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

Abstract

A decoding method and apparatus for point cloud data are disclosed. The decoding method according to embodiments can include the steps of receiving geometry data, attribute data, and signaling information; decoding the geometry data based on the signaling information; and decoding the attribute data based on the signaling information and the decoded geometry data, wherein the step of decoding the geometry data can include the steps of generating a geometry tree consisting of a plurality of depth groups; and decoding the geometry data based on the geometry tree.

Description

Technical Field

[0001] The embodiments relate to a method and apparatus for processing point cloud content. Background Technology

[0002] Point cloud content is content represented by point clouds, which are collections of points belonging to a coordinate system representing three-dimensional space (or volume). Point cloud content can represent media configured in three dimensions and is used to provide various services such as Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), XR (Extended Reality), and autonomous driving services. However, tens of thousands to hundreds of thousands of point data points are required to represent point cloud content. Therefore, a method for efficiently processing large amounts of point data is needed.

[0003] In other words, high throughput is required to send and receive point cloud data. Therefore, the computational operations are complex and time-consuming due to the large volume of point cloud data during the process of sending and receiving point cloud data (in which encoding for compression and decoding for decompression are performed). Summary of the Invention

[0004] Technical issues

[0005] The purpose of this disclosure, designed to solve the above-mentioned problems, is to provide an apparatus and method for efficiently transmitting / receiving point clouds.

[0006] The purpose of this implementation is to provide an apparatus and method for addressing latency and encoding / decoding complexity.

[0007] The purpose of the implementation may be to provide an apparatus and method for efficiently performing geometric encoding / decoding by using a region-adaptive planar mode.

[0008] However, the scope of the implementation is not limited to the technical objectives described above, and can be extended to other objectives that can be inferred by those skilled in the art based on the entire disclosure.

[0009] Technical solution

[0010] To achieve the above objectives and other advantages, the decoding method according to the embodiments may include receiving geometric data, attribute data, and signaling information, decoding the geometric data, and decoding the attribute data.

[0011] According to an implementation, decoding geometric data may include generating a geometric tree with multiple depths, and decoding the geometric data based on the geometric tree, wherein the decoding may include determining whether each depth of the geometric tree has planar eligibility, and decoding the geometric data at the depths determined to have planar eligibility based on a planar pattern.

[0012] According to the implementation method, whether each depth has planar compliance can be determined based on the density of points at the corresponding depth.

[0013] According to the implementation, the density of points can be determined based on at least one of the following: the number of points encoded in a data unit that includes geometric data of the depth, the number of points encoded in a direct node, and / or the number of points present at the depth.

[0014] According to the implementation method, the density of points can be determined based on information about the qualification of a specified plane included in the signaling information.

[0015] According to the implementation, decoding may include decoding geometric data on a subgroup basis, and determining whether each depth has planar compliance on a subgroup basis, and each subgroup may be identified by a subgroup index.

[0016] According to an embodiment, a decoding device may include a memory; and at least one processor connected to the memory, wherein the at least one processor is configured to receive geometric data, attribute data, and signaling information, decode the geometric data, and decode the attribute data.

[0017] According to an implementation, at least one processor may include a geometry decoder configured to decode geometric data, and the geometry decoder may be configured to generate a geometry tree including multiple depths and decode the geometric data based on the geometry tree.

[0018] According to the implementation, the geometry decoder can be configured to determine whether each depth of the geometry tree is planar qualified, and to decode the geometry data at the depths determined to be planar qualified based on the planar pattern.

[0019] According to the implementation method, whether each depth has planar compliance can be determined based on the density of points at the corresponding depth.

[0020] According to the implementation, the density of points can be determined based on at least one of the following: the number of points encoded in a data unit that includes geometric data of the depth, the number of points encoded in a direct node, and / or the number of points present at the depth.

[0021] According to the implementation method, the density of points can be determined based on information about the qualification of a specified plane included in the signaling information.

[0022] According to the implementation, the geometry decoder can be configured to decode geometric data on a subgroup basis, and to determine whether each depth has planar compliance on a subgroup basis, and each subgroup can be identified by a subgroup index.

[0023] According to an implementation, an encoding method may include encoding geometric data, encoding attribute data, and transmitting the encoded geometric data, the encoded attribute data, and signaling information.

[0024] According to an implementation, encoding geometric data may include generating a geometric tree comprising multiple depths; and encoding geometric data based on the geometric tree, wherein encoding may include determining whether each depth of the geometric tree is planar qualified, and encoding the geometric data at the depths determined to be planar qualified based on a planar pattern.

[0025] According to the implementation method, whether each depth has planar compliance can be determined based on the density of points at the corresponding depth.

[0026] Beneficial effects

[0027] The apparatus and method according to the embodiments can provide high-quality point cloud services.

[0028] The apparatus and method according to the embodiments can support various video codec schemes.

[0029] The apparatus and method according to the embodiments can provide general point cloud content, such as autonomous driving services.

[0030] The apparatus and method according to the embodiments can provide improved parallel processing and scalability by performing spatial adaptive partitioning on point cloud data for independent encoding and decoding of point cloud data.

[0031] The apparatus and method according to the embodiments can improve the encoding and decoding performance of point cloud data by dividing point cloud data into tile and / or slice units to perform encoding and decoding and signaling the required data.

[0032] The apparatus and method according to the embodiments can divide point cloud data into compressed data according to predetermined criteria and transmit the divided compressed data. When using layered encoding, compressed data can be divided and transmitted according to layers. Therefore, the storage and transmission efficiency of the transmitting apparatus can be increased. Attached Figure Description

[0033] The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated into and constitute a part of this application, illustrating embodiments of the present disclosure and serving, together with the description, to explain the principles of the present disclosure.

[0034] Figure 1 An example of a point cloud content provision system according to an implementation method is shown.

[0035] Figure 2 This is a block diagram illustrating the operation of providing point cloud content according to an implementation method.

[0036] Figure 3 An example of a point cloud encoder according to an implementation method is shown.

[0037] Figure 4 An example of an octree and occupancy code according to an implementation method is shown.

[0038] Figure 5 An example of point configuration for each LOD according to an implementation method is shown.

[0039] Figure 6 Another example of point configuration for each LOD according to the implementation method is shown.

[0040] Figure 7 An example of a point cloud decoder according to an implementation method is shown.

[0041] Figure 8 An example of a transmitting device according to an embodiment is shown.

[0042] Figure 9 An example of a receiving device according to an embodiment is shown.

[0043] Figure 10 Examples of structures that can interoperate with point cloud data transmission / reception methods / apparatus according to embodiments are illustrated.

[0044] Figure 11 and Figure 12 This is a diagram illustrating the process of encoding, transmitting, and decoding point cloud data according to an implementation method.

[0045] Figure 13 This is a diagram illustrating an example of layer-based point cloud data configuration according to an implementation method.

[0046] Figure 14 (a) illustrates the bitstream structure of geometric data according to the embodiment, and Figure 14 (b) illustrates the bitstream structure of attribute data according to the implementation method.

[0047] Figure 15 This is a diagram illustrating an example of a bitstream configuration for delivering bitstreams on a layer-by-layer basis according to an implementation.

[0048] Figure 16 An example of a bitstream alignment method used according to an implementation is shown when a geometric bitstream and an attribute bitstream are multiplexed into a single bitstream.

[0049] Figure 17 Another example of a bitstream alignment method used according to an implementation is illustrated when a geometric bitstream and an attribute bitstream are multiplexed into a single bitstream.

[0050] Figure 18 (a) to Figure 18 (c) is a diagram illustrating an example of symmetric geometry-attribute selection according to the implementation method.

[0051] Figure 19 (a) to Figure 19 (c) is a diagram illustrating an example of asymmetric geometry-attribute selection according to an implementation method.

[0052] Figure 20 (a) to Figure 20 (c) illustrates an example of a method for configuring slices containing point cloud data according to an implementation.

[0053] Figure 21 (a) and Figure 21 (b) illustrates the geometric coding layer structure according to the implementation method.

[0054] Figure 22 The layer and subgroup structure according to the implementation method is illustrated.

[0055] Figure 23 (a) to Figure 23 (c) illustrates a representation of layer-based point cloud data according to an implementation method.

[0056] Figure 24 An example of a point cloud data transmission / reception apparatus / method according to an embodiment is shown.

[0057] Figure 25 This is a flowchart illustrating an example of an encoding method of an encoder according to an implementation method.

[0058] Figure 26 This is a flowchart illustrating an example of a decoding method of a decoder according to an embodiment.

[0059] Figure 27 This is a diagram illustrating another example of a point cloud transmitting apparatus according to an embodiment.

[0060] Figure 28 This is a diagram illustrating another example of a point cloud receiving device according to an embodiment.

[0061] Figure 29 A bitstream configuration according to an implementation method is illustrated.

[0062] Figure 30a and Figure 30b An example of the syntax structure of the sequence parameter set according to the implementation method is shown.

[0063] Figure 31 An example of the syntax structure of the relevant geometric data unit header according to the implementation method is shown.

[0064] Figure 32 An example of the syntax structure of a list of layer group structures according to an implementation method is shown.

[0065] Figure 33 Another example of the syntax structure of the header of the relevant geometric data unit according to the implementation method is shown.

[0066] Figure 34 (a) and Figure 34 (b) illustrates an example of the relationship between layer groups, subgroups, and fine-grained slices (FGS) according to an implementation.

[0067] Figure 35a and Figure 35b Another example of the syntax structure of the Sequence Parameter Set (SPS) according to the implementation is illustrated.

[0068] Figure 36 An example of the syntax structure of the geometric data unit header according to the implementation method is shown.

[0069] Figure 37 Another example of the syntax structure of the header of the relevant geometric data unit according to the implementation method is shown.

[0070] Figure 38 Examples of compression and provision of geometry and attributes of point cloud data according to an implementation method are illustrated.

[0071] Figure 39 Another example of the compression and provision of geometry and attributes of point cloud data according to the implementation method is illustrated.

[0072] Figure 40 Another example of the compression and provision of geometry and attributes of point cloud data according to the implementation method is illustrated.

[0073] Figure 41 A flowchart of a point cloud data transmission method according to an embodiment is shown.

[0074] Figure 42 A flowchart of a point cloud data receiving method according to an embodiment is shown. Detailed Implementation

[0075] Preferred embodiments of the present disclosure will now be described in detail, examples of which are illustrated in the accompanying drawings. The detailed description given below with reference to the drawings is intended to illustrate exemplary embodiments of the present disclosure, and not to show only embodiments that can be implemented according to the present disclosure. The following detailed description includes specific details in order to provide a thorough understanding of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure can be practiced without these specific details.

[0076] Although most of the terms used in this disclosure are selected from commonly used terms in the art, some terms have been arbitrarily chosen by the applicant and their meanings are explained in detail in the following description as needed. Therefore, this disclosure should be understood based on the intended meaning of the terms rather than their simple names or meanings.

[0077] Figure 1 An exemplary point cloud content delivery system according to an implementation is shown.

[0078] Figure 1 The point cloud content providing system shown may include a transmitting device 10000 and a receiving device 10004. The transmitting device 10000 and the receiving device 10004 are capable of transmitting and receiving point cloud data via wired or wireless communication.

[0079] The point cloud data transmission device 10000 according to an embodiment can secure and process point cloud video (or point cloud content) and transmit the point cloud video. According to an embodiment, the transmission device 10000 may include a fixed station, a base transceiver system (BTS), a network, an artificial intelligence (AI) device and / or system, a robot, an AR / VR / XR device, and / or a server. According to an embodiment, the transmission device 10000 may include devices configured to communicate with base stations and / or other wireless devices using radio access technologies (e.g., 5G New RAT (NR), Long Term Evolution (LTE)), robots, vehicles, AR / VR / XR devices, portable devices, home appliances, Internet of Things (IoT) devices, and AI devices / servers.

[0080] The transmitting device 10000 according to the embodiment includes a point cloud video acquisition unit 10001, a point cloud video encoder 10002 and / or a transmitter (or communication module) 10003.

[0081] The point cloud video acquisition unit 10001 according to an embodiment acquires point cloud video through processing procedures such as capture, synthesis, or generation. Point cloud video is point cloud content represented by a point cloud, which is a set of points located in 3D space, and may be referred to as point cloud video data, point cloud data, etc. The point cloud video according to an embodiment may include one or more frames. A frame represents a still image / scene. Therefore, point cloud video may include point cloud images / frames / scenes, and may be referred to as point cloud images, frames, or scenes.

[0082] The point cloud video encoder 10002 according to an embodiment 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 coding. The point cloud compression coding according to an embodiment may include geometry-based point cloud compression (G-PCC) coding and / or video-based point cloud compression (V-PCC) coding or next-generation coding. The point cloud compression coding according to an embodiment is not limited to the above embodiments. The point cloud video encoder 10002 can output a bitstream containing the encoded point cloud video data. The bitstream may contain not only the encoded point cloud video data but also signaling information related to the encoding of the point cloud video data.

[0083] According to an embodiment, transmitter 10003 transmits a bitstream containing encoded point cloud video data. The bitstream, according to an embodiment, is encapsulated in a file or segment (e.g., a streaming segment) and transmitted via various networks such as broadcast networks and / or broadband networks. Although not shown in the figures, transmitting device 10000 may include an encapsulator (or encapsulation module) configured to perform encapsulation operations. According to an embodiment, the encapsulator may be included in transmitter 10003. According to an embodiment, the file or segment may be transmitted via a network to receiving device 10004 or stored in a digital storage medium (e.g., USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.). Transmitter 10003 according to an embodiment is capable of wired / wireless communication with receiving device 10004 (or receiver 10005) via networks such as 4G, 5G, and 6G. Additionally, the transmitter may perform necessary data processing operations depending on the network system (e.g., a 4G, 5G, or 6G communication network system). Transmitting device 10000 can transmit encapsulated data on demand.

[0084] The receiving device 10004 according to an embodiment includes a receiver 10005, a point cloud video decoder 10006, and / or a renderer 10007. According to an embodiment, the receiving device 10004 may include devices, robots, vehicles, AR / VR / XR devices, portable devices, home appliances, Internet of Things (IoT) devices, and AI devices / servers configured to communicate with base stations and / or other wireless devices using radio access technologies (e.g., 5G New RAT (NR), Long Term Evolution (LTE)).

[0085] According to an embodiment, receiver 10005 receives a bitstream containing point cloud video data or a file / segment encapsulated with a bitstream from a network or storage medium. Receiver 10005 can perform necessary data processing according to the network system (e.g., a communication network system such as 4G, 5G, 6G, etc.). According to an embodiment, receiver 10005 can decapsulate the received file / segment and output a bitstream. According to an embodiment, receiver 10005 may include a decapsulator (or decapsulator module) configured to perform a decapsulation operation. The decapsulator may be implemented as a separate element (or component) from receiver 10005.

[0086] The point cloud video decoder 10006 decodes the bitstream containing point cloud video data. The point cloud video decoder 10006 can decode the point cloud video data according to the method in which the point cloud video data is encoded (e.g., the reverse processing of the operation of the point cloud video encoder 10002). Therefore, the point cloud video decoder 10006 can decode the point cloud video data by performing point cloud decompression encoding (the reverse processing of point cloud compression). Point cloud decompression encoding includes G-PCC encoding.

[0087] Renderer 10007 renders decoded point cloud video data. In one embodiment, renderer 10007 can render decoded point cloud video data according to a viewport, etc. Renderer 10007 can render not only point cloud video data but also audio data to output point cloud content. According to an embodiment, renderer 10007 may include a display configured to display point cloud content. According to an embodiment, the display may be implemented as a separate device or component rather than included in renderer 10007.

[0088] The arrows indicated by dashed lines in the diagram represent the transmission paths of the feedback information acquired by the receiving device 10004. The feedback information reflects the interactivity of the 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 for a service requiring user interaction (e.g., autonomous driving services, etc.), the feedback information can be provided to the content sender (e.g., the sending device 10000) and / or the service provider. Depending on the implementation, the feedback information may be used in both the receiving device 10004 and the sending device 10000, or it may not be provided.

[0089] According to the embodiment, head orientation information can represent information about the position, orientation, angle, and movement of the user's head. The receiving device 10004 according to the embodiment can calculate viewport information based on the head orientation information. Viewport information is information about the area of ​​the point cloud video that the user is viewing (that is, the area the user is currently viewing). In other words, viewport information is information about the area the user is currently viewing in the point cloud video. In other words, the viewport or viewport area can represent the area the user is viewing in the point cloud video. The viewpoint is the point the user is viewing in the point cloud video and can represent the center point of the viewport area. That is, the viewport is the area centered on the viewpoint, and the size and shape of the area can be determined by the field of view (FOV). Therefore, the receiving device 10004 can extract viewport information based on the vertical or horizontal FOV supported by the device and the head orientation information. Furthermore, the receiving device 10004 can perform gaze analysis, etc., based on the head orientation information and / or viewport information to determine how the user consumes the point cloud video, the area the user is gazing at in the point cloud video, and the gaze duration. According to one embodiment, the receiving device 10004 can send feedback information, including gaze analysis results, to the transmitting device 10000. According to another embodiment, a device such as a VR / XR / AR / MR display can extract the viewport region based on the user's head position / orientation and a vertical or horizontal field of view (FOV) supported by the device. According to another embodiment, head orientation information and viewport information can be referred to as feedback information, signaling information, or metadata.

[0090] According to the embodiments, feedback information can be obtained during rendering and / or display. The feedback information can be ensured by one or more sensors included in the receiving device 10004. According to the embodiments, the feedback information can be ensured by the renderer 10007 or by separate external components (or devices, components, etc.). Figure 1 The dashed lines in the diagram represent the process of sending feedback information ensured by renderer 10007. This feedback information can be sent not only to the sending side but also consumed at the receiving side. In other words, the point cloud content providing system can process (encode / decode / render) point cloud data based on the feedback information. For example, point cloud video decoder 10006 and renderer 10007 can prioritize decoding and rendering point cloud video only for the area currently being viewed by the user, based on the feedback information (i.e., head orientation information and / or viewport information).

[0091] Furthermore, the receiving device 10004 can send feedback information to the transmitting device 10000. The transmitting device 10000 (or the point cloud video data encoder 10002) can perform encoding operations based on the feedback information. Therefore, the point cloud content providing system can efficiently process necessary data (e.g., point cloud data corresponding to the user's head position) based on the feedback information instead of processing (encoding / decoding) the entire point cloud data, and provide the point cloud content to the user.

[0092] According to the implementation method, the transmitting device 10000 can be called an encoder, transmitting device, transmitter, transmitting system, etc., and the receiving device 10004 can be called a decoder, receiving device, receiver, receiving system, etc.

[0093] According to the implementation method Figure 1 Point cloud data processed in a point cloud content provision system (through a series of processes including acquisition, encoding, transmission, decoding, and rendering) can be referred to as point cloud content data or point cloud video data. Depending on the implementation, point cloud content data can be used as a concept encompassing metadata or signaling information related to point cloud data.

[0094] Figure 1 The components of the point cloud content providing system shown can be implemented by hardware, software, processors, and / or combinations thereof.

[0095] Figure 2 This is a block diagram illustrating the operation of providing point cloud content according to an embodiment.

[0096] Figure 2 The block diagram shows Figure 1 The operation of the point cloud content providing system described herein. As mentioned above, the point cloud content providing system can process point cloud data based on point cloud compression encoding (e.g., G-PCC).

