropy coding (intra-frame coding) based on the occupancy code of the current node and the occupancy of neighboring nodes, or entropy coding (inter-frame coding) based on the occupancy code of a previous frame. According to the embodiment, a frame represents a collection of simultaneously generated point cloud videos. The compression efficiency of the intra-frame coding / inter-frame coding according to the embodiment can depend on the number of neighboring nodes referenced. As the number of bits increases, the computation becomes more complex, but the coding can be biased to one side, which can increase compression efficiency. For example, when given a 3-bit context, 2... 3 There are 8 methods to perform the encoding. The division of the encoding affects the implementation complexity. Therefore, it is necessary to achieve an appropriate level of compression efficiency and complexity.

[0138] Figure 7 This illustrates the process of obtaining an occupancy pattern based on the occupancy of neighboring nodes. A point cloud video encoder according to an embodiment determines the occupancy of neighboring nodes for each node in an octree and obtains the value of the neighbor pattern. The neighbor pattern is used to infer the occupancy pattern of a node. Figure 7 The upper part shows the cube corresponding to the node (the cube in the middle) and six cubes that share at least one face with this cube (neighboring nodes). The nodes shown in the figure are nodes of the same depth. The numbers shown in the figure represent the weights associated with the six nodes (1, 2, 4, 8, 16, and 32). Weights are assigned sequentially according to the position of the neighboring nodes.

[0139] Figure 7 The lower part of the figure shows the adjacent node pattern value. The adjacent node pattern value is the sum of the values multiplied by the weight of the adjacent node (adjacent node with a point) that is occupied. Thus, the adjacent node pattern value is 0 to 63. When the adjacent node pattern value is 0, it indicates that there is no node with a point (no occupied node) among the adjacent nodes of the node. When the adjacent node pattern value is 63, it indicates that all the adjacent nodes are occupied nodes. As shown in the figure, since the adjacent nodes assigned with the weights 1, 2, 4, and 8 are occupied nodes, the adjacent node pattern value is 15 (sum of 1, 2, 4, and 8). The point cloud video encoder can perform encoding according to the adjacent node pattern value (for example, when the adjacent node pattern value is 63, 64 types of encoding can be performed). According to the embodiment, the point cloud video encoder can reduce the encoding complexity by changing the adjacent node pattern value (for example, based on a table that changes 64 to 10 or 6).

[0140] Figure 8 An example of point configuration in each LOD according to the embodiment is shown.

[0141] As described with reference to Figure 1-7 , the encoded geometry is reconstructed (decompressed) before performing attribute encoding. When direct encoding is applied, the geometry reconstruction operation can include changing the placement of the directly encoded points (for example, placing the directly encoded points in front of the point cloud data). When trisoup geometry encoding is applied, the geometry reconstruction process is performed by triangle reconstruction, upsampling, and voxelization. Since the attribute is dependent on the geometry, the attribute encoding is performed based on the reconstructed geometry.

[0142] The point cloud video encoder (for example, the LOD generator 40009) can classify (reorganize) the points by LOD. The figure shows the point cloud content corresponding to the LOD. The leftmost picture in the figure represents the original point cloud content. The second picture from the left in the figure represents the point distribution in the lowest LOD, and the rightmost picture in the figure represents the point distribution in the highest LOD. That is, the points are sparsely distributed in the lowest LOD, while the points are densely distributed in the highest LOD. That is, as the LOD increases in the direction indicated by the arrow at the bottom of the figure, the space (or distance) between the points becomes narrower.

[0143] Figure 9 An example of point configuration for each LOD according to the embodiment is shown.

[0144] As described with reference to Figure 1-8 , the point cloud content providing system or the point cloud video encoder (for example, Figure 1 the point cloud video encoder 10002 of Figure 4The point cloud video encoder or LOD generator 40009) can generate LODs. LODs are generated by reorganizing 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 not only performed by the point cloud video encoder but also by the point cloud video decoder.

[0145] Figure 9 The upper part of FIG. 4 shows examples of points (P0 to P9) of a point cloud content distributed in a 3D space. In Figure 9 , the original order indicates the order of points P0 to P9 before LOD generation. In Figure 9 , the LOD-based order indicates the order of points according to LOD generation. Points are reorganized by LOD. In addition, a high LOD includes points belonging to a lower LOD. As Figure 9 shown, LOD 0 includes P0, P5, P4, and P2. LOD1 includes the points of LOD 0, P1, P6, and P3. LOD2 includes the points of LOD 0, the points of LOD1, P9, P8, and P7.

[0146] As described with reference to Figure 4 , the point cloud video encoder according to the embodiments can selectively or in combination perform the LOD-based predictive transform coding, the LOD-based lifting transform coding, and the RAHT transform coding.

[0147] The point cloud video encoder according to the embodiments can generate predictors for points to perform the LOD-based predictive transform coding for setting a prediction property (or a prediction property value) of each point. That is, N predictors can be generated for N points. The predictor according to the embodiments can calculate a weight (=1 / distance) based on an LOD value of each point, index information about neighboring points existing within a set distance of each LOD, and a distance to the neighboring points.

[0148] The prediction property (or property value) according to the embodiments is set to an average of values obtained by multiplying properties (or property values) (e.g., color, reflectance, etc.) of neighboring points set in the predictor of each point by a weight (or a weight value) calculated based on a distance to each neighboring point. The point cloud video encoder (e.g., the coefficient quantizer 40011) according to the embodiments can quantize and inverse quantize a residual (which can be referred to as a residual property, a residual property value, a property prediction residual value, or a prediction error property value, etc.) of each point obtained by subtracting the prediction property (or property value) of each point from a property (i.e., an original property value) of each point. The quantization process performed on the residual property value in the transmission device is configured as shown in the table. The inverse quantization process performed on the residual property value in the reception device is configured as shown in the table.

[0149] Table. Property prediction residual quantization pseudo code

[0150] int PCCQuantization(int value, int quantStep) {

[0151] if(value >= 0) {

[0152] return floor(value / quantStep + 1.0 / 3.0);

[0153] } else {

[0154] return floor(-value / quantStep + 1.0 / 3.0);

[0155] }

[0156] }

[0157] Table. Attribute prediction residual inverse quantization pseudo code

[0158] int PCCInverseQuantization(int value, int quantStep) {

[0159] if(quantStep == 0) {

[0160] return value;

[0161] } else {

[0162] return value*quantStep;

[0163] }

[0164] }

[0165] When the predictor of each point has neighboring points, the point cloud video encoder (e.g., the arithmetic encoder 40012) according to the embodiment can perform entropy encoding on the quantized and inverse quantized residual values as described above. When the predictor of each point does not have neighboring points, the point cloud video encoder (e.g., the arithmetic encoder 40012) according to the embodiment can perform entropy encoding on the attribute of the corresponding point without performing the above operation.

[0166] The point cloud video encoder (e.g., the lifting transformer 40010) according to the embodiment can generate the predictor of each point, set the calculated LOD and register the neighboring points in the predictor, and set the weight according to the distance to the neighboring points to perform the lifting transform encoding. The lifting transform encoding according to the embodiment is similar to the prediction transform encoding described above, but differs in that the weight is applied to the attribute value cumulatively. The process of applying the weight to the attribute value cumulatively according to the embodiment is configured as follows.

[0167] 1) Create an array quantization weight (QW) for storing weight values of respective points. All elements of the QW have an initial value of 1.0. Multiply the QW value of the predictor index of the neighboring node registered in the predictor by the weight of the predictor of the current point, and add the value obtained by the multiplication.

[0168] 2) Lift the prediction process: subtract the value obtained by multiplying the attribute value of the point by the weight from the existing attribute value to calculate the predicted attribute value.

[0169] 3) Create temporary arrays called updateweight and update, and initialize the temporary arrays to zero.

[0170] 4) Accumulate the weight calculated by multiplying the weight calculated for all predictors by the weight stored in the QW corresponding to the predictor index as the index of the neighboring node in the updateweight array. Accumulate the value obtained by multiplying the attribute value of the neighboring node index by the calculated weight in the update array.

[0171] 5) Lift the update process: divide the attribute value of the update array of all predictors by the weight value of the updateweight array of the predictor index, and add the existing attribute value to the value obtained by the division.

[0172] 6) Calculate the predicted attribute for all predictors by multiplying the attribute value updated by the lift update process by the weight (stored in the QW) updated by the lift prediction process. The point cloud video encoder (e.g., the coefficient quantizer 40011) according to the embodiment quantizes the predicted attribute value. In addition, the point cloud video encoder (e.g., the arithmetic encoder 40012) performs entropy encoding on the quantized attribute value.

[0173] The point cloud video encoder according to the embodiment (e.g., the RAHT transformer 40008) can perform RAHT transform encoding in which the attribute of a node at a lower level in the octree is used to predict the attribute of a node at a higher level. The RAHT transform encoding is an example of attribute intra-encoding by octree backward scanning. The point cloud video encoder according to the embodiment scans the entire region from a voxel and repeatedly performs a merging process of merging voxels into larger blocks at each step until the root node is reached. The merging process according to the embodiment is performed only on an occupied node. The merging process is not performed on an empty node. The merging process is performed on an upper node directly above the empty node.

[0174] The following equation represents the RAHT transform matrix. In the following equation, represents the average attribute value of the voxels at level l. can be calculated based on and ​and The weight is and

[0175]

[0176] here, It is a low-pass value and is used in the next higher-level merge process. This represents the high-pass coefficient. The high-pass coefficients at each step are quantized and subjected to entropy encoding (e.g., encoding by an arithmetic encoder 400012). The weights are calculated as follows: pass and Create the root node as follows.

[0177]

[0178] The value of gDC, like the high-pass coefficient, is also quantized and subjected to entropy encoding.

[0179] Figure 10 A point cloud video decoder according to an embodiment is shown.

[0180] Figure 10 The point cloud video decoder shown is Figure 1 The example of the point cloud video decoder 10006 described in [the document], and it can be executed with [the following]. Figure 1 The point cloud video decoder 10006 shown operates in the same or similar manner. As shown, the point cloud video decoder can receive a geometry bitstream and an attribute bitstream included in one or more bitstreams. The point cloud video decoder includes a geometry decoder and an attribute decoder. The geometry decoder performs geometry decoding on the geometry bitstream and outputs the decoded geometry. The attribute decoder performs attribute decoding on the attribute bitstream based on the decoded geometry and outputs the decoded attributes. The decoded geometry and decoded attributes are used to reconstruct the point cloud content (the decoded point cloud).

[0181] Figure 11 A point cloud video decoder according to an embodiment is shown.

[0182] Figure 11 The point cloud video decoder shown is Figure 10 The example shown is a point cloud video decoder that can perform decoding operations. Figure 1-9 The reverse processing of the encoding operation of the point cloud video encoder shown.

[0183] For reference Figure 1 and Figure 10 As described, the point cloud video decoder can perform geometric decoding and attribute decoding. Geometric decoding is performed before attribute decoding.

[0184] The point cloud video decoder according to the embodiments includes an arithmetic decoder (arithmetic-decoding) 11000, an octree synthesizer (synthesize octree) 11001, a surface approximation synthesizer (synthesize surface approximation) 11002 and a geometry reconstructor (reconstruct geometry) 11003, a coordinate inverse transformer (inverse-transform coordinate) 11004, an arithmetic decoder (arithmetic-decoding) 11005, an inverse quantizer (inverse quantization) 11006, a RAHT transformer 11007, an LOD generator (generate LOD) 11008, an inverse elevator (inverse-elevate) 11009, and / or a color inverse transformer (inverse-transform color) 11010.

[0185] The arithmetic decoder 11000, the octree synthesizer 11001, the surface approximation synthesizer 11002, the geometry reconstructor 11003, and the coordinate inverse transformer 11004 can perform geometry decoding. The geometry decoding according to the embodiments can include direct decoding and trisoup geometry decoding. The direct decoding and the trisoup geometry decoding are selectively applied. The geometry decoding is not limited to the above-described examples, and as a reference Figure 1-9 Inverse processing of the described geometry encoding is performed.

[0186] The arithmetic decoder 11000 according to the embodiments decodes a received geometry bitstream based on arithmetic encoding. The operation of the arithmetic decoder 11000 corresponds to inverse processing of the arithmetic encoder 40004.

[0187] The octree synthesizer 11001 according to the embodiments can generate an octree by acquiring an occupancy code (or information about geometry obtained as a decoding result) from the decoded geometry bitstream. The occupancy code is as described with reference to Figure 1-9 is configured as described in detail.

[0188] When the trisoup geometry encoding is applied, the surface approximation synthesizer 11002 according to the embodiments can synthesize a surface based on the decoded geometry and / or the generated octree.

[0189] The geometry reconstructor 11003 according to the embodiments can regenerate geometry based on the surface and / or the decoded geometry. As described with reference to Figure 1-9 The direct encoding and the trisoup geometry encoding are selectively applied as described. Accordingly, the geometry reconstructor 11003 directly imports and adds position information about points to which the direct encoding is applied. When the trisoup geometry encoding is applied, the geometry reconstructor 11003 can reconstruct geometry by performing the reconstruction operation (e.g., triangle reconstruction, upsampling, and voxelization) of the geometry reconstructor 40005. Details are the same as described with reference to Figure 6 are described, and thus a description thereof is omitted. The reconstructed geometry can include a point cloud picture or frame that does not include attributes.

[0190] The coordinate inverse transformer 11004 according to the embodiment can acquire point positions by reconstructing a geometry transform coordinate based on the reconstructed geometry transform coordinate.

[0191] The arithmetic decoder 11005, the inverse quantizer 11006, the RAHT transformer 11007, the LOD generator 11008, the inverse upscaler 11009, and / or the color inverse transformer 11010 can perform decoding with reference to the attribute encoding described above. Figure 10 The attribute decoding described above. The attribute decoding according to the embodiment includes a region-adaptive hierarchical transform (RAHT) decoding, an interpolation-based hierarchical nearest neighbor prediction (prediction transform) decoding, and an interpolation-based hierarchical nearest neighbor prediction decoding with an update / upscale step (upscale transform). The above three decoding schemes can be selectively used, or a combination of one or more decoding schemes can be used. The attribute decoding according to the embodiment is not limited to the above-described examples.

[0192] The arithmetic decoder 11005 according to the embodiment decodes an attribute bitstream by arithmetic encoding.

[0193] The inverse quantizer 11006 according to the embodiment inverse quantizes information on a decoded attribute bitstream or an attribute acquired as a decoding result, and outputs an inverse quantized attribute (or attribute value). The inverse quantization can be selectively applied based on attribute encoding of the point cloud video encoder.

[0194] According to the embodiment, the RAHT transformer 11007, the LOD generator 11008, and / or the inverse upscaler 11009 can process the reconstructed geometry and the inverse quantized attribute. As described above, the RAHT transformer 11007, the LOD generator 11008, and / or the inverse upscaler 11009 can selectively perform a decoding operation corresponding to encoding of the point cloud video encoder.

[0195] The color inverse transformer 11010 according to the embodiment performs inverse transform encoding to inverse transform a color value (or texture) included in a decoded attribute. The operation of the color inverse transformer 11010 can be selectively performed based on the operation of the color transformer 40006 of the point cloud video encoder.

[0196] Although not shown in the drawing, Figure 11 The elements of the point cloud video decoder can be implemented by hardware, software, firmware, or a combination thereof including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud content providing apparatus. The one or more processors can perform at least one or more of the operations and / or functions of the elements of the point cloud video decoder described above. Figure 11 In addition, the one or more processors can operate or execute a software program and / or a set of instructions for performing the operations and / or functions of the elements of the point cloud video decoder described above. Figure 11 In addition, the one or more processors can operate or execute a software program and / or a set of instructions for performing the operations and / or functions of the elements of the point cloud video decoder described above.

[0197] Figure 12 A transmitting device according to an embodiment is shown.

[0198] Figure 12 The transmitting device shown is Figure 1 The transmitting device 10000 (or Figure 4 An example of a point cloud video encoder. Figure 12 The transmitting device shown can perform and reference Figure 1-9 The described point cloud video encoder includes one or more of the same or similar operations and methods. The transmitting apparatus according to the embodiment may include a data input unit 12000, a quantization processor 12001, a voxelization processor 12002, an octree occupancy code generator 12003, a surface model processor 12004, an intra / inter-frame coding processor 12005, an arithmetic encoder 12006, a metadata processor 12007, a color transformation processor 12008, an attribute transformation processor 12009, a prediction / boosting / RAHT transformation processor 12010, an arithmetic encoder 12011, and / or a transmission processor 12012.

[0199] According to the embodiment, the data input unit 12000 receives or acquires point cloud data. The data input unit 12000 can perform operations and / or acquisition methods similar to those of the point cloud video acquisition unit 10001 (or refer to...). Figure 2 The described acquisition process (20000) is the same as or similar to the operation and / or acquisition method.

[0200] The data input unit 12000, quantization processor 12001, voxelization processor 12002, octree occupancy code generator 12003, surface model processor 12004, intra / inter-frame coding processor 12005, and arithmetic encoder 12006 perform geometric coding. Geometric coding according to the implementation method and reference. Figure 1-9 The described geometric codes are the same or similar, and therefore their detailed descriptions are omitted.

[0201] The quantization processor 12001 according to the embodiment quantizes geometry (e.g., point position values). The operation of the quantization processor 12001 and / or the quantization with reference... Figure 4 The operation and / or quantization of the described quantizer 40001 are the same or similar. Details and references Figure 1-9 The descriptions are the same.

[0202] According to the embodiment, the voxelization processor 12002 voxels the quantized position values ​​of points. The voxelization processor 120002 can execute and reference... Figure 4 The operation and / or voxelization process of the described quantizer 40001 is the same as or similar to the operation and / or process. Details and referencesFigure 1-9 The same as described.

[0203] The octree occupancy code generator 12003 according to the embodiment performs octree encoding based on the voxelization of the positions of the points based on the octree structure. The octree occupancy code generator 12003 can generate an occupancy code. The octree occupancy code generator 12003 can perform the same or similar operations and / or methods as the octree analyzer 40002. Figure 4 and Figure 6 The same or similar operations and / or methods as described for the operation of the point cloud video encoder (or the octree analyzer 40002). Details are referred to Figure 1-9 The same as described.