[0097] A point cloud content providing system according to an embodiment (e.g., point cloud sending device 10000 or point cloud video acquisition unit 10001) can acquire point cloud video (20000). The point cloud video is represented by a point cloud belonging to a coordinate system used to represent 3D space. The point cloud video according to an embodiment may include Ply (Polygon file format or Stanford Triangle format) files. When the point cloud video has one or more frames, the acquired point cloud video may include one or more Ply files. The Ply file contains point cloud data such as point geometry and / or attributes. Geometry includes the position of the points. The position of each point can be represented by parameters (e.g., values ​​of the X, Y, and Z axes) representing a three-dimensional coordinate system (e.g., a coordinate system consisting of X, Y, and Z axes). Attributes include the attributes of the points (e.g., information about the texture, color (YCbCr or RGB), reflectivity r, transparency, etc., of each point). A point has one or more attributes. For example, a point may have a color attribute or two attributes: color and reflectivity. According to the implementation, geometry can be referred to as location, geometric information, geometric data, location information, location data, etc., and attributes can be referred to as attributes, attribute information, attribute data, etc. The point cloud content providing system (e.g., point cloud sending device 10000 or point cloud video acquisition unit 10001) can secure point cloud data from information related to the point cloud video acquisition process (e.g., depth information, color information, etc.).

[0098] A point cloud content providing system (e.g., a transmitting device 10000 or a point cloud video encoder 10002) according to an embodiment can encode point cloud data (20001). The point cloud content providing system can encode point cloud data based on point cloud compression encoding. As described above, point cloud data can include geometric information and attribute information about points. Therefore, the point cloud content providing system can perform geometric encoding to encode geometry and output a geometric bitstream. The point cloud content providing system can perform attribute encoding to encode attributes and output an attribute bitstream. According to an embodiment, the point cloud content providing system can perform attribute encoding based on geometric encoding. The geometric bitstream and attribute bitstream according to an embodiment can be multiplexed and output as a single bitstream. The bitstream according to an embodiment may also contain signaling information related to geometric encoding and attribute encoding.

[0099] A point cloud content providing system according to an embodiment (e.g., transmitting device 10000 or transmitter 10003) can transmit encoded point cloud data (20002). Figure 1As shown, encoded point cloud data can be represented by geometric bitstreams and attribute bitstreams. Additionally, the encoded point cloud data can be transmitted as a bitstream along with signaling information related to the encoding of the point cloud data (e.g., signaling information related to geometric encoding and attribute encoding). The point cloud content providing system can encapsulate the bitstream carrying the encoded point cloud data and transmit it as a file or fragment.

[0100] The point cloud content providing system according to the embodiment (e.g., receiving device 10004 or receiver 10005) can receive a bitstream containing encoded point cloud data. Additionally, the point cloud content providing system (e.g., receiving device 10004 or receiver 10005) can demultiplex the bitstream.

[0101] A point cloud content providing system (e.g., receiving device 10004 or point cloud video decoder 10005) can decode encoded point cloud data (e.g., geometric bitstream, attribute bitstream) transmitted in a bitstream. The point cloud content providing system (e.g., receiving device 10004 or point cloud video decoder 10005) can decode point cloud video data based on signaling information related to the encoding of the point cloud video data contained in the bitstream. The point cloud content providing system (e.g., receiving device 10004 or point cloud video decoder 10005) can decode the geometric bitstream to reconstruct the location (geometry) of the points. The point cloud content providing system can reconstruct the attributes of the points by decoding the attribute bitstream based on the reconstructed geometry. The point cloud content providing system (e.g., receiving device 10004 or point cloud video decoder 10005) can reconstruct point cloud video based on location according to the reconstructed geometry and the decoded attributes.

[0102] A point cloud content providing system (e.g., receiving device 10004 or renderer 10007) according to an embodiment can render decoded point cloud data (20004). The point cloud content providing system (e.g., receiving device 10004 or renderer 10007) can use various rendering methods to render the geometry and attributes decoded through the decoding process. Points in the point cloud content can be rendered as vertices with a specific thickness, cubes with a specific minimum size centered at the corresponding vertex position, or circles centered at the corresponding vertex position. All or part of the rendered point cloud content is provided to the user through a display (e.g., a VR / AR display, a general display, etc.).

[0103] The point cloud content providing system according to the embodiment (e.g., receiving device 10004) can ensure 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 operation / reference of the point cloud content providing system according to the embodiment... Figure 1 The feedback information and operation described are the same, so their detailed description is omitted.

[0104] Figure 3 An exemplary point cloud encoder according to an implementation is shown.

[0105] Figure 3 Show Figure 1 An example of a point cloud video encoder 10002. The point cloud encoder reconstructs and encodes point cloud data (e.g., point locations and / or attributes) to adjust the quality of the point cloud content (e.g., lossless, lossy, or near-lossless) based on network conditions or applications. When the total size of the point cloud content is large (e.g., providing 60 Gbps of point cloud content for 30 fps), the point cloud content providing system may not be able to stream the content in real time. Therefore, the point cloud content providing system can reconstruct the point cloud content based on a maximum target bitrate to provide the point cloud content according to network conditions, etc.

[0106] For reference Figure 1 and Figure 2 As described, the point cloud encoder can perform both geometric encoding and attribute encoding. Geometric encoding is performed before attribute encoding.

[0107] The point cloud video encoder according to the implementation includes a coordinate transformer (transform coordinates) 30000, a quantizer (quantizes and removes points (voxarization)) 30001, an octree analyzer (analyzes octrees) 30002, a surface approximation analyzer (analyzes surface approximations) 30003, an arithmetic encoder (arithmetic encoding) 30004, a geometry reconstructor (reconstructs geometry) 30005, a color transformer (transforms colors) 30006, an attribute transformer (transforms attributes) 30007, a RAHT transformer 30008, a LOD generator (generates LODs) 30009, a lift transformer (lifts) 30010, a coefficient quantizer (quantizes coefficients) 30011, and / or an arithmetic encoder (arithmetic encoding) 30012. Figure 3 In the point cloud encoder, coordinate transformer 30000, quantizer 30001, octree analyzer 30002, surface approximation analyzer 30003, arithmetic encoder 30004, and geometric reconstructor 30005 can be grouped together and called the geometric encoder. Color transformer 30006, attribute transformer 30007, RAHT transformer 30008, LOD generator 30009, lift transformer 30010, coefficient quantizer 30011, and / or arithmetic encoder 30012 can be grouped together and called the attribute encoder.

[0108] Coordinate transformer 30000, quantizer 30001, octree analyzer 30002, surface approximation analyzer 30003, arithmetic encoder 30004, and geometric reconstructor 30005 can perform geometric coding. Geometric coding according to the implementation may include octree geometric coding, prediction tree geometric coding, direct coding, triplet geometric coding, and entropy coding. Direct coding and triplet geometric coding are applied selectively or in combination. Geometric coding is not limited to the examples described above.

[0109] As shown in the figure, the coordinate transformer 30000 according to the embodiment receives the position and transforms it into coordinates. For example, the position can be transformed into position information in three-dimensional space (e.g., three-dimensional space represented by the XYZ coordinate system). The position information in three-dimensional space according to the embodiment can be referred to as geometric information.

[0110] The quantizer 30001 according to the embodiment quantizes geometry. For example, the quantizer 30001 may quantize points based on the minimum position value of all points (e.g., the minimum value on each of the X, Y, and Z axes). The quantizer 30001 performs a quantization operation: multiplying the difference between the minimum position value and the position value of each point by a preset quantization scaling value, and then finding the nearest integer value by rounding the value obtained by the multiplication. Thus, one or more points may have the same quantized position (or position value). The quantizer 30001 according to the embodiment performs voxelization based on the quantized position to reconstruct the quantized points. As in the case of pixels (the smallest unit containing 2D image / video information), points in the point cloud content (or 3D point cloud video) according to the embodiment may be included in one or more voxels. As a combination of volume and pixel, the term voxel refers to the 3D cubic space generated when 3D space is divided into units (unit = 1.0) based on axes representing 3D space (e.g., X-axis, Y-axis, and Z-axis). Quantizer 30001 can match a group of points in 3D space to voxels. According to one embodiment, a voxel may include only one point. According to another embodiment, a voxel may include one or more points. To represent a voxel as a point, the location of the voxel's center can be set based on the locations of one or more points included in the voxel. In this case, attributes of all locations included in a voxel can be combined and assigned to the voxel.

[0111] The octree analyzer 30002 according to the implementation performs octree geometric encoding (or octree coding) to represent voxels in an octree structure. The octree structure represents points based on the matching of octree structures with voxels.

[0112] The surface approximation analyzer 30003 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 containing multiple points to efficiently provide an octree and voxelization.

[0113] The arithmetic encoder 30004 according to the embodiment performs entropy encoding on octrees and / or approximate octrees. For example, the encoding scheme includes arithmetic encoding. As a result of the encoding, a geometric bitstream is generated.

[0114] The attribute encoding is performed by a color transformer 30006, an attribute transformer 30007, a RAHT transformer 30008, a LOD generator 30009, a boosting transformer 30010, a coefficient quantizer 30011, and / or an arithmetic encoder 30012. As described above, a point can have one or more attributes. The attribute encoding according to the embodiment is also applied to the attributes that a point has. However, when an attribute (e.g., color) includes one or more elements, attribute encoding is applied independently to each element. The attribute encoding according to the embodiment includes color transformation encoding, attribute transformation encoding, region adaptive hierarchical transformation (RAHT) encoding, interpolation-based hierarchical nearest neighbor prediction (prediction transformation) encoding, and interpolation-based hierarchical nearest neighbor prediction (boosting transformation) encoding with update / boosting steps. Depending on the point cloud content, the above-described RAHT encoding, prediction transformation encoding, and boosting transformation encoding can be used selectively, or a combination of one or more encoding schemes can be used. The attribute encoding according to the embodiment is not limited to the examples described above.

[0115] The color converter 30006 according to the embodiment performs color transformation encoding that transforms the color values ​​(or textures) included in the attributes. For example, the color converter 30006 can transform the format of color information (e.g., from RGB to YCbCr). Optionally, the operation of the color converter 30006 according to the embodiment can be applied based on the color values ​​included in the attributes.

[0116] The geometry reconstructor 30005, according to the implementation method, reconstructs (decompresses) octrees and / or approximate octrees. The geometry reconstructor 30005 reconstructs the octree / voxel based on the results of analyzing the point distribution. The reconstructed octree / voxel can be referred to as the reconstructed geometry (restored geometry).

[0117] The attribute transformer 30007 according to the embodiment performs attribute transformation to transform attributes based on reconstructed geometry and / or positions without performing geometric encoding. As described above, since attributes depend on geometry, the attribute transformer 30007 can transform attributes based on reconstructed geometric information. For example, based on the position value of a point included in a voxel, the attribute transformer 30007 can transform the attribute of the point at that 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 30007 transforms the attribute of one or more points. When performing triadic geometric encoding, the attribute transformer 30007 can transform attributes based on the triadic geometric encoding.

[0118] The attribute transformer 30007 performs attribute transformation by calculating the average of the attributes or attribute values ​​(e.g., color or reflectivity of each point) of neighboring points within a specific location / radius from the center (or position value) of each voxel. The attribute transformer 30007 can apply weights based on the distance from the center to each point when calculating the average. Therefore, each voxel has a position and a calculated attribute (or attribute value).

[0119] The attribute transformer 30007 can search for nearest neighbors within a specific location / radius of the center of each voxel based on a KD-tree or Morton code. A KD-tree is a binary search tree and supports a data structure that allows points to be managed based on location, enabling fast nearest neighbor search (NNS). Morton codes are generated by representing the coordinates (e.g., (x, y, z)) of the 3D location of all points as bit values ​​and mixing the bits. For example, when the coordinates representing the point location are (5, 9, 1), the bit values ​​are (0101, 1001, 0001). Mixing the bit values ​​according to the bit index in the order of z, y, and x produces 010001000111. This value is represented as the decimal number 1095. That is, the Morton code value for the point with coordinates (5, 9, 1) is 1095. The attribute transformer 30007 can sort the points based on their Morton code values ​​and perform NNS using a depth-first traversal process. After an attribute transformation operation, use a KD tree or Morton code when an NNS is needed in another transformation process used for attribute encoding.

[0120] As shown in the figure, the transformation properties are input to the RAHT transformer 30008 and / or the LOD generator 30009.

[0121] According to the implementation, the RAHT transformer 30008 performs RAHT encoding for predicting attribute information based on the reconstructed geometric information. For example, the RAHT transformer 30008 can predict the attribute information of higher-level nodes in an octree based on the attribute information associated with lower-level nodes in the octree.

[0122] The LOD generator 30009 according to the embodiment generates a Level of Detail (LOD) to perform predictive transform coding. The LOD according to the embodiment represents the level of detail of the point cloud content. As the LOD value decreases, it indicates a deterioration in the detail of the point cloud content. As the LOD value increases, it indicates an enhancement in the detail of the point cloud content. Points can be classified according to LOD.

[0123] The lift transformer 30010 according to the embodiment performs lift transform coding to transform point cloud attributes based on weights. As described above, lift transform coding may optionally be applied.

[0124] According to the implementation method, the coefficient quantizer 30011 quantizes the attribute encoding based on the coefficient.

[0125] According to the implementation method, the arithmetic encoder 30012 encodes quantized attributes based on arithmetic coding.

[0126] Although not shown in the figure, Figure 3 The elements of the point cloud encoder 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 providing device. One or more processors can perform the above-described... Figure 3 At least one of the operations and / or functions of the elements of the point cloud encoder. Additionally, one or more processors can operate or perform operations for executing... Figure 3 The software program and / or instruction set for the operation and / or function of the elements of the point cloud encoder. One or more memories according to the embodiments may include high-speed random access memory, or include non-volatile memory (e.g., one or more disk storage devices, flash memory devices or other non-volatile solid-state memory devices).

[0127] Figure 4 An example of an octree and occupancy code according to an implementation is shown.

[0128] For reference Figure 1 to Figure 3 As described, the point cloud content delivery system (point cloud video encoder 10002) or point cloud encoder (e.g., octree analyzer 30002) performs octree geometric encoding (or octree encoding) based on an octree structure to efficiently manage the regions and / or locations of voxels.

[0129] Figure 4 The upper part shows the 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). The octree structure is created by recursively subdividing the bounding boxes aligned to the cubic axes defined by two poles (0, 0, 0) and (2d, 2d, 2d). Here, 2d can be set as 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, (xintn, yintn, zintn) represents the position (or position value) of the quantized point.

[0130]

[0131] like Figure 4 As shown in the upper center, the entire 3D space can be divided into eight spaces according to partitions. Each partitioned space is represented by a cube with six faces. For example... Figure 4As shown in the upper right, each of the eight spaces is further subdivided based on a coordinate system axis (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.

[0132] Figure 4 The lower part shows the octet occupancy code. The occupancy code generates the octet to indicate whether each of the eight partitions generated by dividing a space contains 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 contains at least one node, the node is assigned a value of 1. When the space corresponding to a child node does not contain a node (the space is empty), the node is assigned a value of 0. Since... Figure 4 The occupancy code shown is 00100001, thus indicating that the spaces corresponding to the third and eighth child nodes among the eight child nodes each contain 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 an 8-bit occupancy code. The attached figure shows that the occupancy code for the third child node is 10000111, and the occupancy code for the eighth child node is 01001111. A point cloud encoder (e.g., an arithmetic encoder 30004) according to an embodiment can perform entropy encoding on the occupancy code. To increase compression efficiency, the point cloud encoder can perform intra-frame / inter-frame encoding on the occupancy code. A receiving device (e.g., receiving device 10004 or point cloud video decoder 10006) according to an embodiment reconstructs the octree based on the occupancy code.

[0133] A point cloud encoder according to an implementation method (e.g., Figure 4 A point cloud encoder or octree analyzer (30002) can perform voxelization and octree encoding to store point locations. However, points are not always uniformly distributed in 3D space, so there may be specific regions with fewer points. Therefore, performing voxelization over the entire 3D space is inefficient. For example, when a specific region contains very few points, voxelization is not necessary in that specific region.

[0134] Therefore, for the aforementioned specific region (or nodes other than the leaf nodes of the octree), the point cloud encoder according to the embodiment can skip voxelization and perform direct encoding to directly encode the point positions included in the specific region. The coordinates of the directly encoded points according to the embodiment are called the Direct Encoding Mode (DCM). The point cloud encoder according to the embodiment can also perform triadic geometry encoding based on the surface model, which reconstructs the point positions in the specific region (or node) based on voxels. Triadic geometry encoding is a geometric encoding that represents an object as a series of triangular meshes. Therefore, the point cloud decoder can generate a point cloud from the mesh surface. Direct encoding and triadic geometry encoding according to the embodiment can be performed selectively. In addition, direct encoding and triadic geometry encoding according to the embodiment can be performed in combination with octree geometry encoding (or octree encoding).

[0135] To perform direct encoding, the option to apply direct encoding using direct mode should be enabled. The node to which direct encoding is applied must not be a leaf node, and there should be fewer than a threshold number of points within that node. Furthermore, the total number of points to which direct encoding is applied should not exceed a preset threshold. When the above conditions are met, the point cloud encoder (or arithmetic encoder 30004) according to the implementation can perform entropy encoding on the point locations (or location values).

[0136] A point cloud encoder according to an embodiment (e.g., a surface approximation analyzer 30003) can determine a specific level of the octree (a level less than the depth d of the octree) and can start from that level using a surface model to perform triadic geometry encoding to reconstruct the point positions in the node region based on voxels (triadic mode). The point cloud encoder according to an embodiment can specify the level to which triadic geometry encoding is to be applied. For example, when the specific level is equal to the depth of the octree, the point cloud encoder does not operate in triadic mode. In other words, the point cloud encoder according to an embodiment can only operate in triadic mode when the specified level is less than the depth value of the octree. The 3D cubic region of a node at a specified level according to an embodiment is called a block. A block may include one or more voxels. A block or voxel may correspond to a cube. Geometry is represented as surfaces within each block. A surface according to an embodiment may intersect each edge of a block at most once.

[0137] A block has 12 edges, therefore a block contains at least 12 intersections. Each intersection is called a vertex. Vertices along an edge are detected when there is at least one occupied voxel adjacent to the edge in all blocks sharing the edge. An occupied voxel, according to the implementation, refers to a voxel containing a point. The vertex position detected along an edge is the average position of the edges of all voxels adjacent to the edge in all blocks sharing the edge.

[0138] Once a vertex is detected, the point cloud encoder according to the implementation can perform entropy encoding on the edge's origin (x, y, z), the edge's direction vector (Δx, Δy, Δz), and the vertex position value (relative position value within the edge). When applying triad geometry encoding, the point cloud encoder according to the implementation (e.g., geometry reconstructor 30005) can generate the restored geometry (reconstructed geometry) by performing triangle reconstruction, upsampling, and voxelization processes.

[0139] Vertices located at the edges of a block determine the surface passing through the block. The surface, according to the implementation, is a non-planar polygon. During triangle reconstruction, the surface represented by triangles is reconstructed based on the origin of the edges, the direction vectors of the edges, and the position values ​​of the vertices. The triangle reconstruction process is performed as follows: i) calculating the centroid value of each vertex, ii) subtracting the centroid value from each vertex value, and iii) estimating the sum of squares of the values ​​obtained through the subtraction.

[0140]

[0141] The minimum value of the sum is estimated, and a projection process is performed based on the axis with the minimum value. For example, when element x is minimum, each vertex is projected onto the x-axis relative to the center of the block, and onto the (y, z) plane. When the value obtained by projection onto the (y, z) plane is (ai, bi), the value of θ is estimated by atan2(bi, ai), and the vertices are sorted based on the value of θ. Table 1 below shows the vertex combinations for creating triangles based on the number of vertices. Vertices are sorted from 1 to n. Table 1 below shows that for four vertices, two triangles can be constructed based on vertex combinations. The first triangle can be composed of vertices 1, 2, and 3 from the sorted vertices, and the second triangle can be composed of vertices 3, 4, and 1 from the sorted vertices.

[0142] [Table 1]

[0143] [Table 1] Triangles formed from vertices sorted by 1, ..., n

[0144] 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. The added points are called thinned vertices. A point cloud encoder according to an implementation can voxelize the thinned vertices. Additionally, the point cloud encoder can perform attribute encoding based on the voxelized positions (or position values).

[0145] Figure 5 Examples of point configurations in various Levels of Detail (LODs) according to implementation methods are shown.

[0146] For reference Figure 1 to Figure 4The described approach involves reconstructing (decompressing) the encoded geometry before performing attribute encoding. When direct encoding is applied, the geometry reconstruction operation may include altering the placement of directly encoded points (e.g., placing directly encoded points in front of the point cloud data). When triadic geometry encoding is applied, the geometry reconstruction process is performed through triangle reconstruction, upsampling, and voxelization. Since attributes depend on geometry, attribute encoding is performed based on the reconstructed geometry.

[0147] Point cloud encoders (e.g., LOD generator 30009) can classify (or reorganize) points according to LOD. The figure shows the point cloud content corresponding to LOD. The leftmost view in the figure represents the original point cloud content. The second view from the left in the figure represents the point distribution in the lowest LOD, and the rightmost view represents the point distribution in the highest LOD. That is, points are sparsely distributed in the lowest LOD and densely distributed in the highest LOD. In other words, as the LOD increases in the direction indicated by the arrow at the bottom of the figure, the space (or distance) between points narrows.

[0148] Figure 6 Examples of point configurations in various Levels of Detail (LODs) according to implementation methods are shown.

[0149] For reference Figure 1 to Figure 5 As described, a point cloud content providing system or point cloud encoder (e.g., point cloud video encoder 10002, Figure 3 A point cloud encoder or LOD generator (30009) can generate LODs. LODs are generated by reorganizing points into a set of refined levels based on a set of LOD distance values ​​(or a set of Euclidean distances). The LOD generation process is performed not only by the point cloud encoder but also by the point cloud decoder.

[0150] Figure 6 The upper part shows examples of points (P0 to P9) of point cloud content distributed in 3D space. Figure 6 In this context, the original order represents the order of points P0 to P9 before LOD generation. Figure 6 In this context, LOD-based order represents the order in which points are generated according to their LOD. Points are reorganized by LOD. Additionally, higher LODs include points belonging to lower LODs. For example... Figure 6 As shown, LOD0 contains P0, P5, P4, and P2. LOD1 contains the points of LOD0, P1, P6, and P3. LOD2 contains the points of LOD0, the points of LOD1, P9, P8, and P7.

[0151] For reference Figure 3 As described, the point cloud encoder according to the implementation can selectively or in combination perform predictive transform coding, lifting transform coding, and RAHT transform coding.

[0152] The point cloud encoder according to the embodiment can generate predictors for points to perform predictive transformation encoding for setting the predictive attributes (or predictive attribute values) of each point. That is, N predictors can be generated for N points. The predictors according to the embodiment can calculate weights (=1 / distance) based on the LOD value of each point, index information of neighboring points existing within a set distance of each LOD, and the distance to the neighboring points.

[0153] According to the implementation, the predicted attribute (or attribute value) is set as the average of the values ​​obtained by multiplying the attributes (or attribute values) of neighboring points (e.g., color, reflectivity, etc.) set in the predictor of each point by a weight (or weight value) calculated based on the distance to each neighboring point. The point cloud encoder (e.g., coefficient quantizer 30011) according to the implementation can quantize and inverse quantize the residual (which may be referred to as residual attribute, residual attribute value, attribute prediction residual, etc.) obtained by subtracting the predicted attribute (or attribute value) of each point from the attribute (attribute value) of each point. Tables 2 and 3 below illustrate the quantization process.

[0154] [Table 2]

[0155] [Table 3]

[0156] When the predictors of each point have neighboring points, the point cloud encoder (e.g., arithmetic encoder 30012) according to the embodiment can perform entropy encoding on the residual values ​​of quantization and inverse quantization as described above. When the predictors of each point do not have neighboring points, the point cloud encoder (e.g., arithmetic encoder 30012) according to the embodiment can perform entropy encoding on the attributes of the corresponding point without performing the above operations.

[0157] The point cloud encoder (e.g., lift transformer 30010) according to the embodiment can generate predictors for each point, set the calculated LOD and register neighboring points in the predictors, and set weights based on the distance to the neighboring points to perform lift transform coding. The lift transform coding according to the embodiment is similar to the prediction transform coding described above, but the difference is that weights are applied cumulatively to the attribute values. The process of cumulatively applying weights to the attribute values ​​according to the embodiment is configured as follows.

[0158] 1) Create an array quantized weights (QW) to store the weight values ​​of each point. The initial value of all elements of QW is 1.0. Multiply the QW value of the predictor index of the neighboring nodes registered in the predictor by the weight of the current point's predictor, and add the values ​​obtained by multiplication.

[0159] 2) Improve 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.

[0160] 3) Create temporary arrays called updateweight and update, and initialize the temporary arrays to zero.

[0161] 4) The weights calculated by multiplying the weights computed for all predictors by the weights stored in the QW corresponding to the predictor index are summed with the updateweight array and used as the index of the neighbor node. The values ​​obtained by multiplying the attribute values ​​of the neighbor node indexes by the calculated weights are summed with the update array.

[0162] 5) Improve the update process: Divide the attribute values ​​of the update array of all predictors by the weight values ​​of the updateweight array of the predictor index, and add the existing attribute values ​​to the values ​​obtained by division.

[0163] 6) For all predictors, the predicted attribute is calculated by multiplying the attribute value updated through the boosting update process by the weight updated through the boosting prediction process (stored in QW). The predicted attribute value is quantized by a point cloud encoder (e.g., coefficient quantizer 30011) according to the implementation. In addition, the point cloud encoder (e.g., arithmetic encoder 30012) performs entropy encoding on the quantized attribute value.

[0164] A point cloud encoder according to an embodiment (e.g., RAHT transform 30008) can perform RAHT transform coding, where attributes associated with lower-level nodes in an octree are used to predict attributes of higher-level nodes. RAHT transform coding is an example of intra-frame attribute coding performed by scanning backward through an octree. The point cloud encoder according to an embodiment scans the entire region starting from voxels and repeats 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 occupied nodes. The merging process is not performed on empty nodes. The merging process is performed on the node directly above an empty node.

[0165] The following equation represents the RAHT transformation matrix. In this equation, This represents the average attribute value of a voxel at level l. Based on and To calculate. and The weight is and .

[0166]

[0167] here, It is a low-pass value and is used in the next higher level of merging. This represents the high-pass coefficient. The high-pass coefficient at each step is quantized and subjected to entropy encoding (e.g., encoded by an arithmetic encoder 30012). Weights are calculated as follows: .pass and Create the root node as follows.

[0168]

[0169] The value of gDC is also quantized and entropy-encoded, just like the high-pass coefficient.

[0170] Figure 7 A point cloud decoder according to an embodiment is shown.

[0171] Figure 7 The point cloud decoder shown is an example of a point cloud decoder and can perform decoding operations. Figure 1 to Figure 6 The reverse processing of the encoding operation of the point cloud encoder shown.

[0172] For reference Figure 1 and Figure 6 As described, the point cloud decoder can perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

[0173] The point cloud decoder according to the implementation includes an arithmetic decoder (arithmetic decoding) 7000, an octree synthesizer (synthesized octree) 7001, a surface approximation synthesizer (synthesized surface approximation) 7002, a geometry reconstructor (reconstructed geometry) 7003, an inverse coordinate transformer (inverse coordinate transformation) 7004, an arithmetic decoder (arithmetic decoding) 7005, an inverse quantizer (inverse quantization) 7006, a RAHT transformer 7007, an LOD generator (generated LOD) 7008, an inverse lifter (inverse lift) 7009, and / or a color inverse transformer (inverse color transformation) 7010.

[0174] An arithmetic decoder 7000, an octree synthesizer 7001, a surface approximation synthesizer 7002, a geometry reconstructor 7003, and a coordinate inverse transformer 7004 can perform geometric decoding. Geometric decoding according to the embodiment may include direct decoding and triplet geometric decoding. Direct encoding and triplet geometric decoding are selectively applied. Geometric decoding is not limited to the examples described above and is provided as a reference. Figure 1 to Figure 6 The reverse processing of the described geometric encoding is performed.

[0175] The arithmetic decoder 7000 according to the embodiment decodes the received geometric bitstream based on arithmetic coding. The operation of the arithmetic decoder 7000 corresponds to the reverse processing of the arithmetic encoder 30004.

[0176] The octree synthesizer 7001 according to the embodiment can generate an octree by obtaining occupancy codes (or information about the geometry obtained as a decoding result) from the decoded geometry bitstream. The occupancy codes are as follows: Figure 1 to Figure 6 Please describe that configuration in detail.

[0177] When applying triplet geometry encoding, the surface approximation synthesizer 7002 according to the implementation can synthesize the surface based on the decoded geometry and / or the generated octree.

[0178] The geometry reconstructor 7003 according to the embodiment can regenerate geometry based on surface and / or decoded geometry. See also... Figure 1 to Figure 9 As described, direct encoding and triadic geometric encoding are selectively applied. Therefore, the geometry reconstructor 7003 directly imports and sums the positional information of points for which direct encoding has been applied. When triadic geometric encoding is applied, the geometry reconstructor 7003 can reconstruct the geometry by performing the reconstruction operations (e.g., triangle reconstruction, upsampling, and voxelization) of the geometry reconstructor 30005. Details and references Figure 6 The descriptions are the same for all of them, so their descriptions are omitted. The reconstructed geometry may include point cloud images or frames that do not contain attributes.

[0179] According to the implementation method, the inverse coordinate transformer 7004 can obtain the point position based on the reconstructed geometric transformation coordinates.

[0180] Arithmetic decoder 7005, inverse quantizer 7006, RAHT transformer 7007, LOD generator 7008, inverse lifter 7009, and / or color inverse transformer 7010 can perform attribute decoding. Attribute decoding according to embodiments includes Region Adaptive Hierarchical Transformation (RAHT) decoding, interpolation-based hierarchical nearest neighbor prediction (prediction transform) decoding, and interpolation-based hierarchical nearest neighbor prediction (lift transform) decoding with update / lifting steps. The above three decoding schemes can be used selectively, or a combination of one or more decoding schemes can be used. Attribute decoding according to embodiments is not limited to the examples described above.

[0181] According to the implementation method, the arithmetic decoder 7005 decodes the attribute bitstream by arithmetic encoding.

[0182] The inverse quantizer 7006 according to the implementation dequantizes information about the decoded attribute bitstream or the attributes obtained as a decoding result, and outputs the dequantized attributes (or attribute values). Inverse quantization can be selectively applied based on the attribute encoding of the point cloud encoder.

[0183] According to the implementation, the RAHT transformer 7007, LOD generator 7008, and / or inverse lifter 7009 can process the reconstructed geometry and inverse quantization attributes. As described above, the RAHT transformer 7007, LOD generator 7008, and / or inverse lifter 7009 can selectively perform decoding operations corresponding to the encoding of the point cloud encoder.

[0184] According to the implementation, the color inverse transformer 7010 performs inverse transformation encoding to inversely transform the color values ​​(or textures) included in the decoded attributes. The operation of the color inverse transformer 7010 can be selectively performed based on the operation of the color transformer 30006 of the point cloud encoder.

[0185] Although not shown in the figure, Figure 7 The elements of the point cloud 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 providing device. One or more processors can perform the above-described... Figure 7 The point cloud decoder's components include at least one or more operations and / or functions. Additionally, one or more processors can operate or perform operations for executing... Figure 7 The software program and / or instruction set for the operation and / or function of the elements of the point cloud decoder.

[0186] Figure 8 A transmitting device according to an embodiment is shown.

[0187] Figure 8 The transmitting device shown is Figure 1 The transmitting device 10000 (or Figure 3 Example of a point cloud encoder. Figure 8 The transmitting device shown can perform the same operation as the reference. Figure 1 to Figure 6 The described point cloud 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 8000, a quantization processor 8001, a voxelization processor 8002, an octree occupancy code generator 8003, a surface model processor 8004, an intra / inter-frame coding processor 8005, an arithmetic encoder 8006, a metadata processor 8007, a color transformation processor 8008, an attribute transformation processor 8009, a prediction / boosting / RAHT transformation processor 8010, an arithmetic encoder 8011, and / or a transmission processor 8012.

[0188] According to the embodiment, the data input unit 8000 receives or acquires point cloud data. The data input unit 8000 can perform the same operation and / or acquisition method as the point cloud video acquisition unit 10001 (or refer to...). Figure 2The described acquisition process (20000) is the same or similar operation and / or acquisition method.

[0189] The data input unit 8000, quantization processor 8001, voxelization processor 8002, octree occupancy code generator 8003, surface model processor 8004, intra / inter-frame coding processor 8005, and arithmetic encoder 8006 perform geometric coding. Geometric coding according to the implementation method and reference... Figure 1 to Figure 9 The geometric codes described are the same or similar, so their detailed descriptions are omitted.

[0190] The quantization processor 8001 according to the implementation quantizes geometry (e.g., point position values). The operation of the quantization processor 8001 and / or quantization with reference... Figure 3 The operation and / or quantization of the described quantizer 30001 are the same or similar. Details and references Figure 1 to Figure 9 The descriptions are the same.

[0191] According to the embodiment, the voxelization processor 8002 voxels the quantized position values ​​of points. The voxelization processor 8002 can perform operations similar to those described above. Figure 3 The operation and / or voxelization process of the quantizer 30001 described are the same as or similar to the operation and / or process. Details and references Figure 1 to Figure 6 The descriptions are the same.

[0192] According to the implementation method, the octree occupancy code generator 8003 performs octree encoding based on the voxelized positions of points in the octree structure. The octree occupancy code generator 8003 can generate occupancy codes. The octree occupancy code generator 8003 can perform encoding with reference to... Figure 3 and Figure 4 The operations and / or methods described are the same as or similar to those of the point cloud encoder (or octree analyzer 30002). Details and references Figure 1 to Figure 6 The descriptions are the same.

[0193] According to the implementation, the surface model processor 8004 can perform triadic geometry encoding based on a surface model to reconstruct point positions in a specific region (or node) based on voxels. The surface model processor 8004 can perform operations with reference to... Figure 3 The operations and / or methods described are the same as or similar to those of the point cloud encoder (e.g., surface approximation analyzer 30003). Details and references are available. Figure 1 to Figure 6 The descriptions are the same.

[0194] According to an embodiment, the intra / inter-frame coding processor 8005 can perform intra / inter-frame coding on point cloud data. The intra / inter-frame coding processor 8005 can perform coding that is the same as or similar to intra / inter-frame coding. According to an embodiment, the intra / inter-frame coding processor 8005 may be included in an arithmetic encoder 8006.

[0195] The arithmetic encoder 8006 according to the embodiment performs entropy encoding on the octree and / or approximate octree of point cloud data. For example, the encoding scheme includes arithmetic encoding. The arithmetic encoder 8006 performs the same or similar operations and / or methods as the arithmetic encoder 30004.

[0196] The metadata processor 8007 according to an embodiment processes metadata (e.g., set values) about point cloud data and provides it to necessary processing procedures such as geometric encoding and / or attribute encoding. Additionally, the metadata processor 8007 according to an embodiment can generate and / or process signaling information related to geometric encoding and / or attribute encoding. The signaling information according to an embodiment can be encoded separately from the geometric encoding and / or attribute encoding. The signaling information according to an embodiment can be interleaved.

[0197] The color transformation processor 8008, attribute transformation processor 8009, prediction / boosting / RAHT transformation processor 8010, and arithmetic encoder 8011 perform attribute encoding. Attribute encoding and reference according to the implementation method. Figure 1 to Figure 6 The attribute codes described are the same or similar, so their detailed descriptions are omitted.

[0198] According to the implementation, the color transformation processor 8008 performs color transformation encoding to transform color values ​​included in attributes. The color transformation processor 8008 can perform color transformation encoding based on reconstructed geometry. The reconstructed geometry and reference... Figure 1 to Figure 9 The description is the same. Furthermore, its execution is the same as the reference. Figure 3 The operation and / or methods of the described color converter 30006 are the same as or similar to those described. Detailed descriptions are omitted.

[0199] According to the implementation, the attribute transformation processor 8009 performs attribute transformation to transform attributes based on reconstructed geometry and / or locations where geometric encoding is not performed. The attribute transformation processor 8009 performs transformations with reference to... Figure 3 The operation and / or method of the described attribute transformer 30007 are the same as or similar to those described. Detailed descriptions are omitted. The prediction / boosting / RAHT transformation processor 8010 according to the embodiment can encode the transformed attributes using any one or a combination of RAHT encoding, prediction transformation encoding, and boosting transformation encoding. The prediction / boosting / RAHT transformation processor 8010 performs and references... Figure 3The RAHT transformer 30008, LOD generator 30009, and boost transformer 30010 described herein operate at least one of the same or similar operations. Furthermore, the predictive transform coding, boost transform coding, and RAHT transform coding are similar to those of the reference transformer. Figure 1 to Figure 9 The descriptions are the same, so their detailed descriptions are omitted.

[0200] The arithmetic encoder 8011 according to the embodiment can encode the attributes of the code based on arithmetic encoding. The arithmetic encoder 8011 performs the same or similar operations and / or methods as the arithmetic encoder 300012.

[0201] The transmission processor 8012 according to an embodiment can transmit individual bitstreams containing encoded geometric and / or encoded attribute or metadata information, or transmit a single bitstream containing encoded geometric and / or encoded attribute and metadata information. When the encoded geometric and / or encoded attribute and metadata information according to an embodiment is configured as a single bitstream, the bitstream may include one or more sub-bitstreams. The bitstream according to an embodiment may include signaling information and slice data. The signaling information includes a sequence parameter set (SPS) for sequence-level signaling, a geometric parameter set (GPS) for geometric information encoded signaling, an attribute parameter set (APS) for attribute information encoded signaling, and a tile parameter set (TPS) for tile-level signaling. The slice data may include information about one or more slices. A slice according to an embodiment may include a geometric bitstream Geom00 and one or more attribute bitstreams Attr00 and Attr10.

[0202] A slice is a series of syntax elements that represent all or part of a coded point cloud frame.

[0203] The TPS according to an embodiment may include information about individual tiles in one or more tiles (e.g., coordinate information and height / size information about the bounding box). The geometric bitstream may include a header and a payload. The header of the geometric bitstream according to an embodiment may include a geom_parameter_set_id, a geom_tile_id, and a geom_slice_id included in the GPS, as well as information about the data contained in the payload. As described above, the metadata processor 8007 according to an embodiment may generate and / or process signaling information and transmit it to the transmission processor 8012. According to an embodiment, the element performing geometry encoding and the element performing attribute encoding may share data / information with each other, as indicated by the dashed lines. The transmission processor 8012 according to an embodiment may perform the same or similar operations and / or transmission methods as the transmitter 10003. Details and ReferencesFigure 1 and Figure 2 The descriptions are the same, so their descriptions are omitted.

[0204] Figure 9 A receiving device according to an embodiment is shown.

[0205] Figure 9 The receiving device shown is Figure 1 Example of receiving device 10004. Figure 9 The receiving device shown can perform the same operation as the reference. Figure 1 to Figure 8 The same or similar one or more operations and methods described in the point cloud decoder.

[0206] The receiving apparatus according to the embodiments may include a receiver 9000, a receiving processor 9001, an arithmetic decoder 9002, an octree reconstruction processor based on occupancy codes 9003, a surface model processor (triangle reconstruction, upsampling, voxelization) 9004, an inverse quantization processor 9005, a metadata parser 9006, an arithmetic decoder 9007, an inverse quantization processor 9008, a prediction / boost / RAHT inverse transform processor 9009, a color inverse transform processor 9010, and / or a renderer 9011. Each decoding element according to the embodiments can perform inverse processing of the operation of the corresponding encoding element according to the embodiments.

[0207] Receiver 9000, according to the embodiment, receives point cloud data. Receiver 9000 can perform operations related to... Figure 1 The operation and / or receiving method of the receiver 10005 are the same as or similar to those of the receiver. Detailed description omitted.

[0208] The receiving processor 9001 according to the embodiment can acquire geometric bitstreams and / or attribute bitstreams from the received data. The receiving processor 9001 may be included in the receiver 9000.