[0204] The surface model processor 12004 according to the embodiment can perform trisoup geometry encoding based on a surface model to reconstruct the positions of the points in a certain region (or node) based on voxels. The surface model processor 12004 can perform the same or similar operations and / or methods as the surface approximation analyzer 40003. Figure 4 The same or similar operations and / or methods as described for the operation of the point cloud video encoder (e.g., the surface approximation analyzer 40003). Details are referred to Figure 1-9 The same as described.

[0205] The intra / inter encoding processor 12005 according to the embodiment can perform intra / inter encoding on the point cloud data. The intra / inter encoding processor 12005 can perform the same or similar encoding as the intra / inter encoder 40005. Details are referred to Figure 7 The same or similar encoding as described for the intra / inter encoding. Details are referred to Figure 7 The same as described. According to the embodiment, the intra / inter encoding processor 12005 can be included in the arithmetic encoder 12006.

[0206] The arithmetic encoder 12006 according to the embodiment performs entropy encoding on the octree and / or the approximated octree of the point cloud data. For example, the encoding scheme includes arithmetic encoding. The arithmetic encoder 12006 performs the same or similar operations and / or methods as the arithmetic encoder 40004.

[0207] The metadata processor 12007 according to the embodiment processes metadata (e.g., setting values) on the point cloud data and provides it to necessary processes such as geometry encoding and / or attribute encoding. In addition, the metadata processor 12007 according to the embodiment can generate and / or process signaling information related to the geometry encoding and / or the attribute encoding. The signaling information according to the embodiment can be separately encoded from the geometry encoding and / or the attribute encoding. The signaling information according to the embodiment can be interleaved.

[0208] The color transform processor 12008, the attribute transform processor 12009, the prediction / lifting / RAHT transform processor 12010, and the arithmetic encoder 12011 perform attribute encoding. The attribute encoding according to the embodiments is the same as or similar to that described with reference to Figure 1-9 The attribute encoding according to the embodiments is the same as or similar to that described with reference to

[0209] The color transform processor 12008 according to the embodiments performs color transform encoding to transform color values included in the attributes. The color transform processor 12008 can perform the color transform encoding based on the reconstructed geometry. The reconstructed geometry is the same as or similar to that described with reference to Figure 1-9 The color transform processor 12008 according to the embodiments performs color transform encoding to transform color values included in the attributes. The color transform processor 12008 can perform the color transform encoding based on the reconstructed geometry. The reconstructed geometry is the same as or similar to that described with reference to Figure 4 The color transform processor 12008 according to the embodiments performs color transform encoding to transform color values included in the attributes. The color transform processor 12008 can perform the color transform encoding based on the reconstructed geometry. The reconstructed geometry is the same as or similar to that described with reference to

[0210] The attribute transform processor 12009 according to the embodiments performs attribute transform to transform the attributes based on the reconstructed geometry and / or positions for which geometry encoding is not performed. The attribute transform processor 12009 performs the same or similar operations and / or methods as those of the attribute transformer 40007 described with reference to Figure 4 The attribute transform processor 12009 according to the embodiments performs attribute transform to transform the attributes based on the reconstructed geometry and / or positions for which geometry encoding is not performed. The attribute transform processor 12009 performs the same or similar operations and / or methods as those of the attribute transformer 40007 described with reference to Figure 4 The attribute transform processor 12009 according to the embodiments performs attribute transform to transform the attributes based on the reconstructed geometry and / or positions for which geometry encoding is not performed. The attribute transform processor 12009 performs the same or similar operations and / or methods as those of the attribute transformer 40007 described with reference to Figure 1-9 The attribute transform processor 12009 according to the embodiments performs attribute transform to transform the attributes based on the reconstructed geometry and / or positions for which geometry encoding is not performed. The attribute transform processor 12009 performs the same or similar operations and / or methods as those of the attribute transformer 40007 described with reference to

[0211] The arithmetic encoder 12011 according to the embodiments can encode the encoded attributes based on arithmetic encoding. The arithmetic encoder 12011 performs the same or similar operations and / or methods as those of the arithmetic encoder 400012.

[0212] The transmission processor 12012 according to the embodiment can transmit individual bitstreams including the encoded geometry and / or the encoded attribute and metadata information, or transmit one bitstream configured with the encoded geometry and / or the encoded attribute and metadata information. When the encoded geometry and / or the encoded attribute and metadata information according to the embodiment are configured into one bitstream, the bitstream can include one or more sub-bitstreams. The bitstream according to the embodiment can include signaling information and slice data, the signaling information including a sequence parameter set (SPS) for sequence level signaling, a geometry parameter set (GPS) for geometry information encoding signaling, an attribute parameter set (APS) for attribute information encoding signaling, and a tile parameter set (TPS or tile manifest) for tile level signaling. The slice data can include information about one or more slices. One slice according to the embodiment can include one geometry bitstream Geom0 0 and one or more attribute bitstreams Attr0 0 and Attr1 0 The TPS according to the embodiment can include information about individual tiles in one or more tiles (e.g., coordinate information about a bounding box and height / size information). The geometry bitstream can include a header and a payload. The header of the geometry bitstream according to the embodiment can include a parameter set identifier (geom_parameter_set_id) included in the GPS, a tile identifier (geom_tile_id), and a slice identifier (geom_slice_id), and information about data included in the payload. As described above, the metadata processor 12007 according to the embodiment can generate and / or process the signaling information and transmit it to the transmission processor 12012. According to the embodiment, the element performing geometry encoding and the element performing attribute encoding can share data / information with each other as indicated by dotted lines. The transmission processor 12012 according to the embodiment can perform the same or similar operations and / or transmission methods as those of the transmitter 10003. Details are the same as those described with reference to Figure 1 and Figure 2 and thus a description thereof is omitted.

[0213] Figure 13 A receiving apparatus according to the embodiment is illustrated.

[0214] Figure 13 The illustrated receiving apparatus is an example of a receiving apparatus 10004 (or Figure 1 a point cloud video decoder) of Figure 10 and Figure 11 Figure 13 The illustrated receiving apparatus can perform one or more operations and methods the same as or similar to those of the point cloud video decoder described with reference to Figure 1-11 and thus a description thereof is omitted. ​

[0215] The reception apparatus according to the embodiment includes a receiver 13000, a reception processor 13001, an arithmetic decoder 13002, an occupancy code-based octree reconstruction processor 13003, a surface model processor (triangle reconstruction, upsampling, voxelization) 13004, an inverse quantization processor 13005, a metadata parser 13006, an arithmetic decoder 13007, an inverse quantization processor 13008, a prediction / lifting / RAHT inverse transform processor 13009, a color inverse transform processor 13010, and / or a Tenderer 13011. Each decoding element according to the embodiment can perform inverse processing of the operation of the corresponding encoding element according to the embodiment.

[0216] The receiver 13000 according to the embodiment receives point cloud data. The receiver 13000 can perform the same or similar operation and / or reception method as the operation and / or reception method of the receiver 10005 of Figure 1 The detailed description thereof is omitted.

[0217] The reception processor 13001 according to the embodiment can acquire a geometry bitstream and / or an attribute bitstream from the received data. The reception processor 13001 can be included in the receiver 13000.

[0218] The arithmetic decoder 13002, the occupancy code-based octree reconstruction processor 13003, the surface model processor 13004, and the inverse quantization processor 13005 can perform geometry decoding. The geometry decoding according to the embodiment is the same as or similar to the geometry decoding described with reference to Figure 1-10 The detailed description thereof is omitted.

[0219] The arithmetic decoder 13002 according to the embodiment can decode the geometry bitstream based on arithmetic encoding. The arithmetic decoder 13002 performs the same or similar operation and / or encoding as the operation and / or encoding of the arithmetic decoder 11000.

[0220] The occupancy code-based octree reconstruction processor 13003 according to the embodiment can reconstruct an octree by acquiring an occupancy code from the decoded geometry bitstream (or information on the geometry acquired as a decoding result). The occupancy code-based octree reconstruction processor 13003 performs the same or similar operation and / or method as the operation and / or octree generation method of the octree synthesizer 11001. When the trisoup geometry encoding is applied, the surface model processor 13004 according to the embodiment can perform trisoup geometry decoding and related geometry reconstruction (e.g., triangle reconstruction, upsampling, voxelization) based on a surface model method. The surface model processor 13004 performs the same or similar operation as the operation of the surface approximation synthesizer 11002 and / or the geometry reconstructor 11003.

[0221] The inverse quantization processor 13005 according to the embodiments can inverse quantize the decoded geometry.

[0222] The metadata parser 13006 according to the embodiments can parse metadata (e.g., setting values) included in the received point cloud data. The metadata parser 13006 can pass the metadata to the geometry decoding and / or attribute decoding. The metadata is the same as described with reference to Figure 12 The attribute decoding described is the same or similar, and thus a detailed description thereof is omitted.

[0223] The arithmetic decoder 13007, the inverse quantization processor 13008, the prediction / lifting / RAHT inverse transformer 13009, and the color inverse transformer 13010 perform attribute decoding. The attribute decoding is the same or similar to that described with reference to Figure 1-10 The attribute decoding described is the same or similar, and thus a detailed description thereof is omitted.

[0224] The arithmetic decoder 13007 according to the embodiments can decode the attribute bitstream by arithmetic encoding. The arithmetic decoder 13007 can decode the attribute bitstream based on the reconstructed geometry. The arithmetic decoder 13007 performs the same or similar operations and / or encoding as those of the arithmetic decoder 11005.

[0225] The inverse quantization processor 13008 according to the embodiments can inverse quantize the decoded attribute bitstream. The inverse quantization processor 13008 performs the same or similar operations and / or methods as those of the inverse quantizer 11006.

[0226] The prediction / lifting / RAHT inverse transformer 13009 according to the embodiments can process the reconstructed geometry and the inverse quantized attribute. The prediction / lifting / RAHT inverse transformer 13009 performs the same or similar one or more operations and / or decoding as those of the RAHT transformer 11007, the LOD generator 11008, and / or the inverse lifter 11009. The color inverse transformer 13010 according to the embodiments performs inverse transform encoding to inverse transform color values (or textures) included in the decoded attribute. The color inverse transformer 13010 performs the same or similar operations and / or inverse transform encoding as those of the color inverse transformer 11010. The Tenderer 13011 according to the embodiments can render the point cloud data.

[0227] Figure 14 An architecture for G-PCC based point cloud content streaming according to the embodiments is shown.

[0228] Figure 14 The upper part of FIG. 13 shows Figure 1-13The described transmission apparatus (e.g., the transmission apparatus 10000, Figure 12 handles and transmits the processing of the point cloud content.

[0229] As described with reference to Figure 1-13 , the transmission apparatus can acquire audio Ba of the point cloud content (audio acquisition), encode the acquired audio (audio encoding), and output an audio bitstream Ea. In addition, the transmission apparatus can acquire a point cloud (or a point cloud video) Bv of the point cloud content (point acquisition), and perform point cloud video encoding on the acquired point cloud to output a point cloud video bitstream Ev. The point cloud video encoding of the transmission apparatus is the same as or similar to the encoding of the point cloud video encoder described with reference to Figure 1-13 The described point cloud video encoding (e.g., Figure 4 of the point cloud video encoder, and thus a detailed description thereof will be omitted.

[0230] The transmission apparatus can encapsulate the generated audio bitstream and video bitstream into a file and / or a segment (file / segment encapsulation). The encapsulated file and / or segment Fsfile can include a file in a file format such as ISOBMFF or Dynamic Adaptive Streaming over HTTP (DASH) segments. The point cloud related metadata according to the embodiments can be contained in the encapsulated file format and / or segment. The metadata can be contained in boxes at various levels of the ISO International Organization for Standardization Base Media File Format (ISOBMFF) file format, or can be contained in a separate track within the file. According to the embodiments, the transmission apparatus can encapsulate the metadata into a separate file. The transmission apparatus according to the embodiments can deliver the encapsulated file format and / or segment over a network. The processing method in which the transmission apparatus encapsulates and transmits is the same as the processing method described with reference to Figure 1-13 the transmission step 20002, Figure 2 of the transmitter 10003, and thus a detailed description thereof will be omitted.

[0231] Figure 14 The lower part of FIG. 2 shows the processing of the reception apparatus described with reference to Figure 1-13 The described reception apparatus (e.g., the reception apparatus 10004, Figure 13 handles and outputs the processing of the point cloud content.

[0232] According to the embodiments, the reception apparatus can include an apparatus configured to output final audio data and final video data (e.g., a speaker, a headphone, a display) and a point cloud player configured to process the point cloud content (point cloud player). The final data output apparatus and the point cloud player can be configured as separate physical apparatuses. The point cloud player according to the embodiments can perform geometry-based point cloud compression (G-PCC) encoding, video-based point cloud compression (V-PCC) encoding, and / or next generation encoding.

[0233] The reception apparatus according to the embodiments can acquire and de-encapsulate (file / segment de-encapsulation) the file and / or segment F', Fs' contained in the received data (e.g., broadcast signal, signal transmitted through a network, etc.). The reception and de-encapsulation methods of the reception apparatus are the same as described with reference to Figure 1-13 The same as described (e.g., receiver 10005, reception unit 13000, reception processing unit 13001, etc.) and thus the detailed description thereof will be omitted.

[0234] The reception apparatus according to the embodiments acquires the audio bitstream E'a and the video bitstream E'v contained in the file and / or segment. As illustrated, the reception apparatus outputs the decoded audio data B'a by performing audio decoding on the audio bitstream, and renders (audio rendering) the decoded audio data to output the final audio data A'a through a speaker or a headphone.

[0235] In addition, the reception apparatus performs point cloud video decoding on the video bitstream E'v and outputs the decoded video data B'v. The point cloud video decoding according to the embodiments is the same as described with reference to Figure 1-13 The point cloud video decoding (e.g., Figure 11 The decoding of the point cloud video decoder of

[0236] The reception apparatus according to the embodiments can perform at least one of de-encapsulation, audio decoding, audio rendering, point cloud video decoding, and point cloud video rendering based on the transmitted metadata. The details of the metadata are the same as described with reference to Figure 12-13 The details of the metadata are the same as described with reference to

[0237] As illustrated by the dotted line, the reception apparatus (e.g., point cloud player or sensing / tracking unit in the point cloud player) according to the embodiments can generate feedback information (orientation, viewport). According to the embodiments, the feedback information can be used for the de-encapsulation processing, the point cloud video decoding processing, and / or the rendering processing of the reception apparatus, or can be delivered to the transmission apparatus. The details of the feedback information are the same as described with reference to Figure 1-13 The details of the feedback information are the same as described with reference to

[0238] Figure 15 An exemplary transmission apparatus according to the embodiments is illustrated.

[0239] Figure 15 The transmission apparatus according to the embodiments is an apparatus configured to transmit a point cloud content, and corresponds to the transmission apparatus 10000 described with reference to Figure 1-14 The transmission apparatus (e.g., Figure 1 The transmission apparatus 10000 of Figure 4 The point cloud video encoder ofFigure 12 The transmitting device Figure 14 An example of a transmitting device. Therefore, Figure 15 The transmitting device performs and references Figure 1-14 The operation of the described transmitting device is the same as or similar to that of the device.

[0240] The transmitting device according to the embodiment can perform one or more of point cloud acquisition, point cloud video encoding, file / fragment encapsulation, and transmission.

[0241] Due to the point cloud acquisition and transmission operations and references shown in the figure Figure 1-14 The operations described are the same, and therefore their detailed descriptions will be omitted.

[0242] As referenced above Figure 1-14 As described, the transmitting apparatus according to the embodiment can perform geometric encoding and attribute encoding. Geometric encoding can be referred to as geometric compression, and attribute encoding can be referred to as attribute compression. As mentioned above, a point can have a geometry and one or more attributes. Therefore, the transmitting apparatus performs attribute encoding on each attribute. The figure illustrates the transmitting apparatus performing one or more attribute compressions (attribute #1 compression, ..., attribute #N compression). Additionally, the transmitting apparatus according to the embodiment can perform auxiliary compression. Auxiliary compression is performed on metadata. Details and references to metadata are provided. Figure 1-14 The details described are the same, and therefore their description will be omitted. The transmitting device can also perform grid data compression. Grid data compression according to the embodiment may include references Figure 1-14 The trisoup geometry encoding described.

[0243] According to an embodiment, the transmitting apparatus can encapsulate a bitstream (e.g., a point cloud stream) output according to point cloud video encoding into files and / or segments. According to an embodiment, the transmitting apparatus can perform media track encapsulation to carry data other than metadata (e.g., media data), and perform metadata track encapsulation to carry metadata. According to an embodiment, metadata can be encapsulated into media tracks.

[0244] For reference Figure 1-14 As described, the transmitting device can receive feedback information (orientation / viewport metadata) from the receiving device and perform at least one of point cloud video encoding, file / fragment encapsulation, and delivery operations based on the received feedback information. Details and References Figure 1-14 The details described are the same, and therefore their descriptions will be omitted.

[0245] Figure 16 An exemplary receiving device according to an embodiment is shown.

[0246] Figure 16The receiving device is a device for receiving point cloud content, and corresponds to the reference. Figure 1-14 The described receiving device (e.g., Figure 1 The receiving device 10004 Figure 11 Point cloud video decoder and Figure 13 The receiving device Figure 14 An example of a receiving device. Therefore, Figure 16 The receiving device performs and references Figure 1-14 The described receiving device operates in the same or similar manner. Figure 16 The receiving device can receive from Figure 15 The signal sent by the transmitting device, and the execution of the corresponding... Figure 15 The operation of the transmitting device is the opposite of the processing.

[0247] The receiving device according to the embodiment can perform at least one of transmission, file / fragment decapsulation, point cloud video decoding, and point cloud rendering.

[0248] Because the point cloud receiving and point cloud rendering operations shown in the figure are different from the reference... Figure 1-14 The descriptions are the same, and therefore their detailed descriptions will be omitted.