[0209] Arithmetic decoder 9002, octet-based octree reconstruction processor 9003, surface model processor 9004, and inverse quantization processor 9005 can perform geometric decoding. Geometric decoding and reference according to the implementation method. Figure 1 to Figure 8 The described geometric decodings are the same or similar, so their detailed descriptions are omitted.

[0210] The arithmetic decoder 9002 according to the embodiment can decode geometric bitstreams based on arithmetic coding. The arithmetic decoder 9002 performs the same or similar operations and / or encodings as the arithmetic decoder 7000.

[0211] According to an embodiment, the octree reconstruction processor 9003 based on occupancy codes can reconstruct an octree by obtaining occupancy codes from the decoded geometric bitstream (or information about the geometry obtained as a decoding result). The octree reconstruction processor 9003 performs operations and / or methods identical or similar to those of the octree synthesizer 7001 and / or the octree generation method. When applying triad geometry encoding, the surface model processor 9004 according to an embodiment can perform triad geometry decoding and related geometric reconstruction (e.g., triangle reconstruction, upsampling, voxelization) based on surface model methods. The surface model processor 9004 performs operations identical or similar to those of the surface approximation synthesizer 7002 and / or the geometry reconstructor 7003.

[0212] The inverse quantization processor 9005 according to the implementation method can inverse quantize the decoded geometry.

[0213] The metadata parser 9006 according to the implementation can parse metadata (e.g., setting values) contained in the received point cloud data. The metadata parser 9006 can pass the metadata to geometry decoding and / or attribute decoding. Metadata and reference Figure 8 The metadata described is the same, so its detailed description is omitted.

[0214] Arithmetic decoder 9007, inverse quantization processor 9008, prediction / boost / RAHT inverse transform processor 9009, and color inverse transform processor 9010 perform attribute decoding. Attribute decoding and reference Figure 1 to Figure 8 The attributes described by at least one of them are identical or similar, so their detailed descriptions are omitted.

[0215] The arithmetic decoder 9007 according to the embodiment can decode the attribute bitstream via arithmetic coding. The arithmetic decoder 9007 can decode the attribute bitstream based on the reconstructed geometry. The arithmetic decoder 9007 performs the same or similar operations and / or encodings as the arithmetic decoder 7005.

[0216] The inverse quantization processor 9008 according to the embodiment can inverse quantize the decoded attribute bitstream. The inverse quantization processor 9008 performs the same or similar operations and / or methods as the inverse quantizer 7006.

[0217] The predictive / boost / RAHT inverse transform processor 9009, according to the implementation, can handle the reconstructed geometry and inverse quantization properties. The predictive / boost / RAHT inverse transform processor 9009 performs operations related to... Figure 7The operation and / or decoding of the RAHT transformer 7007, LOD generator 7008, and / or inverse booster 7009 are the same as or similar to one or more operations and / or decodings. The color inverse transform processor 9010 according to the embodiment performs inverse transform encoding to inverse transform the color values ​​(or textures) included in the decoded attributes. The color inverse transform processor 9010 performs operations similar to... Figure 7 The operation and / or inverse transform encoding of the color inverse transformer 7010 are the same as or similar to the operation and / or inverse transform encoding. The renderer 9011 according to the embodiment can render point cloud data.

[0218] Figure 10 An exemplary structure that can be combined with a point cloud data transmission / reception method / apparatus according to an embodiment is shown.

[0219] Figure 10 The structure represents a configuration in which at least one of the following components—server 1060, robot 1010, autonomous vehicle 1020, XR device 1030, smartphone 1040, home appliance 1050, and / or head-mounted display (HMD) 1070—is connected to cloud network 1000. Robot 1010, autonomous vehicle 1020, XR device 1030, smartphone 1040, or home appliance 1050 are referred to as devices. Furthermore, XR device 1030 may correspond to a point cloud data (PCC) device according to an embodiment or may be operatively connected to a PCC device.

[0220] Cloud Network 1000 can refer to a network that forms part of or exists within a cloud computing infrastructure. Here, Cloud Network 1000 can be configured using a 3G network, a 4G or Long Term Evolution (LTE) network, or a 5G network.

[0221] Server 1060 can be connected via cloud network 1000 to at least one of robot 1010, self-driving vehicle 1020, XR device 1030, smartphone 1040, home appliance 1050 and / or HMD 1070, and can assist at least a portion of the processing of the connected devices 1010 to 1070.

[0222] HMD 1070 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.

[0223] Hereinafter, various embodiments of the apparatus 1010 to 1050 that apply the above-described technology will be described. Figure 10 The devices 1010 to 1050 shown can be operatively connected to / coupled to the point cloud data transmitting and receiving devices according to the above embodiments.

[0224] <PCC+XR>

[0225] The XR / PCC device 1030 can employ PCC technology and / or XR (AR+VR) technology, and can be implemented as an HMD, a head-up display (HUD) installed 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.

[0226] The XR / PCC device 1030 can analyze 3D point cloud data or image data acquired through various sensors or from external devices and generate positional and attribute data about 3D points. Thus, the XR / PCC device 1030 can acquire information about the surrounding space or real-world objects and render and output XR objects. For example, the XR / PCC device 1030 can match an XR object, including auxiliary information about the identified object, with the identified object and output the matched XR object.

[0227] <PCC+Mobile Phone>

[0228] The XR / PCC device 1030 can be implemented as a smartphone 1040 by applying PCC technology.

[0229] The 1040 smartphone can decode and display point cloud content based on PCC technology.

[0230] <PCC+Self-Driving+XR>

[0231] The self-driving vehicle 1020 can be realized as a mobile robot, vehicle, unmanned aerial vehicle, etc. by applying PCC technology and XR technology.

[0232] The self-driving vehicle 1020 employing XR / PCC technology can refer to a self-driving vehicle equipped with means for providing XR images, or a self-driving vehicle serving as a control / interaction target in an XR image. Specifically, as a control / interaction target in an XR image, the self-driving vehicle 1020 can be distinguished from and operably connected to the XR device 1030.

[0233] The autonomous vehicle 1020, equipped with means for providing XR / PCC images, can acquire sensor information from sensors including cameras and output generated XR / PCC images based on the acquired sensor information. For example, the autonomous vehicle 1020 may have a HUD and output XR / PCC images to it, thereby providing passengers with XR / PCC objects corresponding to real objects or objects presented on the screen.

[0234] When an XR / PCC object is output to a HUD, at least a portion of the XR / PCC object can be output to overlap with the actual object being pointed at by the passenger's eyes. Conversely, when an XR / PCC object is output to a display installed within the autonomous vehicle, at least a portion of the XR / PCC object can be output to overlap with objects on the screen. For example, the autonomous vehicle 1220 can output XR / PCC objects corresponding to objects such as roads, other vehicles, traffic lights, traffic signs, two-wheeled vehicles, pedestrians, and buildings.

[0235] Virtual reality (VR), augmented reality (AR), mixed reality (MR), and / or point cloud compression (PCC) technologies according to the implementation methods are applicable to various devices.

[0236] In other words, VR technology is a display technology that only provides CG images of real-world objects, backgrounds, etc. AR technology, on the other hand, refers to the technology of displaying virtually created CG images on top of images of real objects. MR technology is similar to AR technology in that the virtual objects to be displayed are mixed and combined with the real world. However, MR technology differs from AR technology in that AR technology clearly distinguishes between real objects and virtual objects created as CG images and uses virtual objects as supplementary objects to real objects, while MR technology treats virtual objects as objects with the same characteristics as real objects. More specifically, an example of MR technology application is holographic services.

[0237] Recently, VR, AR, and MR technologies have often been referred to as extended reality (XR) technologies rather than being clearly distinguished from each other. Therefore, embodiments of this disclosure are applicable to any of VR, AR, MR, and XR technologies. Encoding / decoding based on PCC, V-PCC, and G-PCC technologies are applicable to such technologies.

[0238] The PCC method / apparatus according to the embodiments can be applied to vehicles that provide autonomous driving services.

[0239] Vehicles providing autonomous driving services connect to the PCC device for wired / wireless communication.

[0240] When the point cloud data (PCC) transmitting / receiving device according to the embodiment is connected to a vehicle for wired / wireless communication, the device can receive / process content data related to AR / VR / PCC services (which may be provided along with autonomous driving services) and transmit it to the vehicle. When the PCC transmitting / receiving device is installed in the vehicle, it can receive / process content data related to AR / VR / PCC services based on user input signals input through a user interface device and provide it to the user. The vehicle or user interface device according to the embodiment can receive user input signals. User input signals according to the embodiment may include signals indicating autonomous driving services.

[0241] The point cloud data transmission method / apparatus according to the implementation method can be interpreted as referring to... Figure 1 The transmitting device 10000, the point cloud video encoder 10002, and the transmitter 10003, Figure 2 Acquisition 20000 - Encoding 20001 - Transmission 20002 Figure 3 Point cloud video encoder, Figure 8 The transmitting device Figure 10 The device Figure 25 Encoding methods Figure 27 The transmitting device Figure 40 Terms related to sending methods, etc.

[0242] The point cloud data receiving method / apparatus according to the embodiments can be interpreted as referring to... Figure 1 The receiving device 10004, receiver 10005, and point cloud video decoder 10006. Figure 2 Transmission 20002 - Decoding 20003 - Rendering 20004 Figure 7 Point cloud video decoder, Figure 9 The receiving device Figure 10 The device Figure 26 Decoding methods Figure 28 The receiving device Figure 41 Terminology related to receiving methods, etc.

[0243] The point cloud data transmission / reception method / apparatus according to the embodiments can be simply referred to as the method / apparatus according to the embodiments.

[0244] According to the implementation method, geometric data, geometric information, and location information constituting point cloud data are interpreted as having the same meaning. Attribute data and attribute information constituting point cloud data are also interpreted as having the same meaning.

[0245] The method / apparatus according to the implementation can process point cloud data while taking scalable transmission into account.

[0246] Regarding the method / apparatus according to the embodiments, this document discloses a method for efficiently supporting selective decoding of partial data based on receiver performance or transmission speed when transmitting / receiving point cloud data. Specifically, this disclosure proposes a technique for improving the efficiency of scalable coding, wherein the encoder at the transmitting end can selectively deliver information required by the decoder at the receiving end for compressed data, and the decoder can decode it, wherein coding units can be configured as independent slices in the sense of tree level, LOD, layer group unit, etc.

[0247] Specifically, this disclosure proposes a method for improving the efficiency of scalable coding in point cloud data compression methods. Here, scalable coding is a technique for gradually changing the resolution of data based on receiver requests / processing speed / performance / transmission bandwidth, etc., so that compressed data can be efficiently delivered from the sending side and decoded at the receiving side. For this purpose, packetization for efficiently delivering layer-configured point cloud data can be applied in conjunction with the techniques of this disclosure. Specifically, this paper proposes a method for configuring slice fragments to be more suitable for scalable PCC services and sending / receiving these slice fragments when using a direct compression mode for location compression. Additionally, a compression method for efficient storage and transmission of large point cloud data with wide distribution and high point density can be used.

[0248] Reference Figure 3 and Figure 7 The illustrated implementation of a point cloud data transmitting / receiving apparatus (which may be simply referred to as an encoder / decoder) shows point cloud data composed of a set of points. Each point includes geometric information (or geometry or geometric data) and attribute information (or attributes or attribute data). The geometric information is the three-dimensional position information (x, y, z) of each point. That is, the position of each point is represented by parameters in a coordinate system representing three-dimensional space (e.g., parameters (x, y, z) representing the three axes of space (e.g., the X-axis, Y-axis, and Z-axis)). The attribute information represents the point's color (RGB, YUV, etc.), reflectivity, normal vector, transparency, etc. In point cloud compression (PCC), octree-based compression is performed to efficiently compress non-uniform distributions in three-dimensional space, and the attribute information is compressed according to the octree-based compression. Figure 3 and Figure 7 The point cloud video encoder and point cloud video decoder shown can be processed by corresponding components according to the implementation method.

[0249] According to one implementation, the transmitting device compresses geometric information (e.g., location) and attribute information (e.g., color / brightness / reflectivity, etc.) about the point cloud data and sends the compressed information to the receiving device. The point cloud data can be configured according to an octree structure having layers based on the level of detail (LoD). Scalable point cloud data encoding and representation can then be performed based on the configuration. In this case, only a portion of the point cloud data can be decoded or represented depending on the performance of the receiving device or the transmission rate. However, there is currently no method for removing unnecessary data in advance.

[0250] In other words, when only a portion of the scalable point cloud compressed bitstream needs to be sent (e.g., only a portion of the decoding layer in scalable decoding), it is impossible to select and send the necessary parts. Therefore, as... Figure 11 As shown, the transmitting device should re-encode the necessary parts after decoding, or as follows: Figure 12 As shown, when complete data is transmitted to the receiving device, the receiving device should selectively apply the necessary data after decoding.

[0251] However, in Figure 11 In such cases, delays may occur due to the time required for decoding and re-encoding. Figure 12 In such cases, bandwidth efficiency may be reduced due to sending unnecessary data to the receiving device. Furthermore, when using fixed bandwidth, it may be necessary to degrade data quality to transmit data.

[0252] Therefore, the method / apparatus according to the embodiments can provide slices so that the point cloud can be divided into regions for processing.

[0253] Specifically, in the case of octree-based positional compression, entropy-based compression and direct compression can be used together. In this regard, this paper proposes a slice configuration for efficiently utilizing scalability.

[0254] Furthermore, regarding the method / apparatus according to the implementation, a slice fragment structure of point cloud data can be defined, and a signal can be sent to notify the scalable layer and slice structure for scalable transmission.

[0255] The method / apparatus according to the implementation can divide and process bit streams in specific units for efficient bit stream transmission and decoding.

[0256] The method / apparatus according to the implementation method can realize selective transmission and decoding of hierarchical point cloud data in units of bit streams.

[0257] The units according to the implementation may be referred to as LODs, layers, slices, etc. LOD is the same term as LOD in attribute data encoding, but in another sense it can refer to a data unit corresponding to a hierarchical structure of a bitstream. An LOD can be a concept corresponding to a bundle of one or two or more depths (e.g., the depth (level) of an octree or multiple trees) of a layered structure based on point cloud data. Similarly, a layer is a unit that provides a sub-bitstream. It is a concept corresponding to a bundle of one or two or more depths and can correspond to one LOD or two or more LODs. Furthermore, a slice is a unit used to configure sub-bitstreams and can correspond to one depth, a portion of a depth, or two or more depths. Additionally, a slice can correspond to one LOD, a portion of an LOD, or two or more LODs. According to the implementation, LODs, layers, and slices can correspond to each other, or one of LODs, layers, and slices can be included in another. Furthermore, units according to the implementation may include LODs, layers, slices, layer groups, or subgroups, and can refer to each other interchangeably. According to the implementation method, in an octree structure, layer, depth, level, and depth level can have the same meaning.

[0258] Figure 13 This is a diagram illustrating an example of layer-based point cloud data configuration according to an implementation method. Figure 13 An example of an octree structure is shown, where the depth level of the root node is set to 0 and the depth level of the leaf nodes is set to 7.

[0259] The method / apparatus according to the embodiments can be as follows: Figure 13 The configuration shown is based on layer-based point cloud data for encoding and decoding.

[0260] The layering of point cloud data according to the implementation method can have a layered structure in terms of SNR, spatial resolution, color, temporal frequency, bit depth, etc., based on the application field, and layers can be configured in the direction of increasing data density based on octree structure or LOD structure.

[0261] In other words, when generating a Level of Detail (LOD) based on an octree structure, the LOD can be defined as increasing in the direction of detail (i.e., in the direction of increasing octree depth level). In this disclosure, a layer can have the same meaning as level, depth, and depth level.

[0262] Reference Figure 13For example, in an octree structure with 7 depth levels in addition to the root node level (or root level), LOD 0 is configured to include levels from the root node level to the octree depth level 4, LOD 1 is configured to include levels from the root node level to the octree depth level 5, and LOD 2 is configured to include levels from the root node level to the octree depth level 7.

[0263] Figure 14 (a) illustrates the bitstream structure of geometric data according to the embodiment, and Figure 14 (b) illustrates the bitstream structure of attribute data according to the implementation method.

[0264] The method / apparatus according to the embodiments can be based on, for example... Figure 13 The octree structure shown is used to generate LOD through hierarchical generation, and as... Figure 14 (a) and Figure 14 The configuration shown in (b) is the geometric bitstream and attribute bitstream.

[0265] According to the implementation method, the transmitting device can divide the bit stream obtained by point cloud compression into a geometric bit stream and an attribute bit stream according to the data type in order to transmit the bit stream.

[0266] In this scenario, each bitstream can be composed of slices and transmitted. Depending on the implementation, geometric bitstreams (e.g., Figure 14 (a) and attribute bitstreams (e.g., Figure 14 Each of (b) can be configured as a slice and passed regardless of the layer information or LoD information. In this case, in order to use only some layers or LoD, the bitstream should be decoded, only the parts to be used should be selected and unnecessary parts should be removed, and only the necessary information should be re-encoded.

[0267] This disclosure proposes a method for dividing a bitstream into layers (or LoD) to avoid such unnecessary intermediate operations.

[0268] Figure 15 This is a diagram illustrating an example of a bitstream configuration for transmitting a bitstream divided into layers (or LoD) according to an implementation method.

[0269] For example, in the case of LoD-based PCC technology, lower LoDs are included in higher LoDs. That is, higher LoDs include all points of lower LoDs. Furthermore, when information about points included in the current LoD but not in the previous LoD (i.e., new points added to each LoD) is defined as R (reserved), the transmitting device can divide the initial LoD information and the newly included information R in each LoD into independent units (e.g., slices) and transmit them, such as... Figure 15 As shown.

[0270] In other words, the set of new points added to configure each LoD compared to the previous LoD can be defined as information R. Figure 15 An example is given where LoD1 includes LoD0 and information R1 and LoD2 includes LoD1 and information R2.

[0271] According to one implementation, points sampled for one or more octree depth levels can be determined as data belonging to information R. That is, the set of points sampled for one or more octree depth levels (i.e., points matching the occupied node) can be defined as information R. According to another implementation, points sampled for one octree depth level can be divided into multiple information Rs according to predetermined criteria. In this case, various criteria for dividing one octree depth level into multiple information Rs can be considered as follows. For example, when one octree depth level is divided into M information Rs, the M information Rs can be configured such that the data in information Rs can have consecutive Morton codes, or can be grouped such that the data in information Rs have the same remainder obtained by dividing the Morton code sequential index by M. Alternatively, when grouped as sibling nodes, the M information Rs can be configured by grouping data at the same position. According to another implementation, if necessary, some sampled points at multiple octree depth levels can be determined as information R.

[0272] exist Figure 15 In this example, according to the implementation, the geometry bitstream and attribute bitstream are each divided into three slices. Each slice includes a header and a payload (also called a data unit) containing the actual data (e.g., geometry data, attribute data). The header may contain information about the corresponding slice. Additionally, the header may also contain reference information related to the previous slice, the previous LoD, or the previous layer used for LoD configuration.

[0273] For example, refer to Figure 15The geometric bitstream is divided into slices carrying geometric data belonging to LoD0, slices carrying geometric data belonging to information R1, and slices carrying geometric data belonging to information R2. The attribute bitstream is divided into slices carrying attribute data belonging to LoD0, slices carrying attribute data belonging to information R1, and slices carrying attribute data belonging to information R2.

[0274] The receiving method / apparatus according to the embodiments can receive bit streams divided into LODs or layers, and can efficiently decode only the data to be used without complex intermediate processes.

[0275] In this regard, various implementation methods can be used to send bit streams.

[0276] For example, geometric bitstreams and attribute bitstreams can be transmitted separately. Alternatively, geometric bitstreams and attribute bitstreams can be multiplexed into a single bitstream to be transmitted.