[0249] For reference Figure 1-14 As described, the receiving device according to an embodiment decapsulates files and / or fragments acquired from a network or storage device. According to an embodiment, the receiving device may perform media track decapsulation to carry data other than metadata (e.g., media data), and perform metadata track decapsulation to carry metadata. According to an embodiment, metadata track decapsulation is omitted when metadata is encapsulated in a media track.

[0250] For reference Figure 1-14 As described, the receiving device can perform geometric decoding and attribute decoding on a bitstream (e.g., a point cloud stream) obtained through decapsulation. Geometric decoding can be referred to as geometric decompression, and attribute decoding can be referred to as attribute decompression. As mentioned above, a point can have a geometric shape and one or more attributes, each of which is encoded by the transmitting device. Therefore, the receiving device performs attribute decoding on each attribute. The figure illustrates the receiving device performing one or more attribute decompressions (attribute #1 decompression, ..., attribute #N decompression). The receiving device according to the embodiment can also perform auxiliary decompression. Auxiliary decompression is performed on metadata. Details and references to metadata. Figure 1-14 The details described are the same, and therefore their description will be omitted. The receiving device can also perform grid data decompression. Grid data decompression according to the embodiment may include reference to... Figure 1-14 The described trisoup geometry decoding. The receiving device according to the embodiment can render point cloud data output from point cloud video decoding.

[0251] For reference Figure 1-14 As described, the receiving device can use a separate sensing / tracking element to acquire orientation / viewport metadata and send feedback information including the orientation / viewport metadata to the transmitting device (e.g., Figure 15 (The transmitting device). Additionally, the receiving device can perform at least one of the following based on feedback information: receiving operation, file / fragment decapsulation, and point cloud video decoding. Details and References Figure 1-14 The details described are the same, and therefore their descriptions will be omitted.

[0252] Figure 17 An exemplary structure is shown that, according to an embodiment, can be connected to a method / apparatus for sending and receiving point cloud data during operation.

[0253] Figure 17 The structure represents a configuration in which at least one of the following components—server 1760, robot 1710, self-driving vehicle 1720, XR device 1730, smartphone 1740, home appliance 1750, and / or head-mounted display (HMD) 1770—is connected to cloud network 1710. Robot 1710, self-driving vehicle 1720, XR device 1730, smartphone 1740, or home appliance 1750 are referred to as devices. Additionally, XR device 1730 may correspond to a point cloud compressed data (PCC) device according to an embodiment or may be connected to a PCC device during operation.

[0254] Cloud network 1700 can refer to a network that forms part of or exists within a cloud computing infrastructure. Here, cloud network 1700 can be configured using a 3G network, a 4G or Long Term Evolution (LTE) network, or a 5G network.

[0255] Server 1760 can be connected via cloud network 1700 to at least one of robot 1710, self-driving vehicle 1720, XR device 1730, smartphone 1740, home appliance 1750 and / or HMD 1770, and can assist at least a portion of the processing of connected devices 1710 to 1770.

[0256] HMD 1770 represents one of the implementation types of an XR device and / or PCC device according to an embodiment. An HMD-type device according to an embodiment includes a communication unit, a control unit, a memory, an I / O unit, a sensor unit, and a power supply unit.

[0257] Hereinafter, various embodiments of the apparatus 1710 to 1750 that apply the above-described technology will be described. Figure 17 The devices 1710 to 1750 shown are connected to / coupled to the point cloud data transmission and reception device according to the above embodiment when in operation.

[0258] <PCC+XR>

[0259] The XR / PCC device 1730 can employ PCC technology and / or XR (AR+VR) technology, and can be implemented as an HMD, a head-up display (HUD) disposed in a vehicle, a television, a mobile phone, a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a stationary robot, or a mobile robot.

[0260] The XR / PCC device 1730 can analyze 3D point cloud data or image data acquired through various sensors or from external devices and generate position data and attribute data regarding 3D points. Thereby, the XR / PCC device 1730 can acquire information regarding a surrounding space or a real object, and render and output an XR object. For example, the XR / PCC device 1730 can match an XR object including auxiliary information regarding an identified object with the identified object and output the matched XR object.

[0261] <PCC+Self-driving+XR>

[0262] The self-driving vehicle 1720 can be implemented as a mobile robot, a vehicle, an unmanned aerial vehicle, etc. by applying PCC technology and XR technology.

[0263] The self-driving vehicle 1720 to which XR / PCC technology is applied can mean a self-driving vehicle provided with a means for providing an XR image, or a self-driving vehicle that is a control / interaction target in an XR image. Specifically, as a control / interaction target in an XR image, the self-driving vehicle 1720 can be distinguished from and operatively connected with the XR device 1730.

[0264] The self-driving vehicle 1720 having a means for providing an XR / PCC image can acquire sensor information from sensors including a camera, and output a generated XR / PCC image based on the acquired sensor information. For example, the self-driving vehicle 1720 can have a HUD and output an XR / PCC image thereto, thereby providing a passenger with an XR / PCC object corresponding to a real object or an object presented on a screen.

[0265] When an XR / PCC object is output to a HUD, at least a part of the XR / PCC object can be output to overlap with a real object pointed by a passenger's eyes. On the other hand, when an XR / PCC object is output on a display disposed inside a self-driving vehicle, at least a part of the XR / PCC object can be output to overlap with an object on a screen. For example, the self-driving vehicle 1720 can output an XR / PCC object corresponding to an object such as a road, another vehicle, a traffic light, a traffic sign, a two-wheeled vehicle, a pedestrian, and a building.

[0266] Virtual reality (VR) technology, augmented reality (AR) technology, mixed reality (MR) technology, and / or point cloud compression (PCC) technology according to embodiments is applicable to various devices.

[0267] In other words, VR technology is a display technology that provides only a CG image of a real world object, background, etc. On the other hand, AR technology refers to a technology that displays a CG image created virtually on an image of a real object. MR technology is similar to the above-described AR technology in that a virtual object to be displayed is mixed and combined with the real world. However, MR technology is different from AR technology in that AR technology clearly distinguishes between a real object and a virtual object created as a CG image and uses the virtual object as a complementary object of the real object, whereas MR technology treats the virtual object as an object having equivalent characteristics to the real object. More specifically, an example to which MR technology is applied is a hologram service.

[0268] Recently, VR, AR, and MR technology are generally referred to as extended reality (XR) technology rather than being clearly distinguished from each other. Accordingly, embodiments of the disclosure are applicable to any one of VR, AR, MR, and XR technology. Encoding / decoding based on PCC, V-PCC, and G-PCC technology is applicable to such technology.

[0269] A PCC method / apparatus according to an embodiment can be applied to a vehicle that provides a self-driving service.

[0270] A vehicle that provides a self-driving service is connected to a PCC apparatus to perform wired / wireless communication.

[0271] When a point cloud compression data (PCC) transmission / reception apparatus according to an embodiment is connected to a vehicle to perform wired / wireless communication, the apparatus can receive / process content data related to an AR / VR / PCC service (which can be provided together with a self-driving service) and transmit the same to the vehicle. In the case in which the PCC transmission / reception apparatus is mounted on the vehicle, the PCC transmission / reception apparatus can receive / process content data related to an AR / VR / PCC service according to a user input signal input through a user interface apparatus and provide the same to a user. The vehicle or the user interface apparatus according to an embodiment can receive a user input signal. The user input signal according to an embodiment can include a signal indicating a self-driving service.

[0272] A method / apparatus according to an embodiment can refer to a point cloud data transmission / reception method / apparatus, a point cloud data encoding / decoding method / apparatus, a point cloud data processor, etc.

[0273] A method / apparatus according to an embodiment provides a method of storing and transmitting a G-PCC bitstream and related parameters.

[0274] The G-PCC bitstream according to embodiments refers to the bitstream generated by the operation of the encoder 10002, Figure 1 Figure 2 the encoding 20001, Figure 4 the encoder, Figure 12 the transmitting apparatus, Figure 14 the audio encoding and point cloud encoding, Figure 17 the apparatus operation.

[0275] The method / apparatus according to embodiments can provide a scheme for efficiently storing and transmitting the static or dynamic parameter sets of the G-PCC bitstream. In addition, the method / apparatus can efficiently store the G-PCC bitstream in a file and generate the related signaling information.

[0276] For the file according to embodiments, please refer to the description of Figure 26 and Figure 27 .

[0277] The method / apparatus according to embodiments can provide a point cloud content service to efficiently store the G-PCC bitstream in a single track within a file and provide the signaling thereof.

[0278] The method / apparatus according to embodiments can provide a file storage technology to support efficient access to the stored G-PCC bitstream, so that the point cloud content service can be efficiently provided.

[0279] The method / apparatus according to embodiments can efficiently store the G-PCC bitstream in the track of a file, propose a signaling scheme thereof, and support efficient access to the stored G-PCC bitstream. In addition, a technology for dividing and storing the G-PCC bitstream into one or more tracks in a file is proposed.

[0280] The method / apparatus according to embodiments can generate parameter set information for efficiently decoding and processing the G-PCC bitstream. The parameter set according to embodiments can exist statically in order or can change over time. The method / apparatus according to embodiments can appropriately deliver the G-PCC parameter set in a file according to the degree of change of the parameter set.

[0281] The track can store a part of the G-PCC bitstream containing one or more G-PCC components. The method / apparatus according to embodiments provides a scheme for track configuration and signaling related thereto.

[0282] According to embodiments, the following terms are defined.

[0283] Point cloud frame: a set of 3D points specified by Cartesian coordinates (x, y, z) and optionally a set of fixed corresponding attributes at a particular time instance. ​

[0284] Bounding box: A rectangular box containing the source point cloud frame.

[0285] Geometry: A set of Cartesian coordinates associated with a point cloud frame. This is about the positional information of points. Depending on the implementation, geometry can be referred to as geometric information, geometric data, etc.

[0286] Attributes: Scalar or vector properties associated with individual points in a point cloud, such as color, reflectivity, and frame index. These are the attribute values ​​of points according to an implementation. Depending on the implementation, attributes may be referred to as attribute information, attribute data, etc.

[0287] The abbreviations used in this article are as follows: APS (Attribute Parameter Set); ASH (Attribute Slice Header); GSH (Geometry Slice Header); GPS (Geometry Parameter Set); LSB (Least Significant Bit); RAHT (Region Adaptive Hierarchical Transformation), SPS (Sequence Parameter Set), and TPS (Patch Parameter Set). TPS is the same as the patch list.

[0288] Slice: A series of syntax elements that represent part or all of the data of a point cloud frame.

[0289] 3D tile: This can correspond to a rectangular cuboid within a bounding box. Depending on the implementation, it can consist of a set of slices.

[0290] G-PCC Patch Track: A volumetric visual track that delivers a single G-PCC component or all G-PCC components corresponding to one or more G-PCC patches.

[0291] G-PCC tile basic track: a volumetric visual track that carries the tile list and the parameter set corresponding to the G-PCC tile track.

[0292] Track (G-PCC track): A volumetric visual track that carries a bitstream of encoded geometry or a bitstream of encoded attributes, or both.

[0293] Tiles: This can be a set of slices related to geometry within a bounding box specified in the tile list. Depending on the implementation, it can be called a 3D tile or a tile.

[0294] Figure 18 This illustrates the operation of encapsulating a portion of a G-PCC bitstream according to an embodiment.

[0295] Figure 18 Showing the basis and Figure 2 Sending operations and Figure 14 and Figure 15 The method / apparatus for sending point cloud data is related to the operation of the file / fragment encapsulator. It is consistent with the implementation of... Figure 2 The sending operation of receiving point cloud data andFigure 16 The file / fragment decapsulator operation handles the decapsulation of the G-PCC bitstream processed by the file / fragment decapsulator operation.

[0296] The method / apparatus according to embodiments can store G-PCC parameter sets in sample entries, sample groups, track groups, or separate metadata in order to properly convey static or time-varying G-PCC parameter sets in a file.

[0297] The method / apparatus according to embodiments can store a part of a G-PCC bitstream including one or more G-PCC components in a track. The signaling information related thereto, which can be referred to as metadata, parameters, etc., can be stored in a sample entry or a sample group. Information indicating a new reference relationship between tracks can be further generated.

[0298] The method / apparatus according to embodiments can encode and transmit point cloud data, and receive and decode the point cloud data. For example, the scheme can be referred to as G-PCC. It refers to geometry-based point cloud compression data. The data indicates a volumetric encoding of a point cloud consisting of a series of point cloud frames. Each point cloud frame can include points, their positions, and their attributes, and can vary frame by frame.

[0299] Source point cloud data can be split into a plurality of slices and encoded in a bitstream. A slice is a group of points that can be independently encoded or decoded. Geometry information and attribute information of each slice can be independently encoded or decoded. A tile can be a group of slices with bounding box information.

[0300] The bounding box information of each tile can be specified in a tile manifest. A tile can overlap with another tile in a bounding box. Each slice can have an index, which can represent an identifier belonging to a tile.

[0301] A G-PCC bitstream can consist of parameter sets (e.g., sequence parameter sets, geometry parameter sets, and attribute parameter sets), geometry slices, and attribute slices.

[0302] The method / apparatus according to embodiments can encapsulate G-PCC data based on a type-length-value (TLV) scheme. A G-PCC TLV encapsulation structure according to embodiments will be described.

[0303] A byte stream format used by an application can consist of a series of type-length-value (TLV) encapsulation structures, each of which can represent a single coded syntax structure. Each TLV encapsulation structure can include a payload type, a payload length, and a payload byte.

[0304] Table 1

[0305] tlv_encapsulation(){ descriptor tlv_type u(8) tlv_num_payload_bytes u(32) for(i=0; i< tlv_num_payload_bytes; i++) tlv_payload_byte[i] u(8) }

[0306] The tlv_type can identify the syntax structure represented by the tlv_payload_byte[].

[0307] Table 2

[0308] tlv_type syntax table description 0 7.3.1.1 sequence parameter set 1 7.3.1.2 geometry parameter set 2 7.3.2.1 geometry payload 3 7.3.1.3 attribute parameter set 4 7.3.3.1 attribute payload 5 7.3.2.2 tile list 6 7.3.2.5 frame boundary marker

[0309] A geometry payload can be referred to as a geometry data unit. An attribute payload can be referred to as an attribute data unit.

[0310] The tlv_num_payload_bytes indicates the byte length of the tlv_payload_byte[].

[0311] The tlv_payload_byte[i] is the i-th byte of the payload data.

[0312] As shown in FIG. 10, the transmitting apparatus 10000, the encoder 10002, the encoding 20001, Figure 18 of the encoder, Figure 4 the transmitting apparatus of the transmitting apparatus, and Figure 12 the decoder of the decoder can generate a G-PCC bitstream. In addition, the receiving apparatus 10004, the decoder 10006, Figure 14 the decoding 20003, Figure 15 of the decoder, Figure 2 the receiving apparatus of the receiving apparatus, Figure 10 the decoding of the decoding, and Figure 11 the decoder of the decoder can decode the G-PCC bitstream of the G-PCC bitstream. Figure 13 Figure 14 The decoding process of the TLV payload according to the embodiments is performed as disclosed below. The input of the process is an ordered byte stream consisting of a series of TLV encapsulation structures. The output of the process is a series of syntax structures. Figure 16 Figure 18 The decoder according to the embodiments repeatedly parses the TLV encapsulation structure until the end of the byte stream is reached. The last NAL unit in the byte stream is decoded.

[0313] After parsing each TLV, the following encapsulation structure is obtained.

[0314] The array PayloadBytes is set equal to the tlv_payload_byte[]

[0315] The variable NumPayloadBytes is set equal to the tlv_num_payload_bytes

[0316] The array PayloadBytes is set equal to the tlv_payload_byte[]

[0317] The variable NumPayloadBytes is set equal to the tlv_num_payload_bytes ​​

[0318] The application corresponds to the parsing process of tlv_type.

[0319] In the following, the syntax included in the bitstream according to the embodiments will be described.

[0320] Figure 19 A sequence parameter set according to an embodiment is shown.

[0321] Figure 19 A detailed syntax of a sequence parameter set included in the bitstream of Figure 18 is shown.

[0322] main_profile_compatibility_flag equal to 1 specifies that the bitstream conforms to the main profile. main_profile_compatibility_flag equal to 0 specifies that the bitstream conforms to a profile other than the main profile.

[0323] unique_point_positions_constraint_flag equal to 1 indicates that in each point cloud frame referring to the current SPS, all output points have unique positions. unique_point_positions_constraint_flag equal to 0 indicates that in any point cloud frame referring to the current SPS, output points can have the same position.

[0324] level_idc indicates the level to which the bitstream conforms.

[0325] sps_seq_parameter_set_id can provide an identifier of the SPS for other syntax elements to refer to.

[0326] sps_bounding_box_present_flag equal to 1 indicates a bounding box. sps_bounding_box_present_flag equal to 0 indicates that the size of the bounding box is not defined.

[0327] sps_bounding_box_offset_x, sps_bounding_box_offset_y, and sps_bounding_box_offset_z indicate the quantized x, y, and z offsets of the source bounding box in Cartesian coordinates.

[0328] sps_bounding_box_offset_log2_scale indicates a scaling factor to scale the quantized x, y, and z source bounding box offsets.

[0329] sps_bounding_box_size_width, sps_bounding_box_size_height, and sps_bounding_box_size_depth indicate the width, height, and depth of the source bounding box in Cartesian coordinates.

[0330] sps_source_scale_factor_numerator_minus1 plus 1 indicates the numerator of the scaling factor of the source point cloud.

[0331] sps_source_scale_factor_denominator_minus1 plus 1 indicates the denominator of the scaling factor of the source point cloud.

[0332] sps_num_attribute_sets indicates the number of encoded attributes in the bitstream. The value of sps_num_attribute_sets can be in the range of 0 to 63.

[0333] attribute_dimension_minus1[i] plus 1 specifies the number of components of the i-th attribute.

[0334] attribute_instance_id[i] specifies the instance ID of the i-th attribute.

[0335] attribute_bitdepth_minus1[i] plus 1 specifies the bit depth of the first component of the i-th attribute signal.

[0336] attribute_secondary_bitdepth_minus1[i] plus 1 specifies the bit depth of the second component of the i-th attribute signal.