[0277] When each bitstream contains LoD0 and one or more pieces of information R, the transmission order of LoD0 and one or more pieces of information R can vary.

[0278] exist Figure 15 In the example, the geometry bitstream and the attribute bitstream are transmitted separately. In this case, LoD0 containing the geometry bitstream and two pieces of information R (R1, R2) are transmitted sequentially, and LoD0 containing the attribute bitstream and two pieces of information R (R1, R2) are transmitted sequentially.

[0279] Figure 16 An exemplary bitstream ordering is illustrated according to an implementation method when a geometric bitstream and an attribute bitstream are multiplexed into a single bitstream.

[0280] The transmission method / apparatus according to the implementation method can transmit bit streams as follows: Figure 16 The diagram illustrates the serial transmission of geometric and attribute data. In this operation, depending on the data type, the entire geometric data (or geometric information) can be transmitted first, followed by the attribute data (or attribute information). In this case, the geometric data can be quickly reconstructed based on the transmitted bitstream information.

[0281] Reference Figure 16 For example, a layer (LOD) containing geometric data can be positioned first in the bitstream, and a layer (LOD) containing attribute data can be positioned after the geometric layer. Since the attribute data depends on the geometric data, the layer (LOD) containing the geometric data can be positioned before the layer (LOD) containing the attribute data. Figure 16An example is given where LoD0 containing geometric data and two pieces of information R (R1, R2) is passed sequentially, followed by LoD0 containing attribute data and two pieces of information R (R1, R2). The positions can be changed depending on the implementation. Furthermore, references can be made between geometric headers, and also between attribute headers and geometric headers.

[0282] Figure 17 Another exemplary bitstream sorting method is illustrated according to an implementation method when a geometric bitstream and an attribute bitstream are multiplexed into a single bitstream.

[0283] When transmitting a bit stream, the transmission method / apparatus according to the implementation method can be as follows: Figure 17 The diagram shows the serial transmission of geometry and attribute data. In this case, the bitstreams constituting the same layer containing both geometry and attribute data can be bundled and transmitted. This reduces decoding execution time when compression techniques for parallel decoding of geometry and attributes are used. In this regard, the information that needs to be processed first can be prioritized (low LoD and geometry should precede attributes).

[0284] Figure 17 An example is illustrated where LoD0 containing geometric data, LoD0 containing attribute data, information R1 containing geometric data, information R1 containing attribute data, information R2 containing geometric data, and information R2 containing attribute data are sent in this order. In this case, the positions can be changed depending on the implementation. Furthermore, references can be made between the geometric headers, and also between the attribute header and the geometric header.

[0285] The transmitting / receiving method / apparatus according to the embodiments can efficiently select the desired layer (or LoD) in the application domain at the bitstream level when transmitting and receiving bitstreams. In the bitstream ordering method according to the embodiments, when such... Figure 16 When grouping and sending geometric information as shown, there may be empty segments in the middle after selecting the bitstream level. In this case, it may be necessary to rearrange the bitstream.

[0286] like Figure 18 (a) to Figure 18 (c) or Figure 19 (a) to Figure 19 As shown in (c), by means of... Figure 17 The layers shown bundle and send geometric and attribute data, allowing for the selective delivery of necessary information and / or the selective removal of unnecessary information based on the application domain.

[0287] Figure 18 (a) to Figure 18(c) is a diagram illustrating an example of symmetric geometry-attribute selection according to the implementation method.

[0288] For example, refer to Figure 18 (a) to Figure 18 (c) When it is necessary to select a portion of the bitstream according to the implementation method, the transmitting device selects and transmits data only up to LoD1 (i.e., LoD0+R1), and removes the information R2 corresponding to the upper layer (i.e., the new portion of LoD2) from the bitstream without transmitting the information R2. In the case of symmetric geometry-attribute selection, the geometric data and attribute data of the same layer are selected and transmitted simultaneously, or selected and removed simultaneously.

[0289] Figure 19 (a) to Figure 19 (c) is a diagram illustrating an example of asymmetric geometry-attribute selection according to an implementation method. In the case of asymmetric geometry-attribute selection, only one of the geometry data and attribute data of the same layer is selected and sent or removed.

[0290] For example, refer to Figure 19 (a) to Figure 19 (c) When it is necessary to select a portion of the bitstream according to the implementation method, the transmitting device selects and transmits LoD1 (LoD0+R1) containing geometric data and attribute data, LoD1 (LoD0+R1) containing attribute data, and R2 containing geometric data, and removes R2 containing attribute data from the bitstream so that it is not transmitted. In other words, for attribute data, data other than the data at the upper layer (R2) is selected and transmitted. For geometric data, data from all layers (from level 0 (root level) to level 7 (leaf level) in the octree structure) is transmitted.

[0291] When it is necessary to select a portion of the bitstream according to the implementation method, it can be used Figure 18 (a) to Figure 18 Symmetric geometry of (c) - attribute selection method Figure 19 (a) to Figure 19 (c) The asymmetric geometry-attribute selection method or a combination of the symmetric geometry-attribute selection method and the asymmetric geometry-attribute selection method is used to select a portion of the bit stream.

[0292] The aforementioned bitstream fragmentation and selection of a portion of the bitstream are designed to support the scalability of point cloud data.

[0293] When point cloud data is represented in an octree structure and divided into LODs (or layers), scalable encoding / decoding (scalability) can be supported.

[0294] The scalability features, depending on the implementation, may include slice-level scalability and / or octree-level scalability.

[0295] According to the implementation, LoD can be used as a unit to indicate a set of one or more octree layers. Furthermore, LoD can refer to a bundle of octree layers to be configured on a slice-by-slice basis.

[0296] In addition to its meaning in attribute encoding / decoding, LOD can be used in a broader sense, depending on the implementation, such as a unit for detailed data partitioning.

[0297] In other words, spatial scalability can be provided for each octree layer (or scalable attribute layer). However, when scalability is configured at the slice level before bitstream parsing, selection can be performed at the LoD level.

[0298] For example, refer to Figure 13 In an octree structure, levels from the root level to level 4 correspond to LoD0, and levels from the root level to level 5 correspond to LoD1. Furthermore, levels from the root level to level 8 (i.e., the leaf level) correspond to LoD2.

[0299] In other words, in Figure 13 In the example, when scalability is used at the slice level, the provided scalability operations correspond to three steps: LoD0, LoD1, and LoD2, and the scalability operations provided in the decoding operation via the octree structure correspond to eight steps from the root level to the leaf level.

[0300] According to the implementation method, when LoD0 to LoD2 consist of corresponding slices, the transcoder of the receiver or transmitter (see...) Figure 11 You can select only LoD0, only LoD1, or LoD2 for scalable processing. Figure 13 In this context, LoD1 includes LoD0, and LOD2 includes both LoD1 and LoD2.

[0301] For example, when only LoD0 is selected, the maximum octree level is 4, and a scalable layer can be selected from octree level 0 to 4 during the decoding operation. In this case, the receiving device can treat the node size obtainable through the maximum octree level (or depth) as a leaf node, and can send that node size through signaling information.

[0302] For example, when LoD1 is selected, layer 5 can be added. Therefore, the maximum octree level can be 5, and a scalable layer can be selected from octree layers 0 to 5 during the decoding operation. In this case, the receiving device can treat the node size obtainable through the maximum octree level (or depth) as a leaf node, and can transmit this node size through signaling information. According to the implementation, the octree depth, octree layer, and octree level can be units into which data is divided in detail.

[0303] For example, when LoD2 is selected, layers 6 and 7 can be added. Therefore, the maximum octree level can be 7, and a scalable layer can be selected from octree layers 0 to 7 during the decoding operation. In this case, the receiving device can treat the node size available through the maximum octree level (or depth) as a leaf node and can transmit that node size via signaling information.

[0304] Figure 20 (a) to Figure 20 (c) illustrates an exemplary method for configuring a slice including point cloud data according to an implementation.

[0305] According to the implementation method / apparatus / encoder, the G-PCC bitstream can be configured by segmenting the bitstream according to a slice structure. A slice can be a data unit representing detailed data.

[0306] For example, one or more octree layers (or depths) can be matched with a slice.

[0307] The transmission method / apparatus (e.g., an encoder) according to the implementation can configure the bitstream based on slice 41001 by scanning the nodes (points) included in the octree in the direction of scan sequence 41000. A slice can include nodes at one or more levels of the octree structure, can include only nodes at a specific level, or can include only some nodes at a specific level. Alternatively, it can include only some nodes at one or more levels.

[0308] Figure 20 (a) illustrates an exemplary octree structure consisting of seven slices. In this example, slice 41002 may include nodes from level 0 to level 4, and slice 41003 may include some nodes of level 5. Slice 41004 may include some other nodes of level 5, and slice 41005 may include some other nodes of level 5. That is, in Figure 20 In (a), level 5 is divided into three slices. Similarly, in Figure 20 In (a), level 6 (i.e., the leaf level) is also divided into three slices. In other words, a slice can consist of a number of nodes at a specific level.

[0309] Figure 20 (b) illustrates an exemplary octree structure consisting of four slices. In this example, one slice includes nodes from level 0 to level 3 and some nodes from level 4, and another slice includes other nodes from level 4 and some nodes from level 5. Additionally, another slice includes other nodes from level 5 and some nodes from level 6, and yet another slice includes other nodes from level 6.

[0310] Figure 20 (c) illustrates an exemplary octree structure consisting of five slices. One slice consists of nodes from level 0 to level 3, and the four slices consist of nodes from level 4 to level 6. That is, a slice includes some nodes of level 4, some nodes of level 5, and some nodes of level 6. In other words, in levels 4 to 6, a slice may include some data of level 4 and data of level 5 or level 6 corresponding to the child nodes of the data.

[0311] In other words, such as Figure 20 (b) and Figure 20 As shown in (c), when multiple octree layers match a slice, only some nodes from each of the layers can be included in the slice. When multiple slices constitute a geometry / attribute frame in this manner, signaling information required by the receiving device to configure the layers can be transmitted to the receiving device. For example, the signaling information may include information about the layers included in each slice and information about the nodes included in each layer.

[0312] The encoder and the corresponding device according to the embodiment can encode point cloud data and generate and transmit a bit stream containing the encoded data and signaling information (or parameter information) related to the point cloud data.

[0313] Furthermore, when generating the bitstream, the implementation can be based on the bitstream structure (e.g., see [link to implementation details]). Figure 14 The Figure 20 The bitstream is generated by the receiving device, decoder, and corresponding device according to the embodiment. Therefore, the receiving device, decoder, and corresponding device can receive and parse the bitstream configured for selective decoding of some data, thereby decoding only a portion of the point cloud data and providing it efficiently.

[0314] Next, we will describe the scalable transmission of point cloud data.

[0315] The point cloud data transmission method / apparatus according to the embodiments can transmit bit streams containing point cloud data in a scalable manner, and the point cloud data reception method / apparatus according to the embodiments can receive bit streams and decode them in a scalable manner.

[0316] When have Figure 11 to Figure 20When a bitstream with the structure shown is used for scalable transmission, signaling information can be sent to the receiving device to select the desired slice. Scalable transmission does not necessarily mean sending or decoding the entire bitstream, but rather sending or decoding only a portion of the bitstream. Therefore, low-resolution point cloud data can be provided.

[0317] When scalable transmission is applied to an octree-based geometric bitstream according to the implementation method, it should be allowed to be based only on each octree layer up to the root node and leaf nodes. Figure 13 The information of a specific octree layer in the bitstream is used to configure point cloud data.

[0318] Therefore, the target octree layer should not have any dependency on information from lower octree layers. This can be a constraint applied to common geometric coding / attribute coding.

[0319] Additionally, in scalable transmission, a scalable structure needs to be sent to the receiving device for the transmitting / receiving device to select scalable layers. Considering the octree structure according to the implementation, all octree layers can support scalable transmission, or scalable transmission can be allowed only for specific octree layers and lower layers. For example, when some octree layers are included, signaling information can be passed to the receiving device to indicate the scalable layer including the slice. Therefore, the receiving device can determine whether the slice is necessary / unnecessary in the bitstream stage. Figure 20 In example (a), levels 0 (i.e., the root level) to 4 (41002) can form a scalable layer even without supporting scalable transmission, and lower octree levels can be matched to the scalable layer in a one-to-one correspondence. Typically, scalability can be supported for the portions corresponding to leaf nodes. For example... Figure 20 As shown in (c), when multiple octree layers are included in a slice, a scalable layer should be defined for each layer.

[0320] In this context, scalable transmission and scalable decoding can be used separately, depending on the purpose. According to one implementation, scalable transmission can be used so that the transmitting / receiving device can select information up to a specific layer without involving the decoder. According to another implementation, scalable decoding can be used to select a specific layer during encoding. That is, scalable transmission can support the selection of necessary information in a compressed state without involving the decoder (i.e., at the bitstream stage), allowing the transmitting or receiving device to determine a specific layer. On the other hand, in the case of scalable decoding, during encoding / decoding, encoding / decoding can be supported only for information up to the necessary portion. Therefore, scalable decoding can be used for operations such as scalable representation.

[0321] In this context, the layer configuration for scalable transmission can differ from the layer configuration for scalable decoding. For example, the lower three octree layers, including leaf nodes, can constitute a single layer in terms of scalable transmission. On the other hand, in terms of scalable decoding, when all layer information is included, scalable decoding can be enabled for each of the leaf node layer, leaf node layer-1, and leaf node layer-2.

[0322] Figure 21 (a) and Figure 21 (b) illustrates a geometric coding layer structure according to an implementation method. Specifically, Figure 21 (a) illustrates an example of three slices generated by the layer group structure at the encoder on the transmitter side, and Figure 21 (b) illustrates an example of the output of partial decoding using two slices at the decoder on the receiving side.

[0323] When fine-grained slicing (FGS) is enabled, a G-PCC bitstream can be sliced ​​into multiple sub-bitstreams. Here, fine-grained slicing can be referred to as layer-based slicing. To effectively utilize the layered structure of G-PCC, each slice can include encoded data from a portion of the coding layer or a portion of the region. By segmenting or splitting slices that are paired with the coding layer structure, scalable transmission or spatial random access use cases can be supported in an efficient manner.

[0324] Layer group based slice segment

[0325] In fine-grained slicing, each slice fragment can contain data encoded from a layer group defined below.

[0326] A layer group can be defined as a set of consecutive tree layers, wherein the start depth and end depth of the tree layer group can be any number in the tree depth, and the start depth can be less than the end depth. The order of data encoded in a slice fragment can be the same as the order of data encoded in a single slice. In this disclosure, a tree layer can be referred to as a tree level.

[0327] For example, consider as Figure 21 The geometric coding layer structure shown in (a) has eight coding layers, with three layer groups, each matching a different slice. More specifically, layer group 1 matches slice 1 for coding layers 0 through 4, layer group 2 matches slice 2 for coding layer 5, and layer group 3 matches slice 3 for coding layers 6 and 7. When the first two slices (i.e., slice 1 and slice 2) are sent or selected, the decoded output will be a portion of layers 0 through 5, as shown in (a). Figure 21 As shown in (b), partial decoding of the coding layer can be supported without accessing the entire bitstream by using slices in the layer group structure.

[0328] According to the embodiments, bitstream and point cloud data can be generated from slices based on coding layers. By slicing the bitstream at the ends of the coding layers during the encoding process, the method / apparatus according to the embodiments can select relevant slices, thereby supporting scalable transmission or partial decoding.

[0329] Figure 21 (a) illustrates a geometric coding layer structure with eight layers (also called levels), where each slice corresponds to a layer group. Layer group 1 includes coding layers 0 through 4. Layer group 2 includes coding layer 5. Layer group 3 is a group for coding layers 6 and 7. When the geometry (or attribute) has a tree structure comprising eight levels (depths), the bitstream can be configured hierarchically by grouping data corresponding to one or more levels (depths). Each group can be included in a slice.

[0330] Figure 21 (b) shows the decoded output when two slices are selected from three slices. When the decoder selects group 1 and group 2, it selects partial layers of the tree (depth) from 0 to 5. That is, partial decoding of the coding layers can be supported by using slices of the layer group structure, even without accessing the entire bitstream.

[0331] For the partial decoding process according to the embodiment, the encoder can generate three slices based on the layer group structure. The decoder according to the embodiment can select two slices from the three slices and perform partial decoding.

[0332] The bitstream according to an embodiment may include layer-based slices. Each slice may include a header containing signaling information related to the point cloud data (i.e., geometric data and / or attribute data) included in the slice. The receiving method / apparatus according to an embodiment may select slices and decode the point cloud data contained in the payload of a slice based on the header included in the selected slice.

[0333] In addition to the layer group structure, the method / apparatus according to the implementation can also divide the layer group into multiple subgroups, taking into account the use case of spatial random access. The subgroups are mutually exclusive, and the set of subgroups can be the same as the layer group. Since the points of each subgroup form the boundary in the spatial domain, the subgroup can be represented by subgroup bounding box information. Based on the spatial information, the layer group and subgroup structure can support access to the Region of Interest (ROI) by selecting slices that cover the ROI. By efficiently comparing the ROI with the bounding box information about each slice, spatial random access within a frame or tile can be supported.

[0334] like Figure 21 As shown in (a), the method / apparatus according to the embodiment can be configured to transmit point cloud data slices.

[0335] According to the implementation method, the entire encoded bitstream can be included in a single slice. For multiple slices, each slice can contain sub-bitstreams. The order of the slices can be the same as the order of the sub-bitstreams. Furthermore, each slice can match a layer group in a tree structure.

[0336] Furthermore, a slice may not affect the previous slice, just as a higher level in a geometric tree does not affect a lower level.

[0337] The segmented slicing method described in this implementation is efficient in terms of error robustness, effective transmission, and support for regions of interest.

[0338] 1) Error recovery

[0339] Compared to a single-slice structure, segmented slicing may be more robust to errors. In other words, when a slice contains the entire bitstream of a frame, data loss can affect the entire frame's data. On the other hand, when the bitstream is segmented into multiple slices, it is possible to decode at least one slice that is unaffected by the loss even if at least one slice is lost.

[0340] 2) Scalable transmission

[0341] This disclosure can support multiple decoders with different capabilities.

[0342] When encoded point cloud data (i.e., point cloud compression (PCC) bitstream) is contained in a single slice, the Level of Detail (LOD) of the encoded point cloud data can be determined before encoding. Therefore, multiple pre-coded bitstreams of point cloud data with different resolutions can be sent independently, which may be inefficient in terms of large bandwidth or storage space.

[0343] When encoded point cloud data (i.e., PCC bitstream) is contained in segmented slices, a single bitstream can support different levels of decoders. From the decoder's perspective, the receiving device can select the target layer and can pass a partially selected bitstream to the decoder. Similarly, by using a single bitstream without splitting the entire bitstream, the transmitting device can efficiently generate partial bitstreams.

[0344] 3) Region-based spatial scalability

[0345] In terms of the G-PCC requirements according to the implementation method, region-based spatial scalability can be defined as follows: the compressed bitstream consists of one or more layers, so a particular ROI can have a higher density with additional layers, and layers can be predicted from lower layers.

[0346] To support this requirement, it is necessary to support region-by-region detailed representations. For example, in VR / AR applications, distant objects can be represented with lower precision, while closer objects can be represented with higher precision. Furthermore, the decoder can increase the resolution of the region of interest upon request. This operation can be implemented using geometric octrees and scalable structures of G-PCC (e.g., scalable attribute encoding schemes).

[0347] According to one implementation, the decoder should access the entire bitstream based on the current slice structure containing the entire geometry or attributes, which may lead to inefficiencies in bandwidth, memory, and decoder performance. On the other hand, when the bitstream is segmented into multiple slices and each slice contains sub-bitstreams according to scalable layers, the decoder according to one implementation can efficiently select slices as needed before parsing the bitstream.

[0348] The method / apparatus according to the implementation can use a tree structure (or layer structure) of point cloud data to generate layer groups.