[0337] attribute_cicp_colour_primaries[i] indicates the chromaticity coordinates of the colour attribute source primary of the i-th attribute.

[0338] attribute_cicp_transfer_characteristics[i] either indicates the reference electro-optical transfer characteristic function of the colour attribute as a function of the source input linear luminance Lc in the nominal real-valued range of 0 to 1, or indicates the inverse of the reference electro-optical transfer characteristic function as a function of the output linear luminance Lo in the nominal real-valued range of 0 to 1.

[0339] attribute_cicp_matrix_coeffs[i] describes the matrix coefficients used to derive the luminance and chrominance signals from the green, blue, and red or Y, Z, and X primary colours.

[0340] attribute_cicp_video_full_range_flag[i] specifies the black level and range of the luma and chroma signals as derived from the E'Y, E'PB and E'PR or E'R, E'G and E'B real-valued component signals.

[0341] known_attribute_label_flag[i] equal to 1 specifies that know_attribute_label is signaled for the i-th attribute. known_attribute_label_flag[i] equal to 0 specifies that attribute_label_four_bytes is signaled for the i-th attribute.

[0342] known_attribute_label[i] equal to 0 specifies that the attribute is color. known_attribute_label[i] equal to 1 specifies that the attribute is reflectance. known_attribute_label[i] equal to 2 specifies that the attribute is frame index.

[0343] attribute_label_four_bytes[i] indicates the known attribute type with 4 bytes code. The list of supported attributes and their relationship with attribute_label_four_bytes[i] is shown below.

[0344] Table 3

[0345] attribute_label_four_bytes[i] attribute type 0 color 1 reflectance 2 frame index 3 material ID 4 transparency 5 normal 6…255 reserved 256...0xffffffff unspecified

[0346] log2_max_frame_idx plus 1 specifies the number of bits used to signal the syntax variable frame_idx.

[0347] axis_coding_order specifies the correspondence between the X, Y and Z output axis labels and the three position components of all points in the reconstructed point cloud RecPic[pointIdx][axis] for axis = 0 to 2.

[0348] Table 4

[0349] axis_coding_order X Y Z 0 2 1 0 1 0 1 2 2 0 2 1 3 2 0 1 4 2 1 0 5 1 2 0 6 1 0 2 7 0 1 2

[0350] sps_bypass_stream_enabled_flag equal to 1 specifies that the bypass coding mode can be used for reading the bitstream. sps_bypass_stream_enabled_fla equal to 0 specifies that the bypass coding mode is not used when reading the bitstream.

[0351] sps_extension_flag equal to 0 specifies that the sps_extension_data_flag syntax element is not present in the SPS syntax structure. In bitstreams conforming to versions according to embodiments, sps_extension_flag can be equal to 0.

[0352] Figure 20 Syntax showing a tile list (tile parameter set) according to embodiments

[0353] Figure 20 Syntax showing a tile parameter set included in a bitstream of Figure 18

[0354] tile_frame_idx specifies the identifier of the cloud frame associated with the tile list.

[0355] num_tiles_minus1 plus 1 specifies the number of tile bounding boxes present in the tile list.

[0356] tile_bounding_box_offset_x[i], tile_bounding_box_offset_y[i], and tile_bounding_box_offset_z[i] indicate the x, y, and z offsets of the i-th tile in Cartesian coordinates.

[0357] tile_bounding_box_size_width[i], tile_bounding_box_size_height[i], and tile_bounding_box_size_depth[i] indicate the width, height, and depth of the i-th tile in Cartesian coordinates.

[0358] Figure 21 Syntax showing a geometry parameter set according to embodiments

[0359] Figure 21 Syntax showing a geometry parameter set included in a bitstream of Figure 18

[0360] gps_geom_parameter_set_id specifies identification information about the GPS for other syntax elements to refer to.

[0361] gps_seq_parameter_set_id specifies the value of sps_seq_parameter_set_id of the active SPS.

[0362] ​​gps_box_present_flag equal to 1 specifies that additional bounding box information is provided in the geometry header referring to the current GPS. gps_box_present_flag equal to 0 specifies that no additional bounding box information is signaled in the geometry header.

[0363] gps_gsh_box_log2_scale_present_flag equal to 1 specifies that gsh_box_log2_scale is signaled in each geometry slice header referring to the current GPS. gps_gsh_box_log2_scale_present_flag equal to 0 specifies that gsh_box_log2_scale is not signaled in each geometry slice header and a common scaling for all slices is signaled in gps_gsh_box_log2_scale referring to the current GPS.

[0364] gps_gsh_box_log2_scale indicates a common scaling factor for the origin of the bounding box referring to all slices of the current GPS.

[0365] unique_geometry_points_flag equal to 1 indicates that all output points have a unique position within the slice referring to all slices of the current GPS. unique_geometry_points_flag equal to 0 indicates that two or more output points can have the same position within the slice referring to all slices of the current GPS.

[0366] geometry_planar_mode_flag equal to 1 indicates that the planar coding mode is activated. gometry_planar_mode_flag equal to 0 indicates that the planar coding mode is not activated.

[0367] geom_planar_mode_th_idcm specifies the value of the activation threshold for the direct coding mode. geom_planar_mode_th_idcm can be an integer in the range of 0 to 127, inclusive.

[0368] For i in the range of 0...2, geom_planar_mode_th[i] specifies the value of the activation threshold for the planar coding mode along the i-th most probable direction of the efficient planar coding mode. geom_planar_mode_th[i] can be an integer in the range of 0 to 127.

[0369] geometry_angular_mode_flag equal to 1 indicates that the angular coding mode is activated. geometry_angular_mode_flag equal to 0 indicates that the angular coding mode is not activated.

[0370] lidar_head_position specifies the (X, Y, Z) coordinates of the lidar head in the coordinate system with the inner axes.

[0371] number_lasers specifies the number of lasers used for the angular coding mode.

[0372] For i in the range of 1 to number_lasers, laser_angle[i] specifies the tangent of the elevation angle of the i-th laser relative to the horizontal plane defined by the 0th and 1st inner axes.

[0373] laser_correction[i] specifies the correction of the i-th laser position along the second inner axis relative to lidar_head_position[2].

[0374] planar_buffer_disabled equal to 1 indicates that the use of the buffer to track the nearest node is not used in the process of encoding the planar mode flag and the planar positions in the planar mode. planar_buffer_disabled equal to 0 indicates that the use of the buffer to track the nearest node is utilized. When not present, planar_buffer_disabled is inferred to be 0.

[0375] implicit_qtbt_angular_max_node_min_dim_log2_to_split_z specifies the log2 value of the node size below which horizontal splitting is preferred over vertical splitting.

[0376] implicit_qtbt_angular_max_diff_to_split_z specifies the log2 value of the maximum vertical to horizontal node size ratio of a node allowed. When not present, implicit_qtbt_angular_max_node_min_dim_log2_to_split_z is inferred to be 0.

[0377] neighbour_context_restriction_flag equal to 0 indicates that the geometry node occupancy of the current node is coded using contexts determined from neighbouring nodes located inside the parent node of the current node. neighbour_context_restriction_flag equal to 0 indicates that the geometry node occupancy of the current node is coded using contexts determined from neighbouring nodes located inside or outside the parent node of the current node.

[0378] inferred_direct_coding_mode_enabled_flag equal to 1 indicates that direct_mode_flag can be present in the geometry node syntax. inferred_direct_coding_mode_enabled_flag equal to 0 indicates that direct_mode_flag is not present in the geometry node syntax.

[0379] bitwise_occupancy_coding_flag equal to 1 indicates that the geometry node occupancy is coded using bitwise contextualisation of the syntax element occupancy_map. bitwise_occupancy_coding_flag equal to 0 indicates that the geometry node occupancy is coded using dictionary coding of the syntax element occupancy_byte.

[0380] adjacent_child_contextualization_enabled_flag equal to 1 indicates that neighbouring children of neighbouring octree nodes are used for bitwise occupancy contextualisation. adjacent_child_contextualization_enabled_flag equal to 0 indicates that children of neighbouring octree nodes are not used for occupancy contextualisation.

[0381] log2_neighbour_avail_boundary specifies the variable NeighbAvailabilityMask.

[0382] NeighbAvailabilityMask is set equal to 1 when neighbour_context_restriction_flag is equal to 1. Otherwise, neighbour_context_restriction_flag is equal to 0.

[0383] log2_intra_pred_max_node_size specifies the octree node size that conforms to intra-prediction of the occupancy.

[0384] log2_tisoup_node_size specifies the variable TrisoupNodeSize as the size of a triangle node.

[0385] geom_scaling_enabled_flag equal to 1 specifies that scaling of geometry positions is invoked during the geometry slice decoding process. geom_scaling_enabled_flag equal to 0 specifies that scaling of geometry positions is not needed.

[0386] geom_base_qp specifies the base value of the geometry position quantization parameter.

[0387] gps_implicit_geom_partition_flag equal to 1 specifies that implicit geometry partitioning is enabled for a sequence or slice. gps_implicit_geom_partition_flag equal to 0 indicates that implicit geometry partitioning is disabled for a sequence or slice. When gps_implicit_geom_partition_flag is equal to 1, gps_max_num_implicit_qtbt_before_ot and gps_min_size_implicit_qtbt are signaled.

[0388] gps_max_num_implicit_qtbt_before_ot specifies the maximum number of implicit QT and BT partitions before OT partitioning.

[0389] gps_min_size_implicit_qtbt specifies the minimum size of an implicit QT and BT partition.

[0390] gps_extension_flag equal to 0 specifies that there is no gps_extension_data_flag syntax element in the GPS syntax structure.

[0391] gps_extension_data_flag can have any value. It can not affect decoders of decoder versions according to embodiments.

[0392] Figure 22 An attribute parameter set according to embodiments is illustrated.

[0393] Figure 22 A syntax of an attribute parameter set included in a bitstream of Figure 18 is illustrated.

[0394] aps_attr_parameter_set_id provides an identifier of the APS for other syntax elements to reference.

[0395] aps_seq_parameter_set_id specifies the value of sps_seq_parameter_set_id of the active SPS.

[0396] attr_coding_type indicates the coding type of the attribute.

[0397] Table 5

[0398] attr_coding_type coding type 0 Predictive weight lifting 1 Region adaptive hierarchical transform (RAHT) 2 Fixed weight lifting

[0399] aps_attr_initial_qp specifies the initial value of the variable SliceQp for each slice referring to the APS. The value of aps_attr_initial_qp can be in the range of 4 to 51, inclusive.

[0400] aps_attr_chroma_qp_offset specifies the offset relative to the initial quantization parameter signaled by the syntax aps_attr_initial_qp.

[0401] aps_slice_qp_delta_present_flag equal to 1 specifies that the ash_attr_qp_delta_luma and ash_attr_qp_delta_chroma syntax elements are present in the ASH. aps_slice_qp_present_flag equal to 0 specifies that the ash_attr_qp_delta_luma and ash_attr_qp_delta_chroma syntax elements are not present in the ASH.

[0402] lifting_num_pred_nearest_neighbours_minus1 plus 1 specifies the maximum number of nearest neighbours to be used for prediction.

[0403] The value of NumPredNearestNeighbours is set equal to lift_num_pred_nearest_neighbours.

[0404] lifting_num_detail_levels_minus1 specifies the number of detail levels for the attribute coding. The variable LevelDetailCount specifies the number of detail levels as follows:

[0405] LevelDetailCount = lift_num_detail_levels_minus1 + 1

[0406] lifting_neighbour_bias[k] specifies a bias for weighting the k-th component when computing the Euclidean distance between two points as part of the nearest neighbour derivation process.

[0407] lifting_scalability_enabled_flag equal to 1 specifies that the attribute decoding process allows input of pruned octree decoding results of geometry points. lifting_scalability_enabled_flag equal to 0 specifies that the attribute decoding process requires input of complete octree decoding results of geometry points.

[0408] lifting_search_range_minus1 plus 1 specifies the search range used for determining the nearest neighbour to be used for prediction and for constructing distance-based levels of detail. LiftingSearchRange = lifting_search_range_minus1 + 1

[0409] lifting_lod_regular_sampling_enabled_flag equal to 1 specifies that regular sampling strategies are used for constructing levels of detail. lifting_lod_regular_sampling_enabled_flag equal to 0 specifies that instead distance-based sampling strategies are used.

[0410] lifting_sampling_period_minus2[idx] plus 2 specifies the sampling period for level of detail idx.

[0411] lifting_sampling_distance_squared_scale_minus1[idx] plus 1 specifies the scaling factor used for deriving the sampling distance squared for level of detail idx.

[0412] lifting_sampling_distance_squared_offset[idx] specifies the offset used for deriving the sampling distance squared for level of detail idx.

[0413] lifting_adaptive_prediction_threshold specifies the threshold for enabling adaptive predictor selection.

[0414] The variable AdaptivePredictionThreshold specifying the threshold for switching to adaptive predictor selection mode is set equal to lifting_adaptive_prediction_threshold.

[0415] lifting_intra_lod_prediction_num_layers specifies the number of LOD layers from which a decoded point in the same LOD layer can be referenced to generate a prediction value for a target point. lifting_intra_lod_prediction_num_layers equal to LevelDetailCount indicates that for all LOD layers, a target point can reference a decoded point in the same LOD layer. lifting_intra_lod_prediction_num_layers equal to 0 indicates that for any LOD layer, a target point can not reference a decoded point in the same LOD layer. lifting_intra_lod_prediction_num_layers can be in the range of 0 to LevelDetailCount.

[0416] lifting_max_num_direct_predictors specifies the maximum number of predictors to be used for direct prediction.

[0417] inter_component_prediction_enabled_flag equal to 1 specifies that the primary component of a multi-component attribute is used to predict the reconstructed values of non-primary components. inter_component_prediction_enabled_flag equal to 0 specifies that all attribute components are independently reconstructed.

[0418] raht_prediction_enabled_flag equal to 1 specifies that transform weight prediction from neighboring points is enabled in the RAHT decoding process. raht_prediction_enabled_flag equal to 0 specifies that transform weight prediction from neighboring points is disabled in the RAHT decoding process.

[0419] raht_prediction_threshold0 specifies a threshold to terminate transform weight prediction from neighboring points.

[0420] raht_prediction_threshold1 specifies a threshold to jump transform weight prediction from neighboring points.

[0421] aps_extension_flag equal to 0 specifies that the aps_extension_data_flag syntax element is not present in the APS syntax structure.

[0422] aps_extension_data_flag can have any value. It can not affect decoders conforming to a version according to an embodiment.

[0423] The frame included in the bitstream according to the embodiment FIG. 18 may include a frame boundary marker. The frame boundary marker can mark the end of the current frame.

[0424] frame_boundary_marker(){

[0425] / * This syntax structure is intentionally empty * /

[0426] }

[0427] FIG. 23 A geometry slice header according to the embodiment is shown.

[0428] FIG. 23 A syntax of the geometry slice header included in the bitstream of FIG. 18 is shown.

[0429] gsh_geometry_parameter_set_id specifies the value of the gps_geom_parameter_set_id of the active GPS.

[0430] gsh_tile_id specifies the value of the tile id referenced by the GSH.

[0431] gsh_slice_id identifies the slice header for other syntax elements to reference.

[0432] frame_idx specifies the log2_max_frame_idx + 1 least significant bits of the frame number counter. Consecutive slices with different frame_idx values form part of different output point cloud frames. Consecutive slices with the same frame_idx value and without intermediate frame boundary marker data units form part of the same output point cloud frame.

[0433] gsh_num_points specifies the maximum number of encoded points in the slice. gsh_num_points is greater than or equal to the number of decoded points in the slice.

[0434] gsh_box_log2_scale specifies the scaling factor of the bounding box origin of the slice.

[0435] gsh_box_origin_x specifies the x value of the bounding box origin scaled by the value of gsh_box_log2_scale.

[0436] gsh_box_origin_y specifies the y value of the bounding box origin scaled by the value of gsh_box_log2_scale.

[0437] gsh_box_origin_z specifies the z value of the origin of the bounding box scaled by the value of gsh_box_log2_scale.

[0438] The variables slice_origin_x, slice_origin_y, and slice_origin_z can be derived as follows:

[0439] If gps_gsh_box_log2_scale_present_flag is equal to 0,

[0440] originScale is set equal to gsh_box_log2_scale

[0441] Otherwise (gps_gsh_box_log2_scale_present_flag is equal to 1),

[0442] originScale is set equal to gps_gsh_box_log2_scale

[0443] If gps_box_present_flag is equal to 0,

[0444] The values of slice_origin_x and slice_origin_y and slice_origin_z are inferred to be 0.

[0445] Otherwise (gps_box_present_flag is equal to 1), the following applies:

[0446] slice_origin_x = gsh_box_origin_x « originScale

[0447] slice_origin_y = gsh_box_origin_x « originScale

[0448] slice_origin_z = gsh_box_origin_x « originScale

[0449] gsh_log2_max_nodesize_x can specify the bounding box size in the x-dimension, MaxNodesizeXLog2, which is used in the decoding process as follows: MaxNodeSizeXLog2 = gsh_log2_max_nodesize_x, MaxNodeSizeX = 1 « MaxNodeSizeXLog2.

[0450] gsh_log2_max_nodesize_y_minus_x can specify the bounding box size in y-dimension, i.e., MaxNodesizeYLog2, which is used in the decoding process as follows: MaxNodeSizeYLog2 = gsh_log2_max_nodesize_y_minus_x + MaxNodeSizeXLog2, MaxNodeSizeY = 1 « MaxNodeSizeYLog2.

[0451] gsh_log2_max_nodesize_z_minus_y can specify the bounding box size in z-dimension, i.e., MaxNodesizeZLog2, which is used in the decoding process as follows: MaxNodeSizeZLog2 = gsh_log2_max_nodesize_z_minus_y + MaxNodeSizeYLog2, MaxNodeSizeZ = 1 « MaxNodeSizeZLog2.

[0452] When gps_implicit_geom_partition_flag is equal to 1, gsh_log2_max_nodesize can be derived as follows:

[0453] gsh_log2_max_nodesize = max{MaxNodeSizeXLog2, MaxNodeSizeYLog2, MaxNodeSizeZLog2}.

[0454] gsh_log2_max_nodesize specifies the size of the root geometry octree node when gps_implicit_geom_partition_flag is equal to 0. The variables MaxNodeSize and MaxGeometryOctreeDepth can be derived as follows: MaxNodeSize = 1 « gsh_log2_max_nodesize, MaxGeometryOctreeDepth = gsh_log2_max_nodesize - log2_trisoup_node_size.

[0455] geom_slice_qp_offset specifies the offset of the base geometry quantization parameter, geom_base_qp.

[0456] geom_octree_qp_offsets_enabled_flag equal to 1 specifies that geom_node_qp_offset_eq0_flag can be present in the geometry node syntax. geom_octree_qp_offsets_enabled_flag equal to 0 specifies that geom_node_qp_offset_eq0_flag is not present in the geometry node syntax.

[0457] When geom_node_qp_offset_eq0_flag is present in the geometry node syntax, geom_octree_qp_offsets_depth (when present) can specify the depth of the geometry octree.

[0458] geometry_slice_data() can include geometry or geometry-related data associated with a portion or all of a point cloud.

[0459] FIG. 24 An attribute slice according to an embodiment is shown.

[0460] FIG. 24 Syntax of an attribute slice included in a bitstream of FIG. 18 is shown.

[0461] ash_attr_parameter_set_id specifies the value of aps_attr_parameter_set_id of the active APS.

[0462] ash_attr_sps_attr_idx specifies the order of attributes set in the active SPS.

[0463] ash_attr_geom_slice_id can specify the value of gsh_slice_id of the active geometry slice header.

[0464] ash_attr_layer_qp_delta_present_flag equal to 1 can specify that ash_attr_layer_qp_delta_luma and ash_attr_layer_qp_delta_chroma syntax elements are present in the current ASH. ash_attr_layer_qp_delta_present_flag equal to 0 can specify that ash_attr_layer_qp_delta_luma and ash_attr_layer_qp_delta_chroma syntax elements are not present in the current ASH.

[0465] ash attr num layer qp minusl plusl specifies the number of layers for which ash attr qp delta luma and ash attr qp delta chroma are signaled. When ash attr num layer qp is not signaled, the value of ash attr num layer qp can be inferred to be 0. The value of NumLayerQp can be derived as follows: NumLayerQp = num layer qp minusl + 1.

[0466] ash attr qp delta luma specifies the luma delta qp from the initial slice qp in the active attribute parameter set.

[0467] ash attr qp delta chroma specifies the chroma delta qp from the initial slice qp in the active attribute parameter set.

[0468] The variables InitialSliceQpY and InitialSliceQpC can be derived as follows:

[0469] InitialSliceQpY = aps attr attr initial qp + ash attr qp delta luma;

[0470] InitialSliceQpC = aps attr attr initial qp + aps attr chroma qp offset + ash attr qp delta chroma.

[0471] ash attr layer qp delta luma specifies the luma delta qp from InitialSliceQpY in each layer.

[0472] ash attr layer qp delta chroma specifies the chroma delta qp from InitialSliceQpC in each layer.

[0473] The variables SliceQpY[i] and SliceQpC[i] (i = 0,..., NumLayerQP NumQPLayer - 1) can be derived as follows:

[0474] for (i = 0; i < NumLayerQP NumQPLayer; i++) {

[0475] SliceQpY[i] = InitialSliceQpY + ash_attr_layer_qp_delta_luma[i]

[0476] SliceQpC[i] = InitialSliceQpC + ash_attr_layer_qp_delta_chroma[i]

[0477] }

[0478] ash_attr_region_qp_delta_present_flag equal to 1 can indicate that ash_attr_region_qp_delta and region bounding box origin and size are present in the current ASH. ash_attr_region_qp_delta_present_flag equal to 0 can indicate that ash_attr_region_qp_delta and region bounding box origin and size are not present in the current ASH.

[0479] ash_attr_qp_region_box_origin_x can indicate the x offset of the region bounding box relative to slice_origin_x.

[0480] ash_attr_qp_region_box_origin_y can indicate the y offset of the region bounding box relative to slice_origin_y.

[0481] ash_attr_qp_region_box_origin_z can indicate the z offset of the region bounding box relative to slice_origin_z.

[0482] ash_attr_qp_region_box_size_width can indicate the width of the region bounding box.

[0483] ash_attr_qp_region_box_size_height can indicate the height of the region bounding box.

[0484] ash_attr_qp_region_box_size_depth can indicate the depth of the region bounding box.

[0485] ash_attr_region_qp_delta can specify delta qp for SliceQpY[i] and SliceQpC[i] of the region specified by ash_attr_qp_region_box.

[0486] The variable RegionboxDeltaQp, which specifies the region box delta quantization parameter, can be set equal to ash_attr_region_qp_delta.

[0487] The attribute_slice_data() can include attribute or attribute-related data associated with part or all of the point cloud.

[0488] The method / apparatus (e.g., the transmitting apparatus 10000, the encoding 20001 and transmitting 20002, FIG. 14 and FIG. 15 The file / segment encapsulator, FIG. 17 The apparatus, etc. of the present disclosure can encapsulate the bitstream (see FIGS. 25-27 encoded as in FIG. 18 , etc.) in a file (container) structure to effectively transmit the bitstream.

[0489] The receiving apparatus 10004, the transmitting 20002 and decoding 20003, FIG. 14 and FIG. 16 The file / segment decapsulator, FIG. 17 The apparatus, etc. of the present disclosure can receive a file (container) structure (such as the structure shown in FIGS. 26-27 ), parse and decode the bitstream such as FIG. 18 from the file, and render and provide point cloud data.

[0490] In the following, such a file structure delivery or G-PCC system will be described.

[0491] The method / apparatus according to the embodiments can encapsulate the G-PCC bitstream in a track of a file. The G-PCC bitstream can consist of a type-length-value encapsulation structure ( FIG. 18 ) including a parameter set, an encoded geometry bitstream, and zero or one or more encoded attribute bitstreams.

[0492] The method / apparatus according to the embodiments can store the G-PCC bitstream in a single track or multiple tracks.

[0493] The single track and / or multiple tracks of the file according to the embodiments can include the following data.

[0494] Volume visual track

[0495] The volume visual track can be identified by the volume visual media handler type 'volv' in the HandlerBox of the MediaBox and a volume visual media header. There can be multiple volume visual tracks in a file.

[0496] Volume visual media header

[0497] box type: 'vvhd'

[0498] container: MediaInformationBox

[0499] required: yes

[0500] quantity: exactly one

[0501] A volumetric visual track can use VolumetricVisualMediaHeaderBox in MediaInformationBox.

[0502] aligned(8) class VolumetricVisualMediaHeaderBox

[0503] extends FullBox('vvhd', version = 0, 1)

[0504] }

[0505] "version" can be an integer that specifies the version of this box Volumetric visual sample entry

[0506] VolumetricVisualSampleEntr

[0507] A volumetric visual track can use VolumetricVisualSampleEntry.

[0508] class VolumetricVisualSampleEntry(coding name)

[0509] extends SampleEntry(coding name)

[0510] unsigned int(8)

[32] compressor name;

[0511] / / other boxes from the derivation specification

[0512] }

[0513] compressor name is a name for the purpose of providing information. It can be formatted as a fixed 32-byte field, with the first byte set to the number of bytes to be displayed, followed by the number of bytes of displayable data using UTF-8 encoding, and then padded to a full 32 bytes (including size bytes). This field can be set to 0.

[0514] volumetric visual sample

[0515] The format of the volumetric visual sample can be defined by the encoding system.

[0516] The public data structures included in files and tracks according to embodiments are as follows.

[0517] G-PCC decoder configuration box

[0518] The G-PCC decoder configuration box can include a GPCCDDecoderConfigurationRecord ().

[0519] class GPCCConfigurationBox extends Box('gpcC'){

[0520] GPCCDecoderConfigurationRecord() GPCCConfig;

[0521] }

[0522] The G-PCC decoder configuration record can specify G-PCC decoder configuration information for geometry-based point cloud content. The G-PCC decoder configuration record can contain a version field. Incompatible changes to the record can be indicated by a change in the version number. If the version number cannot be recognized, the decoder can be unable to decode the record or stream.

[0523] Compatible extensions to the record can extend the record and can not change the configuration version code. The decoder can ignore unrecognized data.

[0524] The values of profile_idc, profile_compatibility_flags, and level_idc are valid for all parameter sets (referred to as "all parameter sets" in the following sentences of this paragraph) that are activated when decoding the stream described by this record. Specifically, the following restrictions apply:

[0525] The values of profile_idc, profile_compatibility_flags, and level_idc are valid for all parameter sets (referred to as "all parameter sets" in the following sentences of this paragraph) that are activated when decoding the stream described by this record. Specifically, the following restrictions apply:

[0526] The profile indication profile_idc can indicate the profile to which the stream associated with this configuration record conforms.

[0527] Individual bits in the profile_compatibility_flags can be set when all parameter sets are set.

[0528] The level indication level_idc can indicate a capability level equal to or greater than the highest level indicated for all parameter sets.

[0529] The setupUnit array can include a G-PCC TLV encapsulation structure that is constant for the stream to which the sample entry with the decoder configuration record refers.

[0530] The type of the G-PCC encapsulation structure can indicate an SPS, a GPS, an APS, or a TPS.

[0531] aligned(8) class GPCCDDecoderConfigurationRecord {

[0532] unsigned int(8) configurationVersion = 1;

[0533] unsigned int(8) profile_idc;

[0534] unsigned int(24) profile_compatibility_flags;

[0535] unsigned int(8) level_idc;

[0536] unsigned int(8) numOfSetupUnitArrays;

[0537] for(i = 0; i < numOfSetupUnitArrays; i++) {

[0538] unsigned int(7) setupUnitType;

[0539] bit(1) setupUnit_completeness;

[0540] unsigned int(8) numOfSetupUnits;

[0541] for(i = 0; i < numOfSetupUnits; i++) {

[0542] tlv_encapsulation setupUnit;

[0543] }

[0544] }

[0545] / / Additional fields

[0546] }

[0547] configurationVersion is a version field. Incompatible changes to the record can be indicated by a change in the version number.

[0548] profile_idc can indicate the profile code of G-PCC.

[0549] profile_compatibility_flags equal to 1 can indicate that the bitstream conforms to the profile indicated by profile_idc.

[0550] level_idc can indicate the level code of G-PCC.

[0551] numOfSetupUnitArrays can specify the number of arrays of G-PCC setup units of the type indicated by the setupUnitType field.

[0552] setupUnitType indicates the type of G-PCC setup unit signaled. It can be one of the values indicating SPS, GPS, APS, and TPS.

[0553] setupUnit_completeness equal to 1 can indicate that all setup units of the given type are in the following array and none are in the stream. setupUnit_completeness equal to 0 can indicate that additional setup units of the specified type can be in the stream.

[0554] numOfSetupUnits can specify the number of G-PCC setup units of the type indicated by the setupUnitType field signaled in the record.

[0555] setupUnit is an instance of the TLV encapsulation structure that carries a setup unit of the specified type, e.g., SPS, GPS, APS, or TPS.

[0556] The G-PCC decoder configuration record according to embodiments can be as follows.

[0557] aligned(8) class GPCCDDecoderConfigurationRecord {

[0558] unsigned int(8) configurationVersion = 1;

[0559] unsigned int(8)profile_idc;

[0560] unsigned int(24)profile_compatibility_flags;

[0561] unsigned int(8)level_idc;

[0562] GPCCParameterSetStruct();

[0563] / / Additional fields

[0564] }

[0565] FIG. 25 The structure of the G-PCC parameter set according to the implementation method is shown.

[0566] FIG. 25 The diagram shows that the included items are... FIG. 18 The structure of the G-PCC parameter set in the bitstream.

[0567] FIG. 25 The structural information may include a G-PCC TLV package structure that carries G-PCC parameter sets (such as sequence parameter set (SPS), geometric parameter set (GPS), attribute parameter set (APS), and tile parameter set (TPS)).

[0568] numOfSetupUnitArrays can specify the number of arrays of G-PCC setup units of the type indicated by the setupUnitType field.

[0569] The `setupUnitType` indicates the type of G-PCC setup unit notified by signal. It can be one of the values ​​indicating SPS, GPS, APS, or TPS.

[0570] A setupUnit_completeness of 1 indicates that all setup units of a given type are in the array below, and none are in the stream. A setupUnit_completeness of 0 indicates that additional setup units of the type may be in the stream.

[0571] numOfSetupUnits can specify the number of G-PCC setup units of the type indicated by the setupUnitType field, which is signaled in the structure.

[0572] setupUnit is an instance of a TLV encapsulation structure that carries a setup unit of an indication type, such as SPS, GPS, APS, or TPS.

[0573] GPCCParameterSetBox MAY contain GPCCParameterSetStruct().

[0574] class GPCCParameterSetBox extends Box('gpsb'){

[0575] GPCCParameterSetStruct();

[0576] }

[0577] G-PCC entry information structure

[0578] class GPCCEntryInfoBox extends Box('gpsb'){

[0579] GPCCEntryInfoStruct();

[0580] }

[0581] aligned(8) class GPCCEntryInfoStruct{

[0582] unsigned int(1) main_entry_flag;

[0583] unsigned int(1) dependent_on;

[0584] if(dependent_on){ / / non-entry

[0585] unsigned int(16) dependency_id;

[0586] }

[0587] }

[0588] main_entry can indicate whether the entry point is used for decoding the G-PCC bitstream.

[0589] dependent_on can indicate whether the decoding depends on others. When dependent_on is present in a sample entry, the decoding of the sample in the track can depend on other tracks.

[0590] dendency_id indicates an identifier of a track on which the decoding of the associated data depends. When dendency_id is present in a sample entry, it can indicate an identifier of a track of a G-PCC sub-bitstream on which the decoding of the samples in the track depends. When dendency_id is present in a sample group, it can indicate an identifier of a sample of a G-PCC sub-bitstream on which the decoding of the associated samples depends.

[0591] (G-PCC component information structure)

[0592] aligned(8) class GPCCComponentTypeStruct {

[0593] unsigned int(8) numOfComponents;

[0594] for(i = 0; i < numOfComponents; i++) {

[0595] unsigned int(8) gpcc_type;

[0596] if(gpcc_type == 4)

[0597] unsigned int(8) AttrIdx;

[0598] }

[0599] / / Additional fields

[0600] }

[0601] numOfComponents indicates the number of component types signaled in this structure.

[0602] gpcc_type indicates the type of G-PCC component as specified in the following table.

[0603] Table 6

[0604] gpcc type value description 1 reserved 2 geometry component 3 reserved 4 attribute component 5..31 reserved

[0605] AttrIdx indicates the identifier of an attribute signaled in SPS().

[0606] This box can contain GPCCComponentTypeStruct. When this box is present in a sample entry of a track carrying a part or all of a G-PCC bitstream, it can indicate one or more G-PCC component types carried by the corresponding track.

[0607] aligned(8) class GPCCComponentTypeBox extends FullBox('gtyp', version =, 0) {

[0608] GPCCComponentTypeStruct();

[0609] }

[0610] A file or track according to embodiments can include a sample group.

[0611] Sample group

[0612] G-PCC parameter set sample group

[0613] A method / apparatus according to embodiments can group one or more samples that can apply the same G-PCC parameter set, and signal the parameter set related to the sample group as follows.

[0614] The 'gpsg' grouping_type for sample grouping can indicate that the samples in the track carrying part or all of the G-PCC bitstream are assigned to the G-PCC parameter set (e.g., SPS, GPS, APS, TPS) carried in this sample group. When there is a SampleToGroupBox with grouping_type equal to 'gpsg', there can be an accompanying SampleGroupDescriptionBox with the same grouping type and containing the ID of the group to which the samples belong.

[0615] aligned(8) class GPCCParameterSetSampleGroupDescriptionEntry()

[0616] extends SampleGroupDescriptionEntry('gpsg') {

[0617] GPCCParameterSetStruct();

[0618] }

[0619] GPCCParameterSetStruct() can contain the G-PCC parameter set (e.g., SPS, GPS, APS, TPS) that applies to the samples of this sample group.

[0620] G-PCC entry information sample group

[0621] One or more samples to which the same entry information can apply can be grouped, and entry information related to a sample group can be signaled as follows.

[0622] A 'gpei' grouping_type for sample grouping can indicate that samples in a track are assigned to the entry information carried in this sample group (e.g., whether the associated sample is an entry point). When there is a SampleToGroupBox with grouping_type equal to 'gpei', there can be an accompanying SampleGroupDescriptionBox with the same grouping type and containing the ID of the group to which the samples belong.

[0623] aligned(8) class GPCCParameterSetSampleGroupDescriptionEntry()

[0624] extends SampleGroupDescriptionEntry('gpei') {

[0625] GPCCEntryInfoStruct();

[0626] }

[0627] The GPCCEntryInfoStruct can contain entry information (e.g., whether the associated sample is an entry point) that applies to the samples of this sample group.

[0628] G-PCC component type sample group

[0629] The method / apparatus according to embodiments can group one or more samples to which the same component type information can apply, and component type information related to a sample group can be signaled as follows.

[0630] A 'gpct' grouping_type for sample grouping can indicate that samples in a track are assigned to the component type information in this sample group. When there is a SampleToGroupBox with grouping_type equal to 'gpct', there can be an accompanying SampleGroupDescriptionBox with the same grouping type and containing the ID of the group to which the samples belong.

[0631] aligned(8) class GPCCParameterSetSampleGroupDescriptionEntry()

[0632] extends SampleGroupDescriptionEntry( 'gpct' ) {

[0633] GPCCComponentTypeStruct();

[0634] }

[0635] GPCCComponentTypeStruct can contain component type information that applies to samples of the sample group. When multiple types are present in the structure, the order of the signaled component types indicates the order of the associated sub-samples in each sample of the sample group.

[0636] Track grouping

[0637] Methods / apparatuses according to embodiments can group tracks.