[0349] Reference Figure 21 As an example, in (a), there are eight layers (also called levels) in a geometrically encoded layer structure (e.g., an octree structure), and three slices can be used to contain one or more layers. A group represents a set of layers. When using scalable attribute encoding, the tree structure is the same as the geometric tree structure. The same octree slice mapping can be used to create attribute slice fragments.

[0350] According to the implementation method, the layer group represents a bundle of layer structure units (e.g., an octree layer or a LoD layer) generated in G-PCC encoding.

[0351] A subgroup can be represented as a set of neighboring nodes within a layer group. For example, a subgroup can be configured as a set of nodes that are adjacent to each other in Morton code order, a set of neighboring nodes based on distance, or a set of neighboring nodes based on coding order. Nodes in a parent-child relationship can also exist within a subgroup.

[0352] When defining subgroups, the boundary appears in the middle of the layer, and parameters such as `entropy_continuation_enabled_flag` can be signaled to indicate whether entropy continuation should be maintained at the boundary. Continuation can also be maintained by referencing the previous slice using `ref_slice_id`.

[0353] The tree structure according to the implementation can be an octree structure, and the attribute layer structure or attribute encoding tree according to the implementation can include a level of detail (LOD) structure. In other words, the tree structure for point cloud data can include layers corresponding to depth or level, and the layers can be grouped.

[0354] The method / apparatus according to the implementation (e.g.,Figure 3 Octree analyzer 30002 or LOD generator 30009 Figure 7 The octree synthesizer 7001 or LOD generator 7008 can generate octree structures of geometry or LOD tree structures of attributes. Furthermore, point cloud data can be grouped based on layers of the tree structure.

[0355] Reference Figure 21 (a) Multiple layers (also called levels) are divided into first to third groups (also called layer groups). Groups can be further divided to form subgroups.

[0356] According to the implementation, a slice may contain encoded data from a layer group. Here, a layer group is defined as a set of consecutive tree layers. The start depth and end depth of a tree layer can be specific numbers in the tree depth, where the number for the start is less than the number for the end.

[0357] Although Figure 21 (a) illustrates a geometric coding layer structure as an example of a tree structure, but coding layer structures for attributes can also be created in a similar manner.

[0358] Figure 22 This is a diagram illustrating the layer and subgroup structure according to the implementation method.

[0359] Reference Figure 22 Point cloud data and bit streams can be distinguished and represented by bounding boxes.

[0360] exist Figure 22 The diagram illustrates the subgroup structure and the corresponding bounding boxes for each subgroup. Layer group 2 is divided into two subgroups (group 2-1 and group 2-2) contained in different slices, and layer group 3 is divided into four subgroups (group 3-1, group 3-2, group 3-3, and group 3-4) contained in different slices. Given the slice and bounding box information for the layer groups and subgroups, spatial access can be performed by: 1) comparing the bounding box of each slice with the ROI, 2) selecting slices where the subgroup bounding boxes overlap with the ROIs, and 3) decoding the selected slices.

[0361] When considering the ROI in region 3-3, slices 1, 3, and 6 are selected as layer group 1 covering the ROI, and the subgroup bounding boxes of subgroups 2-2 and 3-3. For efficient spatial access, it is assumed that there are no dependencies between subgroups from the same layer group. In real-time streaming or low-latency use cases, time efficiency can be improved by performing selection and decoding upon receiving each slice fragment.

[0362] When encoding geometry and / or attributes, the method / apparatus according to the implementation can present the data as a tree 2200 consisting of layers (which may be referred to as depth, level, etc.). Point cloud data corresponding to layers (depth / level) can be grouped into layer groups (or groups) 45000. Layer group 2 can be further divided (segmented) into two subgroups 45002, and layer group 3 can be further divided (segmented) into four subgroups 45003. Each subgroup can be configured as a slice to generate a bitstream.

[0363] The receiving apparatus according to the embodiment can receive a bitstream, select a specific slice from the bitstream, and decode the bounding boxes corresponding to the subgroups included in the selected slice. For example, when slice 1 is selected, the bounding box 45004 corresponding to layer group 1 can be decoded. Layer group 1 may be data corresponding to the largest region. When additionally displaying detailed regions for layer group 1, the method / apparatus according to the embodiment can select slice 3 and / or slice 6, and access the bounding boxes (point cloud data) of subgroups 2-2 and / or subgroups 3-3 of detailed regions included in the region of layer group 1 in a layered and partial manner.

[0364] use Figure 22 The encoding and decoding of point cloud data of layers and subgroups can be performed by Figure 1 Transmitting / receiving devices Figure 2 Encoding and decoding, Figure 3 Transmission device / method Figure 7 Receiving device / method Figure 8 and Figure 9 Transmitting / receiving device / method Figure 10 The device Figure 24 , Figure 25 or Figure 27 Sending / receiving methods Figure 28 Transmitting / receiving devices and Figure 40 and Figure 41 The send / receive methods are executed.

[0365] Figure 23 (a) to Figure 23 (c) illustrates a representation of layer-based point cloud data according to an implementation method.

[0366] The apparatus / method according to the embodiments can provide efficient access to large-scale or dense point cloud data through layer slicing based on scalability and spatial access capabilities. Due to the large number of points and the large data size, point cloud data can take a significant amount of time to render or display content. Therefore, as an alternative approach, the level of detail can be adjusted based on the viewer's interest. For example, structural or global region information is more important than local details when the viewer moves away from the scene or object. On the other hand, detailed information about the ROI is needed when the viewer moves closer to a specific region or object. Using an adaptive approach, the renderer according to the embodiments can efficiently provide the viewer with data of sufficient quality. Figure 23 (a) to Figure 23 (c) illustrates an example of increased detail across three viewing distance levels based on changes in ROI.

[0367] Figure 23 The high-level view in (a) shows coarse details. Figure 23 The medium-level view in (b) shows medium-level details, and Figure 23 The low-level view in (c) shows fine-grained details.

[0368] Figure 24 An example of a point cloud data transmission / reception apparatus / method according to an embodiment is shown.

[0369] When layer group slices are used to generate G-PCC bitstreams, multi-resolution ROIs can be supported.

[0370] Reference Figure 24 Multi-resolution ROIs can be supported through the scalability and spatial accessibility of layered tiling. Figure 24 In the transmitting side, encoder 47001 can generate bitstream slices of spatial subgroups or octree layer groups for each layer group. Upon request, slices matching the ROI at each resolution are selected and sent to the receiving side. Compared to a tile-based method, the overall bitstream size is reduced because it does not include details beyond the requested ROI. On the receiving side, decoder 47004 can combine slices to produce three outputs, for example: 1) a high-level view output from the layer group; 2) a mid-level view output from selected subgroups of layer groups 1 and 2; and 3) a low-level view output with good quality detail from selected subgroups of layer groups 2 and 3, and layer group 1. The outputs can be generated progressively, allowing the receiver to provide a viewing experience such as scaling, which progressively increases the resolution from high-level to low-level views.

[0371] Encoder 47001 may correspond to a geometric encoder and / or an attribute encoder as a point cloud encoder according to an embodiment. The encoder may slice the point cloud data based on layer groups (or subgroups). Layers may be referred to as the depth of a tree, the level of LOD, etc. As shown in section 47002, the depth of the geometric octree and / or the level of the attribute layer may be divided into layer groups (or subgroups).

[0372] The slice selector 47003, in conjunction with the encoder 47001, can select segmented slices (or sub-slices) and selectively and partially transmit data such as layer group 1 to layer group 3.

[0373] Decoder 47004 can decode point cloud data that is selectively and partially transmitted. For example, decoder 47004 can decode layer group 1 (which has a high depth / layer / level or index 0, or is close to the root) for a high-level view. Furthermore, decoding can be performed for a mid-level view by individually incrementing the depth / level index on layer group 1 based on layer group 1 and layer group 2. Additionally, decoding can be performed based on layer groups 1 through 3 for a low-level view.

[0374] Reference Figure 24 The encoder 47001 according to the embodiment can receive point cloud data as input and slice it into layer groups. That is, the point cloud data can be structured hierarchically and divided into layer groups. The hierarchical structure can refer to an octree structure or a level of detail (LoD). Part 47002 represents the point cloud data divided into layer groups. The slice selector 47003 can select a layer group (or corresponding slice) and send the selected slice to the decoder 47004 on the receiving side. The decoder 47004 can reconstruct only layer group 1, reconstruct layer group 1 and layer group 2, or reconstruct all received layer groups by combining the received slices according to the user's needs. The layer groups are hierarchical and have different levels of detail. Reconstructing only layer group 1 can result in a wider range of reconstruction and coarse detail. Reconstructing all layer groups 1 to 3 can result in a narrower range of reconstruction and fine detail.

[0375] As described above, the input to the encoder used for layer group slicing consists of point cloud data and parameter information (e.g., Sequence Parameter Set (SPS), Geometric Parameter Set (GPS), and Layer Group Slice List or Layer Group Structure List (LGSI)), which describes the structural information about the layer group slices. At the beginning of each tree depth, the layer group is determined using the layer group structure parameters to determine the layer group for the target tree depth. When using the layer group index, the subgroup bounding box is used to determine the subgroup index for each node. When a node's subgroup changes, the context state and buffer used in the previous subgroup encoder are stored, and the context state and buffer of the current subgroup encoder are loaded. By using a separate encoder for each subgroup, the context state is allowed to persist within the subgroup. Furthermore, to restrict neighboring nodes from belonging to the same subgroup as the current node, the geometric occupancy atlas is updated by considering both subgroup boundaries and atlas boundaries. Both methods enable each encoded bitstream to be decoded independently by the decoder without needing node information from neighboring subgroups. This process is performed recursively for all nodes across all tree depths. When the end of the node at the target tree depth is reached, a fine-grained slice is generated that matches the subgroups of each layer group one-to-one. In this disclosure, the fine-grained slice may be referred to as a layer group-based slice.

[0376] The encoder's encoding operation can be performed by Figure 1 The transmitting device Figure 2 encoding, Figure 3 Transmission device / method Figure 8 and Figure 9 Transmission device / method Figure 10 The device Figure 25 Encoding methods Figure 27 The transmitting device or Figure 40 It shall be performed by at least one or a combination of the sending methods.

[0377] The decoding operation of the decoder can be performed by Figure 1 The receiving device Figure 2 Decoding Figure 7 Receiving device / method [[ and ​ Receiving device / method ​ The device ​ Decoding methods ​ The receiving device or ​ It shall be performed by at least one or a combination of the receiving methods.

[0378] ​ This is a flowchart illustrating an example of an encoding method of an encoder according to an implementation method. That is, ​ An example of an encoding process for layer group slicing is illustrated, which enables the encoded bitstream to be decoded independently by the decoder without the need for node information from neighboring subgroups. ​The encoding method can be determined by ​ The transmitting device ​ encoding, ​ Transmission device / method ​ Transmission device / method ​ The device ​ The transmitting device or a combination thereof is used to perform this action.

[0379] The encoder, according to the implementation method, generates parameter information such as SPS, GPS, and LGSI, and determines layer groups and subgroups (S2501, S2502). Then, it checks whether the subgroup has changed (S2503), and whenever the subgroup changes, it stores information for encoding the point cloud data within the subgroup and loads necessary information to perform efficient encoding (S2504). For example, context information can be stored and then loaded for use during the next encoding. That is, when a node's subgroup changes, the context state and buffer used by the previous subgroup encoder are stored, and the context state and buffer of the current subgroup encoder are loaded. Furthermore, the geometric occupancy atlas is updated according to the encoding (S2505). Specifically, to restrict neighboring nodes from belonging to the same subgroup as the current node, the geometric occupancy atlas is updated by considering subgroup boundaries and atlas boundaries. Then, the nodes in the subgroup are encoded (S2506). When this process is repeated and all nodes occupying all depths within the tree are encoded (S2507, S2508), that is, when the end of the node at the target tree depth is reached, a geometric data unit header is generated, and a geometric bitstream containing parameter information, the geometric data unit header, geometric data units, etc., is generated. In other words, fine-grained slices that match the subgroups of each layer group one-to-one are produced. In this disclosure, the terms geometric occupancy atlas, geometric atlas, and atlas can be used interchangeably with the same meaning.

[0380] The following is a detailed description of the operations (S2501 to S2504) used to determine the layer groups and subgroups and to store and load information (layer group and subgroup determination).

[0381] As mentioned in the encoder process above, storing and loading the encoder state is necessary to ensure independent decoding of each subgroup. To provide flexible subgroup partitioning, this process is performed for each node in the tree layer.

[0382] For each tree level, a level group is determined, and this level group has a fixed depth for all trees within it. Since a level group is a collection of consecutive tree levels, the level group index changes at the beginning of the level group. Subgroups are determined based on the determined level groups. Since a subgroup is a group of nodes defined by its bounding box, a node's subgroup is found by comparing its position to the subgroup bounding box. Whenever a subgroup or level group changes, the encoder state of the previous subgroup is stored for later use, and the encoder state of the current subgroup is loaded for continuous encoding.

[0383] The pseudocode below is an example of the encoding process for each level, level group, and subgroup of a tree structure, including the process of storing and loading the encoder state whenever each level group and subgroup changes. That is, when the tree depth is 0, the first level group and subgroup are initialized, and the current encoder state is loaded. When the tree depth reaches the level number in the current level group, the process moves to the next level group, and the encoder state is stored and loaded. This process processes the nodes within each level group, storing and loading the encoder state whenever a subgroup changes.

[0384] pseudocode: for (depth = 0; depth < maxDepth; depth++) { / / Determine the layer group index if (depth == 0) { curLayerGroupId = 0; curSubgroupId = 0; Load current context state; sum_layers = numLayersPerLayerGroup[curLayerGroupId]; } else if (depth == sum_layers) { prevLayerGroupId = curLayerGroupId++; prevSubgroupId = curSubgroupId; curSubgroupId = 0; Save the previous encoder state; Load current encoder state; sum_layers += numLayersPerLayerGroup[curLayerGroupId]; } else if (numSubgroupsMinus1[curLayerGroupId] > 0) { prevSubgroupId = curSubgroupId; curSubgroupId = 0; Save the previous encoder state; Reload current encoder state; } for (all nodes in curLayerGroupId) { / / Determine subgroup index if (!(nodePos >= bbox_min && nodePos < bbox_max)) { for (i = 0; i <= numSubgroupsMinus1[curLayerGroupId]; ++) { if (nodePos >= bbox_min[i] && nodePos < bbox_max[i]) { prevSubgroupId = curSubgroupId; curSubgroupId = i; / / Save and load the encoder Save the previous encoder state; if (first node of the current subgroup) Load reference encoder state. else Reload current encoder state; break }}}} } The following is a detailed description of the geometry occupancy atlas update operation (S2505).

[0385] In the MortonMap3D class, `_maxRange` and `_minRange` are defined to account for subgroup boundaries within the geometric occupancy atlas. When the geometric occupancy atlas is within a subgroup, the minimum and maximum ranges are initially set to 0 and the length of the cube edge, respectively. When the minimum boundary of the subgroup is greater than the minimum of the geometric occupancy atlas, `_minRange` is set to the minimum of the subgroup boundary. Additionally, when the maximum boundary of the subgroup is less than the maximum of the geometric occupancy atlas, `_maxRange` is set to the maximum of the subgroup boundary. By utilizing these ranges (`_maxRange` and `_minRange`), the portion of the atlas that overlaps with the subgroup bounding box is considered active, and nodes within this active region are used as neighbors. This ensures that the FGS (i.e., layer-group-based slices) can be decoded by the receiver's decoder without requiring node information from neighboring subgroups.

[0386] The pseudocode below illustrates an example of the subgroup boundary process when updating the geometry-occupied atlas described above. Specifically, the pseudocode shows the process of calculating the range used to set the subgroup boundaries in the MortonMap3D class. The setRange method calculates the minimum and maximum boundaries of the subgroup and assigns them to the variables _minRange and _maxRange. This range is calculated for each axis in 3D space. _maxRange[m] represents the maximum range of the subgroup along the m-th axis, set by taking into account the atlas origin and the cube size. _minRange[m] represents the minimum range of the subgroup along the m-th axis; this value is set if it is greater than the atlas origin, otherwise it is set to 0. Through this process, the subgroup is positioned within a specific portion of the atlas, and the portion of the atlas that overlaps with the subgroup bounding box can be considered the active area, thus enabling efficient data processing.

[0387] pseudocode: class MortonMap3D { setRange() { for (m = 0; m < 3; m++) { / / _maxRange if (bboxMax < atlasOrigin + _cubeSize) _maxRange[m] = bboxMax - atlasOrigin; Else _maxRange[m] = _cubeSize; / / _minRange if (bboxMin > atlasOrigin) _minRange[m] = bboxMin - atlasOrigin; Else _minRange[m] = 0; }} } These operations update the range of the geometric occupancy atlas used for geometric encoding by taking subgroup boundaries into account. The geometric occupancy atlas is a lookup table (LUT) created for a specific range of neighboring nodes to improve the speed of tasks such as geometric neighbor searches. If the current node's location falls outside the range of the geometric occupancy atlas, a new update must be performed.

[0388] During encoder and decoder operations, if a subgroup boundary exists within the atlas boundary, the atlas boundary (_maxRange, _minRange) is updated to match the subgroup boundary. This allows the boundary to be modified and utilized only when the subgroups within the atlas change, without requiring a complete atlas update.

[0389] Here, `_maxRange` and `_minRange` represent the minimum and maximum actual usable range within the atlas. `atlasOrigin` and `cubeSize` represent the starting position and size of the geometry occupying the atlas, respectively. Furthermore, `bboxMin` and `_bboxMax` represent the minimum and maximum bounding box positions for specific layer groups and subgroups.

[0390] ​ This is a flowchart illustrating an example of a decoding method of a decoder according to an implementation method. That is, ​ A decoder process for layer group slicing according to an embodiment is illustrated.

[0391] ​ You can follow ​ Reverse processing.

[0392] ​ The decoding method can be determined by ​ The receiving device ​ Decoding ​ Receiving device / method ​ Receiving device / method ​ The device ​ The decoder or a combination thereof is used to execute.

[0393] According to the implementation, the decoder parses parameter information (e.g., SPS, GPS, LGSI, etc.), geometric data unit headers, and geometric data units from the received geometric bitstream. The decoding process for the layer group slice reference SW of the first fine-grained slice (FGS) is the same as that for conventional geometric slice decoding (parameter set parsing, data unit header parsing, and data unit decoding). In this disclosure, parameter information, parameters, and parameter sets can be used interchangeably with the same meaning.

[0394] After the parsing process is executed, when layer group slicing is enabled (S2601), the next relevant geometric data unit is considered to be the FGS of the first slice (i.e., if they have the same Slice_id). Considering the context reference and node inheritance between parent (i.e., higher) child groups and sub-child groups, it is assumed that the order of FGS is sorted in ascending order according to layer_group_id and subgroup_id.

[0395] Before decoding the relevant data unit, the context state, output node, and layer group parameters of the previous slice are stored in a buffer for use by the next slice (S2602). That is, the buffer and layer group parameters are updated. After parsing the header of the relevant data unit (S2603), the parent-child group of the current subgroup is detected (S2604). According to the implementation, the parent-child group of the current subgroup is detected by finding a subgroup whose subgroup bounding box is a superset of the current subgroup bounding box. Once the parent-child group is determined, the parent (i.e., higher) node of the current relevant data unit is selected (S2605). When the parent (i.e., higher) node of the current relevant data unit is selected in operation S2605, the relevant data unit is parsed (S2606). Then, by using the selected node as the initial node of the decoding process, the current relevant data unit is decoded to the tree layer covered by the current layer group. The decoding process of the relevant data unit is repeated until the geometric bitstream ends (S2607). When the decoding of the relevant data unit is completed, the output point cloud is generated and the decoding process ends (S2608).

[0396] The following describes the additional process for finding parent-child groups and parent nodes in more detail.