[0638] G-PCC parameter set track group

[0639] Methods / apparatuses according to embodiments can group one or more tracks that can apply the same G-PCC parameter set, and signal G-PCC parameter set information related to a track group as follows.

[0640] TrackGroupTypeBox with track_group_type equal to 'gptg' can indicate that the track belongs to a group of tracks associated with a G-PCC parameter set (e.g., SPS, GPS, APS, TPS).

[0641] Tracks belonging to the same G-PCC parameter set (e.g., SPS, GPS, APS, TPS) can have the same track_group_id value for track_group_type 'gptg', and the track_group_id of a track from one G-PCC parameter set (e.g., SPS, GPS, APS, TPS) can be different from the track_group_id of a track from any other G-PCC parameter set.

[0642] aligned(8) class GPCCParameterSetGroupBox extends TrackGroupTypeBox( 'gptg' ) {

[0643] GPCCParameterSetStruct();

[0644] }

[0645] A GPCCParameterSetStruct can contain the G-PCC parameter sets (e.g., SPS, GPS, APS, TPS) that apply to the tracks of this group. When there is a timed metadata track that carries dynamic changing G-PCC parameter sets or a sample group that carries G-PCC parameter sets, the signaled GPCCParameterSetStruct() can indicate the initial G-PCC parameter sets.

[0646] FIG. 26 A sample structure for single track encapsulation is shown according to an embodiment.

[0647] Methods / apparatuses according to embodiments can encapsulate and decapsulate G-PCC bitstreams based on a single track (see FIG. 26 ) and / or multiple tracks (see FIG. 27 ). For example, this operation can be performed by the sending apparatus 10000, the receiving apparatus 10004, the file / segment encapsulator and file / segment decapsulator of the encoding 20001 and sending 20002, the sending code 20002 and decoding 20003, FIGS. 14-16 .

[0648] Single track encapsulation of G-PCC data in ISOBMFF

[0649] When a G-PCC bitstream is carried in a single track, it requires that the G-PCC encoded bitstream is represented by a single track declaration. Single track encapsulation of G-PCC data can utilize a simple ISOBMFF encapsulation by storing the G-PCC bitstream in a single track without further processing.

[0650] Each sample in this G-PCC bitstream track can contain one or more G-PCC components. That is, each sample can consist of one or more TLV encapsulation structures. When a G-PCC bitstream is stored in a single track, the sample structure can be configured as shown in FIG. 26 .

[0651] In the following, data structures included in a single track will be described.

[0652] Sample entry

[0653] Sample entry type: 'gpe1', 'gpeg'

[0654] Container: SampleDescriptionBox

[0655] Mandatory: 'gpe1' or 'gpeg' sample entry is mandatory

[0656] Number: There can be one or more sample entries

[0657] A G-PCC bitstream track can use a VolumetricVisualSampleEntry with sample entry type 'gpe1' or 'gpeg'.

[0658] GPCC configuration box

[0659] A G-PCC bitstream track sample entry can contain a GPCCConfigurationBox. The setupUnit array can include a TLV encapsulation structure containing one SPS.

[0660] Under the 'gpe1' sample entry, all parameter sets (such as SPS, GPS, and APS) and tile lists can be in the setupUnit array.

[0661] Under the 'gpeg' sample entry, parameter sets can be present in the array or in the stream.

[0662] aligned(8) class GPCCSampleEntry() extends VolumetricVisualSampleEntry('gpe1') {

[0663] GPCCConfigurationBox config; / / mandatory

[0664] The sample format of a single track according to embodiments can be as follows.

[0665] Sample format

[0666] Each G-PCC bitstream sample can correspond to a single point cloud frame and can be composed of one or more TLV encapsulation structures belonging to the same presentation time.

[0667] Each TLV encapsulation structure can contain a single type of G-PCC payload (e.g., geometry slice, attribute slice). A sample can be self-contained (e.g., a sync sample).

[0668] aligned(8) class GPCCSample

[0669] {

[0670] unsigned int GPCCLength = sample_size; / / sample size

[0671] for (i = 0; i < GPCCLength;) / / to the end of the sample

[0672] {

[0673] tlv_encapsulation gpcc_unit;

[0674] i += (1 + 4) + gpcc_unit.tlv_num_payload_bytes;

[0675] }

[0676] }

[0677] A gpcc_unit can contain an instance of G-PCC TLV encapsulation structure, which contains a single G-PCC component. The G-PCC component can be referred to as a G-PCC data unit.

[0678] A track according to embodiments can include sub-samples.

[0679] Sub-samples

[0680] A G-PCC sub-sample can contain only one G-PCC TLV encapsulation structure. SubSampleInformation can be present in SampleTableBox or in TrackFragmentBox of each MovieFragmentBox.

[0681] When the 8-bit type value of a TLV encapsulation structure (if present) and the TLV encapsulation structure contains an attribute payload, the 6-bit value of the attribute index can be included in the 32-bit codec_specific_parameters field of the sub-sample entry in SubSampleInformationBox. The type of each sub-sample can be identified by parsing the codec_specific_parameters field of the sub-sample entry in SubSampleInformationBox. The codec_specific_parameters field of SubsampleInformationBox can be defined as follows:

[0682] unsigned int(8) PayloadType;

[0683] if (PayloadType == 4) { / / attribute payload

[0684] unsigned int(6) AttrIdx;

[0685] bit(18) reserved = 0;

[0686] }

[0687] else

[0688] bit(24) reserved = 0;

[0689] PayloadType can indicate the tlv_type of the TLV encapsulation structure in the sub-sample.

[0690] AttrIdx can indicate the ash_attr_sps_attr_idx of the TLV encapsulation structure containing the attribute payload in the sub-sample.

[0691] FIG. 27 A multi-track container according to an embodiment is illustrated.

[0692] FIG. 27 A multi-track structure in which the bitstream of FIG. 18 is encapsulated is illustrated.

[0693] For example, these operations can be performed by the sending device 10000, the receiving device 10004, the encoding 20001 and sending 20002, the sending 20002 and decoding 20003, FIGS. 14-16 the file / segment encapsulator and the file / segment decapsulator.

[0694] Different G-PCC components are examples of multi-track containers of G-PCC bitstreams carried in individual tracks, as illustrated in FIG. 27 .

[0695] Multi-track container of G-PCC bitstreams

[0696] When carrying G-PCC bitstreams in multiple tracks, individual geometry or attribute sub-bitstreams can be mapped to individual tracks. There can be two types of G-PCC component tracks: geometry tracks and attribute tracks. Geometry tracks can carry geometry sub-streams, and attribute tracks can carry a single type of attribute sub-stream. Individual samples in a track can contain at least one TLV encapsulation structure, which carries a single G-PCC component (not a multiplex of geometry and attribute data, or different attribute data). The overall layout of a multi-track ISOBMFF G-PCC container is illustrated in FIG. 27 .

[0697] Multi-track encapsulation of G-PCC bitstreams can enable G-PCC players (10004, etc.) to efficiently access components. For example, geometry should be decoded first, and attributes can depend on the decoded geometry. A player according to an embodiment can access a track carrying geometry bitstreams before the attribute bitstreams. G-PCC component tracks can be created as follows:

[0698] In the sample entry, a new box can be added to indicate the role of the stream contained in this track.

[0699] One track carrying a geometry sub-bitstream can be an entry point.

[0700] The method / apparatus according to embodiments can generate and send a sample entry as follows.

[0701] Sample entry

[0702] Sample entry type: 'gpc1' or 'gpcg'

[0703] Container: SampleDescriptionBox ('stsd')

[0704] Mandatory: 'gpc1', 'gpcg' sample entries are mandatory Number: There can be one or more sample entries

[0705] A G-PCC geometry or attribute track can use VolumetricVisualSampleEntry with sample entry type 'gpc1' or 'gpcg'.

[0706] The sample entry can contain GPCCConfigurationBox and GPCCComponentTypeBox.

[0707] According to ISO Base Media File Format, multiple sample entries can be used to indicate parts of G-PCC data using different configurations and parameter sets.

[0708] aligned(8) class GPCCSampleEntry() extends VolumetricVisualSampleEntry ('gpc1') {

[0709] GPCCConfigurationBox config;

[0710] GPCCComponentTypeBox();

[0711] GPCCEntryInfoBox();

[0712] }

[0713] GPCCEntryInfoBox can present entry information of this track, for example, such as whether this track is an entry point for G-PCC bitstream decoding.

[0714] config can include G-PCC decoder configuration record information.

[0715] The GPCCComponentTypeBox can indicate one or more G-PCC component types carried in the track. When multiple types of G-PCC component data can be multiplexed in one track. For example, a G-PCC geometry and one G-PCC attribute sub-bitstream can be multiplexed in a track. The GPCCComponentTypeBox can contain multiple G-PCC component data carried by the corresponding track.

[0716] When multiple component types are indicated and there is no component type sample group, the order of the signaled component types in the GPCCComponentTypeBox can indicate the order of the associated sub-samples in the respective samples in the track.

[0717] FIG. 28 A sample structure according to an embodiment is shown.

[0718] FIG. 28 A sample contained in a track of a file according to an embodiment is shown.

[0719] A method / apparatus according to an embodiment can generate the following sample.

[0720] Sample format

[0721] Each sample can contain one or more TLV structures. An example of a sample structure for a track carrying one geometry sub-bitstream is shown in FIG. 28 .

[0722] aligned(8) class GPCCSample

[0723] {

[0724] unsigned int GPCCLength = sample_size; / / sample size

[0725] for(i = 0; i < GPCCLength;) / / to the end of the sample

[0726] {

[0727] tlv_encapsulation gpcc_unit;

[0728] i += (1 + 4) + gpcc_unit.tlv_num_payload_bytes;

[0729] }

[0730] }

[0731] The gpcc_unit can contain an instance of the G-PCC TLV encapsulation structure including a single G-PCC component.

[0732] When multiplexing multiple types of G-PCC component data in one track, for example, when multiplexing a G-PCC geometry and one G-PCC attribute sub-bitstream in a track, there can be one or more sub-samples in each sample. One SubSampleInformation can be in the SampleTableBox, or can be in the TrackFragmentBox of each MovieFragmentBox.

[0733] When the 8-bit type value of the TLV encapsulation structure (if present) and the TLV encapsulation structure contains an attribute payload, the 6-bit value of the attribute index can be included in the 32-bit codec_specific_parameters field of the sub-sample entry in the SubSampleInformationBox. The type of each sub-sample can be identified by parsing the codec_specific_parameters field of the sub-sample entry in the SubSampleInformationBox. The codec_specific_parameters field of the SubsampleInformationBox can be defined as follows:

[0734] unsigned int(8) PayloadType;

[0735] if (PayloadType == 4) { / / attribute payload

[0736] unsigned int(6) AttrIdx;

[0737] bit(18) reserved = 0;

[0738] }

[0739] else

[0740] bit(24) reserved = 0;

[0741] PayloadType can indicate the TLV type of the TLV encapsulation structure in the sub-sample.

[0742] AttrIdx can indicate the ash_attr_sps_attr_idx of the TLV encapsulation structure containing the attribute payload in the sub-sample.

[0743] When SubSampleInformation is not present, the order of subsamples in a sample can follow the order of component types signaled in the sample entry or sample group.

[0744] The method / apparatus according to embodiments can provide references to G-PCC component tracks as follows.

[0745] Referencing G-PCC component tracks

[0746] To link a G-PCC geometry track to other tracks, the track reference tool of ISOBMFF can be used. One TrackReferenceTypeBox can be added to the TrackReferenceBox within the TrackBox of the G-PCC geometry track. The TrackReferenceTypeBox can contain an array of track_IDs that specify the tracks referenced by the G-PCC geometry track.

[0747] The reference_type of the TrackReferenceTypeBox can identify attribute tracks. The 4CCs of these track reference types are:

[0748] ‘gpca’: The referenced tracks can contain encoded bitstreams of G-PCC attribute data

[0749] When there are multiple tracks and each track includes multiplexed G-PCC sub-bitstreams, a new track reference can be suggested. One TrackReferenceTypeBox can be added to the TrackReferenceBox within the TrackBox of the track indicated as the entry point. The TrackReferenceTypeBox can contain an array of track_IDs that specify the tracks referenced by the G-PCC track.

[0750] The 4CCs of these track reference types are:

[0751] ‘gpcs’: The referenced tracks can contain other parts of the encoded G-PCC bitstream.

[0752] FIG. 29 Parameter sets contained in a timing metadata track according to embodiments are shown.

[0753] The method / apparatus according to embodiments can create a timing metadata track in a file, and this timing metadata track can carry parameter sets.

[0754] Timing metadata track

[0755] G-PCC parameter set timing metadata track

[0756] A dynamic G-PCC parameter set timing metadata track can indicate that G-PCC parameter sets (SPS, GPS, APS, TPS) can change dynamically over time.

[0757] The timing metadata track can be linked to the respective tracks carrying part or all of the G-PCC bitstream by referencing the 'cdsc' track. The timing metadata track can be linked to the respective group of tracks associated with the same G-PCC parameter set by referencing the 'cdsc' track.

[0758] A sample entry of the timing metadata track can contain a GPCCParameterSetBox, which includes the default G-PCC parameter set applied to the corresponding G-PCC content.

[0759] The sample format of the timing metadata track is shown in FIG. 29 .

[0760] num_active_parameters specifies the number of active G-PCC parameter sets signaled in the sample entry. num_active_parameters equal to 0 indicates that no G-PCC parameter set from the sample entry is active.

[0761] addl_active_parameters equal to 1 can specify additional active G-PCC parameter sets signaled directly in the sample in GPCCParameterSetStruct(). addl_active_parametersets equal to 0 can specify that no additional active G-PCC parameter sets are signaled directly in the sample.

[0762] active_parameter_set_type can indicate the type of the active G-PCC parameter set.

[0763] active_parameter_set_id indicates the identifier of the active G-PCC parameter set of the indicated parameter set type.

[0764] GPCCParameterSetStruct() can contain additional G-PCC parameter sets (e.g., SPS, GPS, APS, TPS) directly in the sample.

[0765] The method / apparatus according to the embodiments can process the point cloud data at the sending side or the encoder side as follows. In the point cloud data receiving method / apparatus according to the embodiments, the processing opposite to that of the sending method / apparatus can be performed.

[0766] The file packaging, file packager, transmitting device or encoder (10000, 10002, FIG. 12 , FIGS. 14-15 and FIG. 17 ) generates and stores tracks in a file according to the degree of change of parameter sets in the G-PCC bitstream, and stores the related signaling information.

[0767] In addition, the file packaging, file packager, transmitting device or encoder (10000, 10002, FIG. 12 , FIGS. 14-15 and FIG. 17 ) according to the embodiments can add the signaling information according to the embodiments in one or more tracks in the file. The track according to the embodiments can be a media track including part or all of the G-PCC bitstream or a metadata track associated with the G-PCC bitstream.

[0768] The file unpackager, receiving device or decoder (10004, 10006, FIGS. 13-17 ) according to the embodiments can obtain the information such as the signaling and parameter sets included in the tracks in the file, and based on this, the track data in the file can be efficiently extracted, decoded and post-processed.

[0769] Due to the operation of the file packager and the file unpackager (G-PCC system) according to the embodiments, the point cloud data transmitting / receiving method / device according to the embodiments can provide the following effects.

[0770] The transmitter or receiver for providing point cloud content services according to the scheme proposed in the disclosure constructs a G-PCC bitstream and stores the file as described above. In addition, the G-PCC samples are defined and stored in the file. In addition, the sub-samples can be stored in the G-PCC bitstream file. Therefore, the transmitter or receiver for providing point cloud content services can support efficient access to the stored G-PCC bitstream.

[0771] It can realize efficient multiplexing of G-PCC bitstreams. Efficient access to the bitstream can be supported on the basis of G-PCC access units.

[0772] The metadata for data processing and rendering in the G-PCC bitstream can be sent in the bitstream.

[0773] The parameter sets for decoding and processing the partial streams and the entire stream of the G-PCC bitstream can be efficiently stored and sent in the file. In addition, by storing and carrying the parameter sets in the tracks of the file, the G-PCC decoder / player can be allowed to correctly operate when decoding the partial or entire G-PCC bitstream or parsing and processing the partial or entire G-PCC required in the track.

[0774] The data representation method according to the embodiments can support efficient access to a point cloud bitstream.

[0775] The transmitter or receiver according to the embodiments can efficiently store and transmit a file of a point cloud bitstream through a technique for dividing and storing a G-PCC bitstream as one or more tracks in a file, signaling for this, and signaling a relationship between the stored tracks of the G-PCC bitstream.

[0776] The method / apparatus according to the embodiments can be described in connection with the G-PCC data transmission method and apparatus described below.

[0777] Data of a G-PCC and a G-PCC system can be generated by an encapsulator (which can be referred to as a generator) of a transmission apparatus according to the embodiments and transmitted by a transmitter of the transmission apparatus. In addition, the data of the G-PCC and the G-PCC system described above can be received by a receiver of a reception apparatus of the embodiments and acquired by a decapsulator (which can be referred to as a parser) of the reception apparatus. According to the embodiments, a decoder, a Tenderer, etc. of the reception apparatus can provide appropriate point cloud data to a user based on the data of the G-PCC and the G-PCC system.

[0778] The method / apparatus according to the embodiments can process point cloud data and generate related signaling information based on a media format for supporting partial access to G-PCC data.

[0779] The method / apparatus according to the embodiments can store 3D tile configuration information of point cloud data and generate signaling information.

[0780] The method / apparatus according to the embodiments can store point cloud data associated with 3D tiles in tracks and group tracks associated with the same 3D spatial region.

[0781] The method / apparatus according to the embodiments can store static or dynamic 3D tile information of point cloud data.

[0782] When G-PCC data is transmitted based on a plurality of tracks, the method / apparatus according to the embodiments can generate signaling information related to an association between the tracks.

[0783] The method / apparatus according to the embodiments can process point cloud data based on a user viewport and use only a part of the point cloud data. For this, a method for extracting only necessary point cloud data from the entire point cloud data in a file and decoding the same is required. The method / apparatus according to the embodiments can support such an operation.

[0784] The point cloud data can be composed of one or more 3D tiles. The sender can send 3D tile configuration information of the point cloud data, thereby allowing the receiver or player to extract only the point cloud data existing in the desired area from the file. Accordingly, the 3D tile information of the point cloud data included in the track according to the embodiment can be carried in a sample entry, a sample group, or a separate metadata track.

[0785] When the point cloud data associated with a specific 3D tile area is sent in one or more tracks, track grouping signaling can be provided, which allows the receiver to acquire the point cloud data from the associated track when using the point cloud of the area. With the signaling configuration according to the embodiment, spatial information of a dynamic 3D tile associated with a corresponding track group can be sent in a metadata track or the like.

[0786] The method for sending point cloud data according to the embodiment includes encoding the point cloud data, encapsulating the point cloud data, and sending the point cloud data.

[0787] The point cloud data according to the embodiment is file-encapsulated, and the file includes a track for a parameter set of the point cloud data, the parameter set including a sequence parameter set, a geometry parameter set, an attribute parameter set, and a tile parameter set.

[0788] FIG. 30 A tile list according to the embodiment is illustrated.

[0789] FIG. 30 The tile list can correspond to FIG. 20 The tile list.

[0790] tile_frame_idx can contain an identification number that can be used for the purpose of identifying the tile list.

[0791] tile_seq_parameter_set_id specifies the value of the ID of the SPS sequence parameter set of the active SPS.

[0792] tile_id_present_flag equal to 1 specifies that the tiles are identified according to the value of the tile_id syntax element. tile_id_present_flag equal to 0 specifies that the tiles are identified according to their position in the tile list.

[0793] tile_cnt specifies the number of tile bounding boxes existing in the tile list.

[0794] tile_bounding_box_bits specifies the bit depth to represent the bounding box information of the tile list.

[0795] tile_id identifies a particular tile within tile_inventory. When not present, the value of tile_id can be inferred to be the index of the tile in the tile inventory as given by the loop variable tileldx. All values of tile_id can be unique within one tile inventory.

[0796] tile_bounding_box_offset_xyz[tileId][k] and tile_bounding_box_size_xyz[tileId][k] specify a bounding box that contains the slice identified by gsh_tile_id equal to tileId. tile_bounding_box_offset_xyz[tileId][k] is the k-th component of the (x, y, z) origin coordinates of the tile bounding box relative to TileOrigin[k].

[0797] tile_bounding_box_size_xyz[tileId][k] is the k-th component of the tile bounding box with width, height, and depth.

[0798] tile_origin_xyz[k] specifies the k-th component of the tile origin in Cartesian coordinates. The value of tile_origin_xyz[k] can be equal to sps_bounding_box_offset[k].

[0799] tile_origin_log2_scale specifies a scaling factor to scale the components of tile_origin_xyz. The value of tile_origin_log2_scale can be equal to sps_bounding_box_offset_log2_scale. An array TileOrigin with elements TileOrigin[k] for k = 0,..., 2 can be derived as follows:

[0800] TileOrigin[k] = tile_origin_xyz[k] « tile_origin_log2_scale.

[0801] FIG. 31 A G-PCC 3D tile information structure according to an embodiment is shown.

[0802] FIG. 31 A syntax of G-PCC 3D tile information included in a file FIG. 26 and FIG. 27 is shown.

[0803] tile_id is an identifier of a 3D tile.

[0804] tile_frame_idx specifies an identifier of an associated point cloud frame associated with the 3D tile.

[0805] tile_bounding_box_offset_x, tile_bounding_box_offset_y, and tile_bounding_box_offset_z indicate the x, y, and z offsets of the 3D tile in Cartesian coordinates.

[0806] tile_bounding_box_size_width, tile_bounding_box_size_height, and tile_bounding_box_size_depth indicate the width, height, and depth of the 3D tile in Cartesian coordinates.

[0807] FIG. 32 A structure of G-PCC 3D tile manifest information according to an embodiment is shown.

[0808] FIG. 32 Syntax of G-PCC 3D tile manifest information included in a file FIG. 26 and FIG. 27

[0809] tile_frame_idx specifies an identifier of an associated point cloud frame associated with the tile manifest structure.

[0810] num_tiles_minus1 plus 1 specifies the number of tile bounding boxes present in the tile manifest.

[0811] tile_id is an identifier of the i-th tile.

[0812] tile_bounding_box_offset_x[i], tile_bounding_box_offset_y[i], and tile_bounding_box_offset_z[i] indicate the x, y, and z offsets of the i-th tile in Cartesian coordinates.

[0813] tile_bounding_box_size_width[i], tile_bounding_box_size_height[i], and tile_bounding_box_size_depth[i] indicate the width, height, and depth of the i-th tile in Cartesian coordinates.

[0814] ​tile_origin_x, tile_orign_y and tile_origin_z specify the x, y, z values of the tile origin in the Cartesian coordinate.

[0815] tile_origin_log2_scale specifies a scale factor to scale the components tile_origin_x, tile_orign_y and tile_origin_z.

[0816] The file and tracks according to the embodiments can group and carry samples as follows.

[0817] Sample group

[0818] 3D tile sample group

[0819] The method / apparatus according to the embodiments can group one or more samples associated with the same 3D tile, and generate and signal 3D tile information related to the group as follows.

[0820] The '3tsg' grouping_type for sample grouping indicates assigning samples in a track to the spatial region (including cuboid region) information carried in the sample group.

[0821] When there is a SampleToGroupBox with grouping_type equal to'srsg', there can be a SampleGroupDescriptionBox with the same grouping type, and it can contain the ID of the group to which the samples belong.

[0822] aligned(8) class GPCC3DTileSampleGroupDescriptionEntry()

[0823] extends SampleGroupDescriptionEntry('3tsg') {

[0824] TileInfoSturct();

[0825] }

[0826] TileInfoSturct can contain 3D tile information of samples applied to the sample group.

[0827] The method / apparatus according to the embodiments can group 3D tile manifest samples of a file or track.

[0828] 3D tile manifest sample group

[0829] The method / apparatus according to embodiments can group one or more samples associated with the same tile inventory information, and store and signal the tile inventory information associated with the group as follows.

[0830] The 'tisg' grouping_type for sample grouping can indicate that samples in the track are assigned to the tile inventory information carried in the sample group. When there is a SampleToGroupBox with grouping_type equal to 'tisg', there can be a SampleGroupDescriptionBox with the same grouping type and containing the ID of the group to which the samples belong.

[0831] aligned(8) class GPCCCubicRegionSampleGroupDescriptionEntry()

[0832] extends SampleGroupDescriptionEntry('tisg') {

[0833] TileInventoryStruct();

[0834] }

[0835] The TileInventoryStruct can contain the tile inventory information that applies to the samples of the sample group.

[0836] Parameter set sample group

[0837] The method / apparatus according to embodiments can group one or more samples associated with the same G-PCC parameter set (which can include SPS, GPS, APS, tile inventory, etc.), and generate and signal the G-PCC parameter set associated with the group as follows.

[0838] The 'pasg' grouping_type for sample grouping can indicate that samples in the track are assigned to the G-PCC parameter set (e.g., SPS, GPS, APS, tile inventory) carried in the sample group. When there is a SampleToGroupBox with grouping_type equal to 'pasg', there can be a SampleGroupDescriptionBox with the same grouping type and containing the ID of the group to which the samples belong.

[0839] aligned(8) class GPCCParameterSetSampleGroupDescriptionEntry()

[0840] extends SampleGroupDescriptionEntry('pasg'){

[0841] unsigned int(8)numOfSetupUnitArrays;

[0842] for(i=0; i <numOfSetupUnitArrays;i++){

[0843] unsigned int(7)setupUnitType;

[0844] unsigned int(8)numOfSetupUnits;

[0845] for(i=0; i <numOfSetupUnits;i++){

[0846] tlv_encapsulation setupUnit;

[0847] }

[0848] }

[0849] }

[0850] numOfSetupUnitArrays specifies the number of arrays of G-PCC setup units of the type indicated by the setupUnitTye field.

[0851] `setupUnitTye` indicates the type of G-PCC setup unit notified by signal. It can be one of the values ​​indicating SPS, GPS, APS, and tile list.

[0852] numOfSetupUnits specifies the number of G-PCC setup units of the type indicated by the setupUnitType field in the record, which is signaled by a signal.

[0853] setupUnit is an instance of a TLV encapsulation structure that carries a setup unit of an indicated type, such as SPS, GPS, APS, or a list of pieces.

[0854] Track grouping

[0855] GPCC 3D Patch Track Grouping

[0856] The method / apparatus according to the implementation can group one or more tracks carrying data belonging to the same 3D tile of point cloud data, and generate and notify them by signals as follows.

[0857] A TrackGroupTypeBox with track_group_type equal to '3dtg' can indicate that the track belongs to a track group that is associated with the same spatial region.

[0858] Tracks belonging to the same spatial region can have the same track_group_id value for track_group_type '3dtg', and the track_group_id of tracks from one 3D tile can be different from the track_group_id of tracks from other 3D tiles.

[0859] aligned(8) class GPCC3DTileTrackGroupBox extends TrackGroupTypeBox('3dtg') {

[0860] TileInfoStruct();

[0861] }

[0862] TileInfoStruct can contain 3D tile information that applies to the tracks of the group. When there is a timed metadata track that carries associated 3D tile information or spatial region information, the initial 3D tile information can be indicated.

[0863] Each track can belong to one or more 3D tile track groups.

[0864] GPCC tile group list track grouping

[0865] The method / apparatus according to embodiments can group one or more tracks that apply the same tile group list of point cloud data, and generate and signal information as follows.

[0866] A TrackGroupTypeBox with track_group_type equal to 'titg' indicates that the track belongs to a track group that applies the same tile group list information.

[0867] Tracks belonging to the same tile group list can have the same track_group_id value for track_group_type 'titg', and the track_group_id of tracks from one tile group list can be different from the track_group_id of tracks from another tile group list.

[0868] aligned(8) class GPCCTileInventoryTrackGroupBox extends TrackGroupTypeBox('titg') {

[0869] TileInventoryStruct();

[0870] }

[0871] TileInventoryStruct can contain tile inventory information that applies to the tracks of the group. When there is a timed metadata track that carries the associated tile inventory information or spatial region information, the initial tile inventory information can be indicated.

[0872] Each track can belong to one or more tile inventory track groups.

[0873] / / GPCC tile inventory track grouping

[0874] The method / apparatus according to embodiments can group one or more tracks that apply the same G-PCC parameter set (which can include SPS, GPS, APS, and tile inventory), and generate the following information.

[0875] TrackGroupTypeBox with track_group_type equal to 'patg' can indicate that the track belongs to a track group that applies the same parameter set.

[0876] Tracks that belong to the same tile inventory can have the same track_group_id value for track_group_type 'patg', and the track_group_id of a track from one tile inventory is different from the track_group_id of a track from another tile inventory.

[0877] aligned(8) class GPCCParameterSetTrackGroupBox extends TrackGroupTypeBox('patg') {

[0878] unsigned int(8) numOfSetupUnitArrays;

[0879] for (i = 0; i < numOfSetupUnitArrays; i++) {

[0880] unsigned int(7) setupUnitType;

[0881] unsigned int (8) numOfSetupUnits;

[0882] for (i = 0; i < numOfSetupUnits; i++) {

[0883] tlv_encapsulation setupUnit;

[0884] }

[0885] }

[0886] }

[0887] numOfSetupUnitArrays specifies the number of arrays of G-PCC setup units of the type indicated by the setupUnitTye field.

[0888] setupUnitTye indicates the type of G-PCC setup unit signaled. It can be one of the values indicating SPS, GPS, APS and tile manifest.

[0889] numOfSetupUnits specifies the number of G-PCC setup units of the type indicated by the setupUnitType field signaled in the record.

[0890] setupUnit is an instance of the TLV encapsulation structure carrying a setup unit of the specified type (e.g. SPS, GPS, APS or tile manifest).

[0891] Encapsulation of G-PCC data in ISOBMFF

[0892] The method / apparatus (e.g. sending apparatus, encapsulator of an encoder, receiving apparatus or decapsulator of a decoder) according to embodiments can encapsulate data of an encoded G-PCC bitstream into a single track and / or multiple tracks and decapsulate it based on ISOBMFF (see FIGS. 26-28 ).

[0893] Reference FIG. 28 When carrying a G-PCC bitstream in multiple tracks, the track carrying the G-PCC geometry bitstream can be the entry point.

[0894] In the sample entry, a new box can be added indicating the role of the stream contained in this track.

[0895] Track references can be introduced from the track carrying only the G-PCC geometry bitstream to the track carrying the G-PCC attribute bitstream.

[0896] Sample entry

[0897] sample entry type: 'gpe1', 'gpeg', 'gpc1', or 'gpcg'

[0898] container: SampleDescriptionBox

[0899] mandatory: 'gpe1', 'gpeg', 'gpc1', or 'gpcg' sample entries are mandatory number of sample entries: there can be one or more sample entries

[0900] A G-PCC track can use VolumetricVisualSampleEntry with sample entry type 'gpe1', 'gpeg', 'gpc1', or 'gpcg'.

[0901] A G-PCC sample entry can contain GPCCConfigurationBox and optionally GPCCComponentTypeBox

[0902] Under 'gpe1' sample entry, all parameter sets (as defined in ISO / IEC 23090-9 [GPCC]) can be in the setupUnit array. Under 'gpeg' sample entry, parameter sets can be present in the array. Under 'gpe1' or 'gpeg' sample entry, GPCCComponentTypeBox can not be present. Under 'gpe1' sample entry, all SPS, GPS, and tile lists (as defined in ISO / IEC 23090-9 [GPCC]) can be in the SetupUnit array of the track carrying the G-PCC geometry bitstream. All associated APS can be in the SetupUnit array of the track carrying the G-PCC attribute bitstream. Under 'gpe1' sample entry, SPS, GPS, APS, or tile lists can be present in the array. Under 'gpe1' or 'gpeg' sample entry, GPCCComponentTypeBox can be present.

[0903] When parameter sets are used and need to be updated, parameter sets can be included in the samples of the stream.

[0904] aligned(8) class GPCCSampleEntry()

[0905] extends VolumetricVisualSampleEntry(codingname) {

[0906] GPCCConfigurationBox config; / / mandatory

[0907] GPCCComponentTypeBox type; / / optional

[0908] TileInventoryBox ( ) ;

[0909] }

[0910] The compressorname in the base class VolumetricVisualSampleEntry indicates the name of the compressor used, with the value of "\013GPCC Coding"; the first byte is the count of the remaining bytes, indicated by \013, which (octal 13) is 11 (decimal), and the number of bytes in the remainder of the string.

[0911] The "config" can include G-PCC decoder configuration record information.

[0912] The "type" indicates the type of G-PCC component carried in the corresponding track.

[0913] The TileInventoryBox indicates tile inventory information of point cloud data carried in samples in the track.

[0914] FIG. 33 A G-PCC base track according to an embodiment is shown.

[0915] FIG. 33 Syntax of a G-PCC base track included in a file and track, etc. according to FIGS. 26-28 In an embodiment, the sending apparatus 10000, the receiving apparatus 10004, the encoding 20001 and sending 20002, the sending 20002 and decoding 20003,

[0916] the file / segment encapsulator and file / segment decapsulator, etc. can generate, send and receive, and parse the G-PCC base track. FIGS. 14-16 G-PCC base track

[0917] The G-PCC base track can include a common parameter set that can be applied to one or more G-PCC tracks. This can use the 'gpcb' sample entry. The GPCCConfigurationBox can contain parameter sets (SPS, GPS, APS, tile inventory, etc.) to be applied to one or more G-PCC tracks.

[0918]

[0919] ​It can also contain parameter sets (SPS, GPS, APS, tile list, etc.) to be applied to one or more G-PCC tracks, which can change over time in the samples of the corresponding tracks.

[0920] aligned(8) class GPCCSampleEntry()

[0921] extends VolumetricVisualSampleEntry('gpcb') {

[0922] GPCCConfigurationBox config; / / Mandatory

[0923] }

[0924] One or more parameter sets can exist in each sample of the track.

[0925] The method / apparatus according to the embodiments can additionally generate a sub-sample for a sample.

[0926] Sub-sample

[0927] In the G-PCC base track, the G-PCC sub-sample can only contain one G-PCC TLV encapsulation structure. One SubSampleInformation can be in the SampleTableBox, or can be in the TrackFragmentBox of each MovieFragmentBox.

[0928] When the 8-bit type value of the TLV encapsulation structure (if present) and the TLV encapsulation structure contains attribute payload, the 6-bit value of the attribute index can be included in the 32-bit codec_specific_parameters field of the sub-sample entry in the SubSampleInformationBox. The type of each sub-sample can be identified by parsing the codec_specific_parameters field of the sub-sample entry in the SubSampleInformationBox. The codec_specific_parameters field of the SubsampleInformationBox can be defined as follows:

[0929] unsigned int(8) PayloadType;

[0930] if (PayloadType == 4) { / / Attribute payload

[0931] unsigned int (6) AttrIdx;

[0932] bit(18) reserved = 0;

[0933] }

[0934] else

[0935] bit(24) reserved = 0;

[0936] PayloadType can indicate the TLV type of the TLV encapsulation structure in the sub-sample.

[0937] Table 7

[0938] tlv type description 0 sequence parameter set 1 geometry parameter set 3 attribute parameter set 5 tile list 6 frame boundary marker

[0939] The method / apparatus according to embodiments can generate a G-PCC tile track in a track of a file.

[0940] G-PCC tile track

[0941] Sample entry type: 'get1'

[0942] Container: SampleDescriptionBox

[0943] Mandatory: Yes

[0944] Number: There can be one or more sample entries

[0945] The G-PCC tile track can use AtlasTileSampleEntry, which extends VolumetricVisualSampleEntry, with a sample entry type of 'v3t1'.

[0946] The G-PCC tile track can contain G-PCC data associated with one or more G-PCC tiles. When a GPCCComponentTypeBox is included in the sample entry, only data associated with one G-PCC component can be included in the G-PCC tile track, and the G-PCC component type included in the sample entry can be referred to. When a GPCCComponentTypeBox is not included in the sample entry, multiple G-PCC component data can be included in the track, and these data can be associated with the tile signaled in the sample entry.