[0397] The decoder according to the implementation parses parameter information such as SPS, GPS, and LGSI contained in the bitstream. It parses signaling information in the header of data units contained in slices such as FGS. Based on the header information, it parses the point cloud data contained in the data units. When a layer group slice is activated, the decoder updates the buffer and layer group parameters, and parses the header information of data units related to higher-level data units. It detects parent-child groups and selects a parent node. Then it parses the relevant data units. The following is a detailed description of the parent-child group detection operation (S2604).

[0398] When decoding fine-grained slices (i.e., layer-group-based slices), nodes from parent-child groups are used as inputs to child-subgroups to provide continuous decoding at layer group boundaries. This can be derived using parent-child spatial relationships due to the hierarchical structure of layer-group slices. That is, a child-subgroup is a subset of a parent-child group, where the bounding box of a child-subgroup spatially excludes the bounding boxes of other child-subgroups within the same layer group.

[0399] Based on this relationship, slices with parent-child groups can be detected by finding the spatial superset of the current slice. By utilizing the subgroup_bbox_origin and subgroup_bbox_size signaled in the data cell header, parent-child groups can be identified by comparing this information with the bounding box information associated with the subgroup at the previous layer level.

[0400] The pseudocode below is an example of the parent-child group detection process described above.

[0401] pseudocode: parentLayerGroup = curLayerGroup - 1; for (i = 0; i < numSubgroups[parentLayerGroup]; i++) { if (_bboxMin[parentLayerGroup][i] <= curBboxMin && _bboxMax[parentLayerGroup][i] > curBboxMin) { parentSubgroup = i; break } } In other words, as described above, this disclosure enables the inference of parent-child groups based on spatial relationships between them without additional signaling.

[0402] From the encoder's perspective, a sub-subgroup can be defined by dividing the bounding boxes of the parent and child groups. Since a sub-subgroup is a child region of the parent and child group, the encoder is allowed to infer the parent and child group index using the method described above.

[0403] From the decoder's perspective, if the bounding box of the parent-child group is a superset of the bounding box of the child-child group, then the corresponding child group index is determined as the parent-child group index.

[0404] In the pseudocode above, parentLayerGroup is the index of the higher-level group associated with the current layer group.

[0405] `parentSubgroup` is the subgroup index of subgroups that have a parent-child relationship with the current subgroup.

[0406] curLayerGroup is the current layer group.

[0407] numSubgroups is the number of subgroups belonging to the layer group.

[0408] _bboxMin and _bboxMax are the minimum and maximum values ​​of the bounding box position for a specific layer group and subgroup.

[0409] curBboxMin is the minimum bounding box position of the currently coded subgroup.

[0410] The following is a detailed description of the input parent node selection operation (S2605).

[0411] When decoding a relevant slice, the output nodes of the parent-child group are used as inputs for decoding the child-child group. If the child bounding boxes of the parent-child group and the child-child group are the same, all nodes generated in the parent-child group are used. Conversely, if the child bounding box of the child-child group is a subset of the parent-child group, the decoder selects the actual parent node. To identify the parent node, this disclosure compares each node of the parent-child group with the bounding box boundary of the child-child group.

[0412] The pseudocode below is an example of the parent node selection process described above. That is, only nodes within a specific range (bbox_min and bbox_max) are selected as parent nodes. For example, a node is added as a parent node only if its position (node.Pos) is greater than or equal to the minimum value (bbox_min) and less than the maximum value (bbox_max) of the bounding box.

[0413] pseudocode: for (node ​​= inNodes.begin(); node != inNodes.end(); node++) { if (node.Pos >= bbox_min && node.Pos < bbox_max) fifo.emplace_back(node); else continue? } ​ Another example of a point cloud transmitting apparatus according to an embodiment is shown. ​ The elements of the transmitting device shown can be implemented by hardware, software, at least one processor connected to at least one memory, and / or a combination thereof. That is, although ​ Not shown in the image. ​ The elements of the transmitting device can be implemented by hardware, software, firmware, or a combination thereof, and may include one or more processors or integrated circuits configured to communicate with one or more memories. One or more processors can perform the above-described... ​At least one of the operation and / or functions of the elements of the transmitting device. One or more processors can perform operations for executing... ​ The software program and / or instruction set for the operation and / or function of the elements of the transmitting device. ​ In this process, the order in which the boxes are executed can be changed, some boxes can be omitted, and some boxes can be added.

[0414] According to an implementation, the point cloud transmitting device may include a data input unit 51001, a signaling processor 51002, a geometric encoder 51003, an attribute encoder 51004, and a transmission processor 51005.

[0415] The geometry encoder 51003 and the attribute encoder 51004 can perform operations with ​ Point cloud video encoder 10002 ​ The code 20001 ​ Point cloud video encoder, ​ Point cloud video encoder, ​ encoding and ​ The encoding-related description includes some or all of the operations.

[0416] According to the embodiment, the data input unit 51001 receives or acquires point cloud data. The data input unit 51001 can perform... ​ Point cloud video acquisition unit 10001 or ​ Some or all of the operations in the data input unit 8000.

[0417] The data input unit 51001 outputs the positions of points in the point cloud data to the geometry encoder 51003 and the attributes of the points to the attribute encoder 51004. Additionally, it outputs parameters to the signaling processor 51002. According to an embodiment, the parameters can be provided to both the geometry encoder 51003 and the attribute encoder 51004.

[0418] The geometric encoder 51003 performs geometric compression on a layer-group basis using the positions of the input points. The geometric encoder 51003 performs entropy encoding on the compressed geometric information and outputs the encoded information to the transmission processor 51005 in the form of a geometric bit stream.

[0419] The geometry encoder 51003 reconstructs geometric information based on the position changed by compression and outputs the reconstructed (or decoded) geometric information to the attribute encoder 51004.

[0420] According to the implementation, the geometric encoder 51003 uses the positions of input points to construct an octree, performs layer-based slicing in the octree, selects one or more slices, and then compresses the geometric information associated with the selected one or more slices. (See also...) ​ The layer-group-based slicing and slice-by-slice geometric compression according to the implementation method have been described in detail, so their description will be omitted below to avoid redundancy.

[0421] The attribute encoder 51004 compresses attribute information based on positional and / or reconstructed geometric information that has not yet undergone geometric encoding. In one implementation, the attribute information may be encoded using any one or a combination of RAHT coding, LOD-based predictive transform coding, and lifting transform coding. The attribute encoder 51004 performs entropy coding on the compressed attribute information and outputs the information to the transmission processor 51005 as an attribute bitstream.

[0422] Signaling processor 51002 generates and / or processes signaling information required for encoding / decoding / rendering geometric and attribute information, and provides it to geometry encoder 51003, attribute encoder 51004, and / or transmission processor 51005. Alternatively, signaling processor 51002 may receive signaling information generated by geometry encoder 51003, attribute encoder 51004, and / or transmission processor 51005. Signaling processor 51002 may provide information fed back from receiving device (e.g., head orientation information and / or viewport information) to geometry encoder 51003, attribute encoder 51004, and / or transmission processor 51005.

[0423] In this disclosure, signaling information including slice-related information based on layer groups (Layer Group Structure List (LGSI)) can be signaled and transmitted in units of parameter sets (SPS: Sequence Parameter Set, GPS: Geometric Parameter Set, APS: Attribute Parameter Set, TPS: Patch Parameter Set (also known as Patch List) etc.) and / or data units (i.e., slices). That is, signaling can be signaled and transmitted in units of coding units (or compression units or prediction units) (such as slices or patches) for each image.

[0424] The transmission processor 51005 can perform operations with ​ The operation and / or transmission method of the transmission processor 8012 are the same as or similar to the operation and / or transmission method of the transmission processor 8012, or perform the same as... ​ The operation and / or transmission method of transmitter 10003 are the same as or similar to those of transmitter 10003. Details are omitted below; please refer to [reference needed]. ​ or ​ The description.

[0425] The transmission processor 51005 can multiplex the geometric bitstream output from the geometric encoder 51003, the attribute bitstream output from the attribute encoder 51004, and the signaling bitstream output from the signaling processor 51002 into a single bitstream and transmit the bitstream, or it can encapsulate the bitstream into a file or fragment for transmission. In one embodiment of this disclosure, the file may be in ISOBMFF file format.

[0426] According to the embodiments, files or fragments can be sent to a receiving device or stored in a digital storage medium (e.g., USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.). The transmission processor 51005 according to the embodiments is capable of wired / wireless communication with the receiving device via a 4G, 5G, or 6G network. Furthermore, the transmission processor 51005 can perform necessary data processing operations according to the network system (e.g., a 4G, 5G, or 6G communication network system). In addition, the transmission processor 51005 can transmit encapsulated data on demand.

[0427] ​ Another example of a point cloud receiving device according to an embodiment is illustrated. ​ The elements of the receiving device shown can be implemented by hardware, software, at least one processor connected to at least one memory, and / or a combination thereof. That is, although ​ Not shown in the image. ​ The elements of the receiving device can be implemented by hardware, software, firmware, or a combination thereof, and may include one or more processors or integrated circuits configured to communicate with one or more memories. One or more processors can perform the above-described... ​ At least one of the operation and / or functions of the elements of the receiving device. One or more processors can perform operations for executing... ​ The software program and / or instruction set for the operation and / or function of the receiving device's components. ​ In this process, the order in which the boxes are executed can be changed, some boxes can be omitted, and some boxes can be added.

[0428] According to an implementation, the point cloud receiving device may include a receiving processor 61001, a signaling processor 61002, a geometry decoder 61003, an attribute decoder 61004, a geometry buffer 61005, an attribute buffer 61006, and a post-processor 61007.

[0429] The receiving processor 61001 according to the embodiment can receive a single bit stream, or it can receive a geometric bit stream, an attribute bit stream, and a signaling bit stream separately. When a file and / or fragment is received, the receiving processor 61001 according to the embodiment can decapsulate the received file and / or fragment and output a bit stream for it.

[0430] When a single bitstream is received (or decapsulated), the receiving processor 61001 according to the embodiment can demultiplex the geometric bitstream, attribute bitstream and / or signaling bitstream from the single bitstream, and output the demultiplexed signaling bitstream to the signaling processor 61002, the geometric bitstream to the geometric decoder 61003, and the attribute bitstream to the attribute decoder 61004.

[0431] When the geometry bit stream, attribute bit stream and / or signaling bit stream are received (or decapsulated) respectively, the receiving processor 61001 according to the embodiment can deliver the signaling bit stream to the signaling processor 61002, the geometry bit stream to the geometry decoder 61003, and the attribute bit stream to the attribute decoder 61004.

[0432] According to the embodiment, the receiving processor 61001 can distinguish between geometric bitstreams and attribute bitstreams in units of slices, layers, or subgroups based on signaling information processed by the signaling processor 61002. It can deliver geometric bitstreams in units of slices / layers / subgroups to the geometric decoder 61003, and attribute bitstreams in units of slices / layers / subgroups to the attribute decoder 61004. For example, the input to the geometric decoder 61003 may include fine-grained slice bitstreams (or layer-based slice bitstreams) and layer structure.

[0433] The signaling processor 61002 can parse and process information included in signaling information (e.g., SPS, GPS, APS, TPS, LGSI, and metadata) from the input signaling bitstream, and can provide this information to the receiving processor 61001, the geometry decoder 61003, the attribute decoder 61004, the geometry buffer 61005, the attribute buffer 61006, and the post-processor 61007. In another embodiment, the signaling processor 61002 can also pre-parse the signaling information contained in the geometry data unit header and / or the attribute data unit header before decoding the corresponding slice (or subgroup) data. In this disclosure, the geometry buffer 61005 can store nodes belonging to the current subgroup and / or nodes belonging to the parent-child groups of the current subgroup for geometry decoding. Furthermore, before decoding the relevant data units, the context state, output nodes, and layer group parameters of the previous slice are stored in the geometry buffer 61005 for use by the next slice (S2602). In addition, the attribute buffer 61006 may include nodes belonging to the current subgroup and / or nodes belonging to the parent-child groups of the current subgroup for attribute decoding.

[0434] According to the implementation, the signaling processor 61002 can also parse and process the information that signals notifications in the sequence parameter set, geometric parameter set and / or geometric data unit header (e.g., information related to slices based on layer groups), and provide the processed information to the geometric decoder 61003 and the geometric buffer 61005.

[0435] According to the implementation, the signaling processor 61002 can also parse and process the information that signals notifications in the sequence parameter set, attribute parameter set and / or attribute data unit header (e.g., information related to slices based on layer groups), and provide the processed information to the attribute decoder 61004 and the attribute buffer 61006.

[0436] According to an implementation, the geometry decoder 61003 can perform operations on the compressed geometry bitstream based on signaling information (e.g., slice-related information based on layer groups). ​ The reverse processing of the operation of the geometry encoder 51003 is used to reconstruct the geometry. (This has been referenced...) ​ and ​ The geometry decoding performed by the geometry decoder 61003 when layer group slicing is enabled is described in detail, so its description is omitted below to avoid redundancy.

[0437] The geometric information recovered (or reconstructed) by the geometry decoder 61003 is provided to the attribute decoder 61004.

[0438] The attribute decoder 61004 performs operations on the compressed attribute bitstream based on signaling information and reconstructed geometric information. ​ The reverse processing of the operation of the attribute encoder 51004 is used to restore the attribute.

[0439] According to the implementation, the attribute decoder 61004 can perform LoD generation and NN search on a subgroup basis.

[0440] According to the implementation, the post-processor 61007 can reconstruct and display / render point cloud data by matching the geometric information (i.e., position) reconstructed and output by the geometry decoder 61003 with the attribute information reconstructed and output by the attribute decoder 61004.

[0441] The slice structure for layer configuration and the signaling method for scalable transmission described above will be described in detail below.

[0442] ​ A bitstream configuration according to an implementation method is illustrated.

[0443] The method / apparatus according to the implementation method can generate, as follows: ​ The bitstream shown can contain encoded geometric data and attribute information, as well as parameter information.

[0444] The syntax and semantics related to parameter information are as follows.

[0445] Information regarding the segmentation, according to the implementation method, can be defined in the parameter set and SEI message of the bitstream as follows.

[0446] According to the implementation, a bitstream may contain a sequence parameter set, a geometric parameter set, an attribute parameter set, a geometric slice header (also called a geometric data unit header), and an attribute slice header (also called an attribute data unit header). Depending on the application or system, these can be defined at corresponding or separate locations, resulting in different scopes and application methods. That is, they can have different meanings depending on the location where the signal is transmitted. When defined in SPS, it can be consistently applied to the entire sequence. When defined in GPS, it can be used for geometric reconstruction. When defined in APS, it can be used for attribute reconstruction. When defined in TPS, the signaling can be applied only to points within a tile. When delivered by tile, the signaling can be applied only to that tile. Depending on the application or system, these can be defined at corresponding or separate locations, resulting in different scopes and application methods. Additionally, the syntax elements defined below can be applied not only to the current point cloud data stream but also to multiple point cloud data streams and can be sent through higher-level parameter sets.

[0447] The meanings of each abbreviation are as follows: SPS: Sequence Parameter Set, GPS: Geometric Parameter Set, APS: Attribute Parameter Set, TPS: Tile Parameter Set, Geom: Geometric Bitstream = Geometric Tile Header + Geometric Tile Data, Attr: Attribute Bitstream = Attribute Tile Header + Attribute Tile Data.

[0448] The following information is defined independently of the encoding technique, but it can also be defined in conjunction with the encoding method. To support locally distinct scalability, it can be defined in the Tile Parameter Set (TPS) within the bitstream. Furthermore, when the syntax elements defined below apply not only to the current point cloud data stream but also to multiple point cloud data streams, they can be delivered via a higher-level parameter set.

[0449] Alternatively, bitstreams can be selected at the system level by defining Network Abstraction Layer (NAL) units for the bitstream and delivering relevant information for layer selection, such as layer_id.

[0450] According to the implementation, the parameters required for encoding and / or decoding point cloud data can be newly defined in a parameter set (e.g., SPS, GPS, APS, and TPS (or a list of tiles)) and / or the header of the corresponding tile (i.e., the data cell header or the tile header). For example, parameters can be added to GPS when performing geometric encoding and / or decoding, or parameters can be added to the tile and / or data cell header when performing tile-based encoding and / or decoding.

[0451] According to the implementation method, slice-related information based on layer groups can be signaled in SPS, and / or GPS, and / or APS, and / or LGSI.

[0452] According to the implementation method, slice-related information based on layer groups can be signaled in TPS, and / or geometric data cell header, and / or attribute data cell header, and / or LGSI.

[0453] According to the implementation, when the syntax elements defined below apply not only to the current stream but also to multiple point cloud data streams, slice-related information based on layer groups can be delivered through a higher-level parameter set.

[0454] According to the implementation, slice-related information based on layer groups can be defined at corresponding or separate locations according to the application or system, allowing for different scopes and application methods. The term "field" used in the syntax described below can have the same meaning as "parameter" or "syntax element".

[0455] The parameters (which may be referred to as metadata, signaling information, etc.) according to the implementation method can be generated during the process of the transmitter and delivered to the receiver for the reconstruction process.

[0456] According to the implementation, parameters containing slice-related information based on layer groups can be generated by the metadata processor (or metadata generator) or signaling processor of the transmitting device and delivered to the receiving device for decoding / reconstruction. For example, parameters generated and transmitted by the transmitting device can be obtained by the metadata parser of the receiving device.

[0457] According to the implementation, parameters describing the layer group structure (e.g., parameters containing slice-related information based on the layer group) are signaled at various levels. For example, common structure information is described in the SPS, while details of each layer group or subgroup are signaled in the slice header (or data unit header). Additionally, the LGSI and associated slice header (or associated data unit header) are provided to describe the overall layer group structure. The following describes the definition of G-PCC slices and the signaling method for fine-grained slices.

[0458] 1) Definition of G-PCC slice

[0459] A slice is a set of points encoded as an independent fine-grained slice and zero or more related fine-grained slices. For example, a slice may include multiple fine-grained slices (FGS), and the FGS may be called a segmented slice or a subdivided slice.

[0460] Related fine-grained slices: Data units of a slice that depend on the previous data unit within the same slice.

[0461] Independent fine-grained slice: the first [geometric] data unit of the slice.

[0462] 2) Enable fine-grained slicing in SPS

[0463] 3) The header of the relevant data unit carries the basic information required for decoding the relevant fine-grained slice, including context inheritance and slice-specific bounding boxes.

[0464] 4) Define a list of layer group structures to describe the relationships between fine-grained slices.

[0465] ​ and ​ An example of the syntax structure of a Sequence Parameter Set (SPS) according to an implementation is illustrated. The SPS may contain sequence information of a point cloud data bitstream. Specifically, in this example, it contains slice-related information based on layer groups.

[0466] According to the implementation method, ​ and ​ The syntax in can be included Figure 29 The bitstream is generated by the point cloud encoder and decoded by the point cloud decoder.

[0467] simple_profile_compatibility_flag: Indicates whether the bitstream conforms to a simple profile (when it is 1) or not (when it is 0).

[0468] dense_profile_compatibility_flag: Indicates whether the bitstream conforms to a dense profile (when it is 1) or not (when it is 0).

[0469] predictive_profile_compatibility_flag: Indicates whether the bitstream conforms to the predictive profile (when it is 1) or does not (when it is 0).

[0470] main_profile_compatibility_flag: Indicates whether the bitstream conforms to the main profile (when it is 1) or not (when it is 0).

[0471] `slice_reordering_constraint_flag`: Indicates whether the bitstream is sensitive (when it is 1) or insensitive (when it is 0) to the reordering or removal of slices within the encoded point cloud frame. When `slice_reordering_constraint_flag` is 1, the resulting bitstream may not be fully decoded if slices are reordered or removed.

[0472] `unique_point_positions_constraint_flag`: When equal to 1, it indicates that each point should have a unique position in each coded point cloud frame. `unique_point_positions_constraint_flag` equal to 0 indicates that two or more points can have the same position.