[0947] aligned(8) class GPCCTileSampleEntry()

[0948] extends VolumetricVisualSampleEntry(‘get1’){

[0949] unsigned int(8) configurationVersion = 1;

[0950] unsigned int(1) multiplexed_flag;

[0951] unsigned int(16) num_tiles;

[0952] for (i = 0; i < num_tiles; i++) {

[0953] unsigned int(16) tile_id;

[0954] }

[0955] if (multiplexed_flag == 0)

[0956] GPCCComponentTypeBox type; / / Optional

[0957] }

[0958] configurationVersion is a version field. Incompatible changes to the sample entry can be indicated by a change in the version number.

[0959] multiplexed_flag can indicate whether one or more G-PCC component data is contained in the track. multiplexed_flag equal to 0 can indicate that only one G-PCC component data is contained. In this case, the type value can be included in the sample entry.

[0960] num_tiles indicates the number of tiles contained in the track.

[0961] tile_id specifies the identifier of the tile present in this track.

[0962] “type” indicates the type of G-PCC component carried in the corresponding track.

[0963] A 3D tile sample group can be present in the track. Therefore, tile information related to the samples contained in the track can be signaled.

[0964] The method / apparatus according to the embodiments can handle references between G-PCC tracks as follows.

[0965] References between G-PCC tracks

[0966] When carrying G-PCC bitstreams in multiple tracks, in order to link between tracks, the track reference tool is used. One TrackReferenceTypeBox can be added to the TrackReferenceBox within the TrackBox of a G-PCC track. The TrackReferenceTypeBox can contain an array of track_IDs that specify the tracks that the G-PCC track refers to.

[0967] In order to link a G-PCC base track to a G-PCC tile track that carries encoded bitstreams associated with one or more G-PCC tiles, the reference_type of the TrackReferenceTypeBox in the G-PCC base track can identify the G-PCC tile track. The 4CC of these track reference types can be 'gpbt'.

[0968] In order to link a G-PCC tile track that carries encoded bitstreams associated with one or more G-PCC tiles to a G-PCC base track, the reference_type of the TrackReferenceTypeBox in the G-PCC tile track can identify the G-PCC base track. The 4CC of these track reference types can be 'gptb'.

[0969] In order to link a G-PCC base track to a G-PCC track that carries encoded G-PCC bitstreams, the reference_type of the TrackReferenceTypeBox in the G-PCC base track can identify the G-PCC track. The 4CC of these track reference types can be 'gpbc'.

[0970] In order to link a G-PCC track that carries encoded G-PCC bitstreams to a G-PCC base track, the reference_type of the TrackReferenceTypeBox in the G-PCC tile track can identify the G-PCC base track. The 4CC of these track reference types can be 'gpcb'.

[0971] In order to link a G-PCC geometry track to a G-PCC attribute track, the reference_type of the TrackReferenceTypeBox in the G-PCC geometry track can identify the associated attribute track. The 4CC of these track reference types is:

[0972] 'gpca': The referenced track can contain an encoded bitstream of G-PCC attribute data.

[0973] The method / apparatus according to embodiments can add a timed metadata track to a file or a track.

[0974] timed metadata track

[0975] 3D tile timed metadata track

[0976] A dynamic 3D tile timed metadata track can indicate 3D tiles, and tile inventory information can dynamically change over time. The timed metadata track can be linked to a corresponding track carrying an associated G-PCC point cloud bitstream by referencing with a ‘cdsc’ track. By referencing with a ‘cdtg’ track, the timed metadata track can be linked to a corresponding group of tracks carrying an associated G-PCC point cloud bitstream.

[0977] aligned(8) class GPCCTileInventorySampleEntry extends MetadataSampleEntry(‘dyti’) {

[0978] TileInventoryBox();

[0979] }

[0980] A sample entry of the timed metadata track can contain 3D tile information or tile inventory information, which includes default 3D tile information applied to associated point cloud data.

[0981] A sample format of the timed metadata track can be as follows.

[0982] aligned(8) class GPCCSpatialRegionSample {

[0983] TileInventoryStruct();

[0984] }

[0985] TileInventoryStruct can indicate dynamically changed 3D tile or tile inventory information of associated point cloud data.

[0986] A file packaging or file packager (see FIGS. 14-16 ) of a transmitting / receiving apparatus according to embodiments can generate and store a track in a file according to a degree of change of parameter sets existing in a G-PCC bitstream, and store related signaling information (see FIGS. 18-33). The file packaging or file packager can add the signaling information according to the embodiments to one or more tracks in the file when generating the file. For example, it can process a media track including part or all of the G-PCC bitstream, a metadata track related to the G-PCC bitstream, etc.

[0987] The file unpackaging or file unpackager according to the embodiments can acquire information such as signaling and parameter sets included in the tracks in the file, and effectively extract, decode, and post-process the track data in the file accordingly.

[0988] Due to the operation of the file packager, the file unpackager, etc. according to the embodiments, the method / apparatus for transmitting and receiving point cloud data according to the embodiments can provide the following effects.

[0989] The method / apparatus according to the embodiments can effectively play a point cloud video. In addition, it can allow a user to interact with the point cloud video. In addition, it can allow the user to change a play parameter.

[0990] The method / apparatus according to the embodiments can allow a user to select a track or item containing point cloud data in a file according to a user viewport, or partially parse, decode, or render data in the track or item. By reducing unnecessary data, i.e., unnecessary calculation of point cloud data irrelevant to the user viewport, parsing of the point cloud data in the file and decoding / rendering of the point cloud data can be effectively performed.

[0991] The file according to the embodiments can further include one or more tiles for a tile of point cloud data, and the track can include a sample entry including tile quantity information for a number of tiles of point cloud data and tile identification information for the tile.

[0992] The track for the tile and the track for the parameter set can be linked based on the track reference.

[0993] Due to the file structure according to the embodiments, e.g., based on the base track, the tile track, the reference, the method / apparatus for receiving point cloud data can acquire point cloud data desired at the receiving side, and decode and efficiently provide it to the user. The operation and data structure according to the embodiments can enable the receiver to partially access the point cloud data.

[0994] The track according to the embodiments can be a base track, and the base track can include a common parameter set applied to one or more tracks of point cloud data.

[0995] In addition, the file can further include a track for a tile of point cloud data, and the track can be a tile track, and the tile track can carry point cloud data associated with the track.

[0996] FIG. 34 The method of transmitting point cloud data according to an embodiment is exemplified.

[0997] The method of transmitting point cloud data according to an embodiment can include encoding the point cloud data.

[0998] The encoding operation according to an embodiment can include FIG. 1 the operations of the transmitting apparatus 10000 and the encoder 10002, FIG. 2 the encoding 20001, FIG. 4 the encoding of the apparatus, FIG. 12 the transmitting apparatus, FIG. 14 and FIG. 15 the point cloud encoding, FIG. 17 the processing of the apparatus, FIGS. 18-24 the encoding of the bitstream, etc.

[0999] The method of receiving point cloud data according to an embodiment is exemplified.

[1000] The receiving operation according to an embodiment can include FIG. 1 the operations of the receiving apparatus 20000 and the decoder 20001, FIG. 2 the encoding 20001 and the receiving 20002, FIG. 14 and FIG. 15 the file / segment receiving, FIG. 17 the processing of the apparatus, FIGS. 18-24 the receiving of the bitstream such as FIGS. 25-33 a file container into a file container such as

[1001] The method of receiving point cloud data according to an embodiment is exemplified.

[1002] The receiving operation according to an embodiment can include FIG. 1 the operations of the receiving apparatus 20000 and the decoder 20001, FIG. 2 the receiving 20002, FIG. 14 and FIG. 15 the delivery, FIG. 17 the processing of the apparatus, FIGS. 25-33 the receiving of a file container such as

[1003] FIG. 35 The method of receiving point cloud data according to an embodiment is exemplified.

[1004] The method of receiving point cloud data according to an embodiment can include receiving the point cloud data.

[1005] The receiving operation according to an embodiment includes FIG. 1the operation of the receiving device 10004 and the receiver 10005, FIG. 2 the sending 20002, FIG. 13 the receiving device, FIGS. 14-16 the delivery reception, FIG. 17 the processing of the device, such as FIGS. 18-24 the receiving of the bitstream of the bitstream and such as FIGS. 25-33 the file container of the file container.

[1006] S3510: The point cloud data receiving method according to the embodiments can further comprise de-encapsulating the point cloud data.

[1007] The de-encapsulating operation according to the embodiments can comprise FIG. 1 the operation of the receiving device 10004 and the receiver 10005, FIG. 2 the sending 20002 and the decoding 20003, FIGS. 14-16 the file / segment de-encapsulation, FIG. 17 the processing of the device and from FIGS. 25-33 the file de-encapsulation FIGS. 18-24 the bitstream.

[1008] S3520: The point cloud data receiving method according to the embodiments can further comprise decoding the point cloud data.

[1009] The decoding operation according to the embodiments can comprise FIG. 1 the operation of the receiving device 10004 and the decoder 10006, FIG. 2 the decoding 20003, FIG. 10 and FIG. 11 the decoding, FIG. 13 the decoding of the receiving device and FIGS. 14-17 the decoding and decoding the bitstream of FIGS. 18-24 the bitstream.

[1010] The embodiments have been described according to methods and / or devices. The description of the methods and the description of the devices can complement each other.

[1011] Although the embodiments have been described with reference to the respective drawings, new embodiments can be designed by combining the embodiments shown in the drawings. If a computer-readable recording medium in which a program for executing the above-described embodiments in the foregoing description is recorded is designed by one of ordinary skill in the art, it can also fall within the scope of the appended claims and equivalents thereof. The apparatus and method can not be limited by the configuration and method of the above-described embodiments. The above-described embodiments can be configured by completely or partially selectively combining each other to achieve various modifications. Although the preferred embodiments have been described with reference to the drawings, it will be understood by those skilled in the art that various modifications and changes can be made to the embodiments without departing from the spirit or scope of the disclosure described in the appended claims. Such modifications should not be understood from the technical idea or concept of the embodiments alone.

[1012] The various elements of the apparatus of the embodiments can be implemented by hardware, software, firmware, or a combination thereof. The various elements in the embodiments can be implemented by a single chip, for example, a single hardware circuit. According to the embodiments, components according to the embodiments can be implemented as separate chips, respectively. According to the embodiments, at least one or more of the components of the apparatus according to the embodiments can include one or more processors capable of executing one or more programs. The one or more programs can perform any one or more of the operations / methods according to the embodiments, or include instructions for performing the operations / methods. Executable instructions for performing the methods / operations of the apparatus according to the embodiments can be stored in a non-transitory CRM or other computer program product configured to be executed by one or more processors, or can be stored in a transitory CRM or other computer program product configured to be executed by one or more processors. In addition, the memory according to the embodiments can be used as a concept that covers not only a volatile memory such as a RAM but also a non-volatile memory, a flash memory, and a PROM. In addition, it can also be implemented in the form of a carrier wave, for example, transmission over the Internet. In addition, the processor-readable recording medium can be distributed to computer systems connected through a network so that the processor-readable code can be stored and executed in a distributed manner.

[1013] In this document, the terms “ / ” and “,” are to be interpreted as “and / or”. For example, the expression “A / B” can mean “A and / or B”. In addition, “A, B” can mean “A and / or B”. In addition, “A / B / C” can mean “at least one of A, B, and / or C”. “A / B / C” can also mean “at least one of A, B, and / or C”. In addition, in this document, the term “or” is to be interpreted as “and / or”. For example, the expression “A or B” can mean 1) only A, 2) only B, and / or 3) both A and B. In other words, the term “or” in this document is to be interpreted as “additionally or alternatively”.

[1014] The terms such as first and second can be used to describe various elements of the embodiments. However, the various components according to the embodiments should not be limited by the above terms. The terms are used only to distinguish one element from another element. For example, a first user input signal can be referred to as a second user input signal. Similarly, a second user input signal can be referred to as a first user input signal. The use of these terms should be interpreted not to depart from the scope of the various embodiments. The first user input signal and the second user input signal are both user input signals, but do not mean the same user input signal unless the context clearly dictates otherwise.

[1015] The terms used to describe the embodiments are only for the purpose of describing particular embodiments and are not intended to limit the embodiments. As used in the description of the embodiments and the claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. The expression "and / or" is used to include all possible combinations of the terms. The terms such as "include" or "have" are intended to mean that there is existence of the appended figures, numbers, steps, elements, and / or components, and it should be understood that the possibility of existence of additional existence is not excluded. As used herein, conditional expressions such as "if" and "when" are not limited to optional cases, and are intended to be interpreted as performing a related operation when a certain condition is met or interpreting a related definition according to a certain condition.

[1016] Operations according to the embodiments described in the present specification can be performed by a transmitting / receiving device according to the embodiments including a memory and / or a processor. The memory can store a program for processing / controlling operations according to the embodiments, and the processor can control various operations described in the present specification. The processor can be referred to as a controller or the like. In the embodiments, operations can be performed by firmware, software, and / or a combination thereof. The firmware, software, and / or a combination thereof can be stored in the processor or the memory.

[1017] Disclosed mode

[1018] As described above, the relevant content has been described in the best mode of implementing the embodiments.

[1019] Industrial applicability

[1020] As described above, the embodiments can be applied to a point cloud data transmitting / receiving device and system in whole or in part.

[1021] It will be apparent to those skilled in the art that various modifications and changes can be made to the embodiments within the scope of the embodiments.

[1022] Therefore, these embodiments are intended to cover modifications and variations of the present disclosure provided they come within the scope of the appended claims and their equivalents.

Claims

1. A method for transmitting point cloud data, the method comprising the following steps: The geometric data of the point cloud data is encoded based on an octree; The attribute data of the point cloud data is encoded based on the Level of Detail (LoD). The point cloud data is encapsulated in a file, wherein the component tracks of the file respectively include the geometric data and the attribute data; and Send the file, The file also includes orbits for the parameter set of the point cloud data. The parameter set includes a sequence parameter set, a geometric parameter set, an attribute parameter set, and a tile parameter set. The file also includes one or more tracks for the tiles, which are represented as cuboids within a 3D bounding box for the point cloud data. Specifically, each of the one or more tracks for the patch includes a sample entry, the sample entry including patch quantity information for the number of patches of the point cloud data in the track, patch identifier information for identifying each patch in the track, and component type information including point cloud type information for the components of the point cloud data and index information for the attribute data. The file also includes a timing metadata track, which is used to signal dynamic changes in position information, including the x, y, and z coordinates of the capture device's position.

2. The method according to claim 1, in, The tracks for the aforementioned pieces and the tracks for the parameter sets are based on track reference links.

3. A device for transmitting point cloud data, the device comprising: The encoder is configured to encode the geometric data of the point cloud data based on an octree and to encode the attribute data of the point cloud data based on the level of detail (LoD). An encapsulator configured to encapsulate the point cloud data in a file, wherein the component tracks of the file respectively include the geometric data and the attribute data; and A transmitter configured to transmit the point cloud data. The file also includes orbits for the parameter set of the point cloud data. The parameter set includes a sequence parameter set, a geometric parameter set, an attribute parameter set, and a tile parameter set. The file also includes one or more tracks for the tiles, which are represented as cuboids within a 3D bounding box for the point cloud data. Specifically, each of the one or more tracks for the patch includes a sample entry, the sample entry including patch quantity information for the number of patches of the point cloud data in the track, patch identifier information for identifying each patch in the track, and component type information including point cloud type information for the components of the point cloud data and index information for the attribute data. The file also includes a timing metadata track, which is used to signal dynamic changes in position information, including the x, y, and z coordinates of the capture device's position.

4. The device according to claim 3, in, The tracks for the aforementioned pieces and the tracks for the parameter sets are based on track reference links.

5. A method for receiving point cloud data, the method comprising the following steps: Receive files containing point cloud data; The file is decapsulated, wherein the component tracks of the file respectively include the geometric data of the point cloud data and the attribute data of the point cloud data; Decoding the geometric data of the point cloud data based on an octree; and The attribute data of the point cloud data is decoded based on the level of detail (LoD). The file also includes orbits for the parameter set of the point cloud data. The parameter set includes a sequence parameter set, a geometric parameter set, an attribute parameter set, and a tile parameter set. The file also includes one or more tracks for the tiles, which are represented as cuboids within a 3D bounding box for the point cloud data. Specifically, each of the one or more tracks for the patch includes a sample entry, the sample entry including patch quantity information for the number of patches of the point cloud data in the track, patch identifier information for identifying each patch in the track, and component type information including point cloud type information for the components of the point cloud data and index information for the attribute data. The file also includes a timing metadata track, which is used to signal dynamic changes in position information, including the x, y, and z coordinates of the capture device's position.

6. The method according to claim 5, in, The tracks for the aforementioned pieces and the tracks for the parameter sets are based on track reference links.

7. A device for receiving point cloud data, the device comprising: processor; Memory, which is connected to the processor; The processor is configured as follows: Receive files containing point cloud data; The file is decapsulated, wherein the component tracks of the file respectively include the geometric data of the point cloud data and the attribute data of the point cloud data; Decoding the geometric data of the point cloud data based on an octree; and The attribute data of the point cloud data is decoded based on the level of detail (LoD). The file also includes orbits for the parameter set of the point cloud data. The parameter set includes a sequence parameter set, a geometric parameter set, an attribute parameter set, and a tile parameter set. The file also includes one or more tracks for the tiles, which represent cuboids within a 3D bounding box of the point cloud data. Specifically, each of the one or more tracks for the patch includes a sample entry, the sample entry including patch quantity information for the number of patches of the point cloud data in the track, patch identifier information for identifying each patch in the track, and component type information including point cloud type information for the components of the point cloud data and index information for the attribute data. The file also includes a timing metadata track, which is used to signal dynamic changes in position information, including the x, y, and z coordinates of the capture device's position.

8. The device according to claim 7, in, The tracks for the aforementioned pieces and the tracks for the parameter sets are based on track reference links.

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