[0473] sps_seq_parameter_set_id: Identifies the SPS for reference by other Data Units (DUs).

[0474] seq_origin_bits: Indicates the length of each seq_origin_xyz syntax element in bits, excluding the sign bit.

[0475] seq_origin_xyz[k] and seq_origin_log2_scale: Together, these indicate the XYZ origin and the encoded coordinates of the sequence in the sequence coordinate system, starting from the origin of the applied coordinate system. When seq_origin_bits is 0, seq_origin_xyz[k] and seq_origin_log2_scale are inferred to be 0.

[0476] seq_bounding_box_size_bits: Indicates the length (in bits) of each seq_bbox_size_minus1_xyz syntax element.

[0477] seq_bounding_box_size_minus1_xyz[k]: Add 1 to indicate the k-th XYZ component of the encoded volume dimension in the sequence coordinate system.

[0478] seq_unit_numerator_minus1, seq_unit_denominator_minus1, and seq_unit_is_metres together indicate the length in the sequence coordinate system as a unit vector.

[0479] sps_num_attribute_sets: Indicates the number of attributes listed in the SPS attribute list.

[0480] attribute_instance_id[attrId]: Indicates the instance identifier of the attribute being identified.

[0481] attribute_bitdepth_minus1[attrId]: Increment 1 to specify the bit depth of all components of the identified attribute.

[0482] `layer_group_enabled_flag`: When equal to 1, it indicates that the geometric bitstream of a slice is contained in multiple slices that match the group or corresponding subgroup of the coding layer. When equal to 0, `layer_group_enabled_flag` specifies that the geometric bitstream is contained in a single slice.

[0483] `num_layer_groups_minus1 + 1` indicates the number of layer groups, where a layer group represents a group of consecutive tree layers that are part of the geometric coding tree structure. `num_layer_groups_minus1` ranges from 0 to the total number of coding tree layers.

[0484] `layer_group_id` indicates the layer group ID of the slice (or an indicator of the layer group of the slice). `layer_group_id` is in the range of 0 to `num_layer_groups_minus1`.

[0485] num_layers_minus1 + 1 indicates the number of coding layers (also called levels) included in the i-th layer group. When i is 0, the total number of layer groups can be derived by adding all (num_layers_minus1[i] + 1) to num_layer_groups_minus1.

[0486] A `subgroup_enabled_flag` value of 1 indicates that the i-th layer group is divided into two or more subgroups, where the set of points in the subgroups of the layer group is the same as the set of points in the layer group. When the `subgroup_enabled_flag` of the i-th layer group is 1, the `subgroup_enabled_flag` of the j-th layer group should also be 1 if `j` is greater than or equal to `i`. A `subgroup_enabled_flag` value of 0 indicates that the current layer group is not subdivided into multiple subgroups and is contained within a single slice.

[0487] `subgroup_bbox_origin_bits_minus1 + 1` indicates the bit length of the `subgroup_bbox_origin` field (or syntax element).

[0488] The subgroup_bbox_size_bits_minus1 + 1 indicates the bit length of the subgroup_bbox_size field.

[0489] exist Figure 30a and Figure 30b In the context of layer groups, slice-related information may include at least one of layer_group_enabled_flag, num_layer_groups_minus1, layer_group_id[i], num_layers_minus1[i], subgroup_enabled_flag[i], subgroup_bbox_origin_bits_minus1, and subgroup_bbox_size_bits_minus1.

[0490] Figure 31 An example of the syntax structure of the relevant geometric data unit header according to the implementation method is shown. Figure 31 An example is shown where the header of the relevant geometric data cell contains slice-related information based on the layer group.

[0491] According to the implementation method, Figure 31 The syntax in can be included Figure 29 These are in the bitstream. They can be generated by the point cloud encoder, sent to a receiving device including a decoder, and decoded by the point cloud decoder.

[0492] According to the implementation, the related geometric data unit header may be included in the geometric data unit. The term "geometric data unit" may be used interchangeably with "related geometric data unit" or "geometric slice".

[0493] `dgsh_geometry_parameter_set_id` indicates the active GPS identified by `gps_geom_parameter_set_id`. The value of `dgsi_geometry_parameter_set_id` is the same as the value of `gdu_geometry_parameter_set_id` for the corresponding slice.

[0494] dgsi_slice_id specifies the geometric slice to which the currently relevant geometric data cell belongs.

[0495] `layer_group_id` is an indicator that specifies the layer group of a slice. The range of `layer_group_id` can be from 0 to `num_layer_groups_minus1`. If it does not exist, it is inferred to be 0.

[0496] `subgroup_id` is an indicator of the subgroup of the layer group referenced by `layer_group_id`. The range of `subgroup_id` can be from 0 to `num_subgroups_minus1[layer_group_id]`. Here, `subgroup_id` can indicate the order within slices with the same `layer_group_id`. When it does not exist, it is inferred to be 0.

[0497] subgroup_bbox_origin indicates the origin of the bounding box of the subgroup of the j-th layer group, as indicated by layer_group_id, and the i-th subgroup as indicated by subgroup_id.

[0498] subgroup_bbox_size indicates the size of the bounding box of the subgroup of the j-th layer group, as indicated by layer_group_id, and the size of the bounding box of the i-th subgroup, as indicated by subgroup_id.

[0499] According to the implementation, the bounding boxes of points in a subgroup are described by `subgroup_bbox_origin` and `subgroup_bbox_size`. In this case, when i is not equal to j, the region in the bounding box of the i-th subgroup does not overlap with the bounding box of the j-th subgroup.

[0500] The `ref_layer_group_id` is an indicator that specifies the layer group identifier used for the context reference of the currently relevant data cell. `ref_layer_group_id` ranges from 0 to the `layer_group_id` of the currently relevant data cell.

[0501] `ref_subgroup_id` indicates the reference subgroup of the layer group indicated by `ref_layer_group_id`. `ref_subgroup_id` is in the range of 0 to `num_subgroup_id_minus1` of the layer group indicated by `ref_layer_group_id`.

[0502] The bitstream according to the implementation may include at least one slice or subdivided slice, and may contain geometric data units and attribute data units. Each data unit includes a header. Additionally, the data units include independent data units and related data units. Related data units may represent inclusion relationships based on dependencies between parent and child nodes. Figure 31 The syntax for the header of the relevant geometric data cell is shown. Similarly, the syntax for the header of the geometric data cell can be related to... Figure 31 same.

[0503] Figure 32A list of layer group structures (LGSI) according to an implementation method is illustrated. That is, Figure 32 The relationship between fine-grained slices is described in the LGSI below.

[0504] lgsi_seq_parameter_set_id indicates the value of sps_seq_parameter_set_id.

[0505] lgsi_frame_ctr_lsb_bits indicates the length (in bits) of the lgsi_frame_ctr_lsb field (or syntax element).

[0506] lgsi_frame_ctr_lsb indicates the least significant bits (lgsi_frame_ctr_lsb_bits) of the valid FrameCtr of the LGSI. The LGSI remains valid until it is replaced by another LGSI.

[0507] lgsi_num_slice_ids_minus1 + 1 indicates the number of slices in LGSI.

[0508] lgsi_slice_id indicates the slice ID of the sid-th slice within LGSI.

[0509] lgsi_num_layer_groups_minus1 + 1 indicates the number of layer groups.

[0510] `lasi_subgroup_bbox_origin_bits_minus1 + 1` indicates the length (in bits) of the `lgsi_subgroup_bbox_origin` field.

[0511] lgsi_subgroup_bbox_size_bits_minus1 + 1 indicates the length (in bits) of the lgsi_subgroup_bbox_size field.

[0512] lgsi_layer_group_id is an indicator for the layer group.

[0513] lgsi_num_layers_minus1 + 1 indicates the number of coding layers in the i-th layer group of the sid-th slice. The total number of coding layers required to decode the n-th layer group is equal to the sum of lgsi_num_layers_minus1[sid][i] + 1, where i ranges from 0 to n.

[0514] lgsi_num_subgroups_minus1 + 1 indicates the number of subgroups in the i-th layer group of the sid-th slice.

[0515] `lgsi_subgroup_id` indicates the ID (or indicator) of the subgroup (or subgroup). The value of `lgsi_subgroup_id` is between 0 and `lgsi_num_subgroups_minus1`.

[0516] `lgsi_parent_subgroup_id` indicates the subgroup within the layer group indicated by `lgsi_subgroup_id`. Within the layer group indicated by `lgsi_subgroup_id`, the value of `lgsi_parent_subgroup_id` is between 0 and `gi_num_subgroups_minus1`.

[0517] lgsi_subgroup_bbox_origin indicates the origin of the subgroup bounding box of the subgroup indicated by lgsi_subgroup_id of the layer group indicated by lgsi_layer_group_id.

[0518] lgsi_subgroup_bbox_size indicates the size of the subgroup bounding box of the layer group indicated by lgsi_layer_group_id, which is the subgroup of the subgroup indicated by lgsi_subgroup_id.

[0519] lgsi_origin_bits_minus1 + 1 indicates the length (in bits) of the lgsi_origin_xyz field.

[0520] lgsi_origin_xyz indicates the origin of all partitions. The value of lgsi_origin_xyz[k] is equal to sps_bounding_box_offset[k].

[0521] lgsi_origin_log2_scale indicates the scaling factor used to scale the lgsi_origin_xyz field. The value of lgsi_origin_log2_scale is equal to sps_bounding_box_offset_log2_scale.

[0522] As mentioned above, geometric information can be encoded (i.e., compressed) based on octrees. In this case, the planar pattern used in octree-based geometric encoding probabilistically determines whether each node is planar. This introduces the burden of updating the probability each time and tracking the local node density of each node. As a way to mitigate this situation, the use of a planar pattern can be determined based on the density at each octree level.

[0523] According to the implementation method, the density condition used to determine whether to use the planar pattern can be calculated as shown in Equation 1. That is, the planar compliance for each tree depth is determined based on the density shown in Equation 1.

[0524] [Formula 1]

[0525] numPoints indicates the number of points in the point cloud. This indicates the number of IDCM nodes (also known as direct coding mode nodes) from the root to the i-th octree level. In other words, Indicates the number of points from the root node level to the i-th octree level using IDCM encoding.

[0526] numSubnodes i This indicates the number of child nodes (i.e., the number of offspring nodes) generated at the i-th octree level. In other words, numSubnodes i This indicates the number of child nodes occupied by the nodes included in the i-th octree level. Additionally, realDensity_(i+1) indicates the actual density of points in the (i+1)-th octree level. Here, the octree level can be referred to as a tree level or hierarchy.

[0527] According to the implementation method, whether to use a planar pattern in the (i+1)th octree layer can be determined as follows. Specifically, before the encoding process of the (i+1)th octree layer, the suitability of the xyz planar pattern for all nodes in the (i+1)th octree layer is determined as follows:

[0528] The planarEligibleKOctreeDepth_(i+1) can be independently derived and applied by both the encoder and decoder. Here, th is a predefined threshold, which can be set to, for example, 1.3.

[0529] For example, the density (realDensity) calculated by applying Equation 1 in the (i+1)th octree layer. i+1 When the value is less than the threshold, planarEligibleKOctreeDepthi+1 A value of 1 indicates the use of a planar pattern at the (i+1)th octree level. Conversely, when the density (realDensity) is 1, it indicates the use of a planar pattern. i+1 When the value is not less than the threshold, planarEligibleKOctreeDepth i+1 A value of 0 indicates that the planar pattern is not used in the (i+1)th octree level.

[0530] As described above, when using layer group slicing, Equation 1 for realDensity belonging to the m-th subgroup of the n-th layer group can be modified to Equation 2 given below.

[0531] [Equation 2]

[0532] numPoints(n,m) indicates the number of final points included in the bounding box corresponding to the m-th subgroup of the n-th level group.

[0533] numPointsCodedByIdcm i (n,m) indicates the number of points in the octree from the root to the i-th octree level that are included in the bounding box corresponding to the m-th subgroup belonging to the n-th level group, using IDCM encoding.

[0534] numSubnodes i (n,m) indicates the number of child nodes (i.e., the number of child nodes) generated at the i-th octree level for the m-th subgroup belonging to the n-th level group.

[0535] Based on the above, the following can be used to determine whether to use the planar pattern in the (i+1)th octree level of the mth subgroup belonging to the nth level group:

[0536] In other words, although regions are divided into subgroups due to layer slicing, the above formula can be used to adaptively apply the planar pattern according to the characteristics of the regions.

[0537] However, in the case of layer-group slicing, the octree depth can be divided into different slices, making it difficult to directly apply the above method to the upper-level group. For example, in the last layer-group, numPoints can be estimated because the number of points generated in the subgroups included in the slice is signaled in the header. However, since the number of points signaled in the header refers to the number of final points generated in each slice, it is difficult to estimate numPoints of the upper-level group. To solve this problem, according to the implementation, the following method can be considered: 1) Signal the number of final points generated at the leaf group level: For a given slice in a layer group, the encoder can send the number of final points generated at the leaf layer group level to the decoder.

[0538] 2) Use any number of points: When numPoints is unknown, both the encoder and decoder can use any number of predefined points.

[0539] In this context, numPoints(n, m) can indicate the number of final points included within the bounding box of the m-th subgroup of the n-th layer group. If no subgroup exists (i.e., it belongs to the last layer group), numSlicePoint carried in the header can be used. If a subgroup exists (i.e., it belongs to the upper layer group), the encoder and decoder can use any number of points pre-agreed upon.

[0540] For example, a fixed value can be used, such as the maximum number of points that can be included in each slice (MaxSlicePoint).

[0541] As another example, predicted values ​​can be used. Equation 3 below illustrates an example of a prediction using coefficients (e.g., coeff_a, coeff_b). That is, numPoints(n, m) can be estimated by a linear function as shown in Equation 3.

[0542] [Formula 3]

[0543] Here, coeff_a and coeff_b represent the coefficients of the linear function. coeff_a and coeff_b can be predefined or directly passed to the decoder as needed.

[0544] Specifically, the number of final points (numPoints(n, m)) included in the bounding box of the m-th subgroup in the n-th layer group can be obtained by adding the coefficients coeff_a and coeff_b of the linear function to the number of parent and child group nodes.

[0545] 3) Send planarEligibleKOctreeDepth directly

[0546] Since the encoder of the transmitting device knows the number of final points for each subgroup, it can calculate the planarEligibleKOctreeDepth based on this. i+1 (n, m) and send this value directly to the decoder of the receiving device. In this case, the decoder does not need to calculate the plane qualification, thus reducing the complexity of the decoder.

[0547] According to the implementation, the encoder of the transmitting device can send a signal in the relevant geometric data unit header to notify the above-mentioned planar pattern related information, so as to send it to the decoder of the receiving device.

[0548] Figure 33 Another example of the syntax structure of the header of the relevant geometric data unit according to the implementation method is shown. Figure 33 An example is shown where the header of the relevant geometric data cell contains slice-related information based on layer groups and planar pattern-related information.

[0549] According to the implementation method, Figure 33 The syntax in can be included Figure 29 These are part of the bitstream. They can be generated by a point cloud encoder, sent to the receiving device as point cloud data, and decoded by a point cloud decoder.

[0550] because Figure 33 Details regarding the slice-related information based on layer groups, which is signaled in the header of the relevant geometric data unit, have been referenced. Figure 31 It has been described, so please refer to Figure 31 The description will be omitted below to avoid redundancy. Figure 33 The middle section describes information related to the planar pattern.

[0551] exist Figure 33 In this context, when checkPlanarEligibilityBasedOnOctreeDepth is true, it indicates that a planar mode should be used in the corresponding layer group. In this case, the header of the relevant geometric data cell may include planarEligibilityType.

[0552] planarEligibilityType can indicate the method of using planar mode. For example, in the values ​​of planarEligibilityType, 0 indicates that the existing nodes are adaptively planar mode applied; 1 indicates that the number of final points generated at the leaf group level is signaled; 2 indicates that any number of points are used; and 3 indicates that planarEligibleKOctreeDepth is directly signaled.

[0553] Specifically, when the value of planarEligibilityType is 1, the header of the relevant geometric data cell includes the number of final points in the subgroup (numFinalOutputPointsInSubgroup). When the value of planarEligibilityType is 2, the header includes the predicted number of points in the slice (numSlicePointEstimationType). When the value of planarEligibilityType is 3, the header includes the planar pattern available octree depth flag (planarEligibleKOctreeDepthFlag) as many times as the number of layers in the subgroup (numLayersInSubgroup).

[0554] The number of final output points in a subgroup (numFinalOutputPointsInSubgroup) represents the number of final output points included within the bounding box of the subgroup of the current slice.

[0555] The predicted number of points in a slice (numSlicePointEstimationType) indicates the estimation method used to obtain the realDensity of numSlicePoint (or numPoints). For example, numSlicePointEstimationType equal to 0 indicates a predetermined value (e.g., maxSlicePoint, the maximum number of points that a slice can contain). numSlicePointEstimationType equal to 1 indicates that the number is estimated by a linear function. In this case, the coefficients coeff_a and coeff_b of the linear function can be predefined or sent directly as needed.

[0556] numLayersInSubgroup: This indicates the number of octree depths included in the current slice.

[0557] When the value of `planarEligibleKOctreeDepthFlag` is 1, which is repeated as many times as the value of `numLayersInSubgroup`, it indicates that the planar mode should be used at the i-th octet depth within the octet depths included in the current slice. When the value is 0, it indicates that the planar mode should not be used at the i-th octet depth within the octet depths included in the current slice. `planarEligibleKOctreeDepthFlag` can correspond to `planarEligibleKOctreeDepth_(i+1)(n,m)` generated in the method that directly signals `planarEligibleKOctreeDepth`.

[0558] Equation 4 below is another example of obtaining the density condition used to determine whether to use the planar mode.

[0559] [Formula 4]

[0560] numPoints(s) indicates the number of points in subgroup s.

[0561] numPointsCodedByIdcm i (s) indicates the number of points from the root node level to the i-th octree level that are encoded using IDCM and are within the subgroup boundary of subgroup s.

[0562] numSubnodes i (s) indicates the number of child nodes occupied by nodes included in the i-th octree level of subgroup s.

[0563] Furthermore, realDensity i+1 (s) indicates the actual density of points in the (i+1)th octree layer of the subgroup s.

[0564] According to the implementation method, whether to use a planar pattern in the (i+1)th octree layer can be determined as follows. That is, before the encoding process of the (i+1)th octree layer, the suitability of the xyz planar pattern of all nodes in the (i+1)th octree layer of subgroup s is determined as follows:

[0565] Here, th is a predefined threshold, which can be set to, for example, 1.3.

[0566] In this regard, when using subgroup adaptive planar eligibility based on density, the number of points in each subgroup remains unknown to the decoder until the subgroup belonging to the last layer group is delivered. This disclosure can notify the decoder of planar eligibility information in the data cell header or associated geometric data cell header that identifies each geometric data cell (e.g., planarEligibleKOctreeDepth). i+1 (s) is used to match the encoder and decoder to solve this problem.

[0567] In this disclosure, an IDCM node may be referred to as a direct compression node. That is, when the hierarchical structure used for geometry decoding is in tree form, some subgroups may include at least one IDCM node.

[0568] According to the implementation, this disclosure defines a node that meets the following conditions as an IDCM node, and some subgroups may include at least one IDCM node that meets these conditions.

[0569] 1) Based on the parent's eligibility criteria: From the perspective of the parent node of the current node, there is only one occupied child node (the current node). From the perspective of the grandparent node, there is at most one occupied child node (that is, the parent node has at most one occupied sibling node, which means that the grandparent node has at most two occupied children nodes).

[0570] 2) 6N eligibility criteria: From the perspective of the parent node, there is only one occupied child node (the current node), and the six neighbors (nodes sharing the same face) are not occupied.

[0571] In this disclosure, nodes that meet the above conditions are referred to as IDCM nodes (or points), and nodes that do not meet the conditions are referred to as non-IDCM nodes (or points). Within each subgroup, a single layer may include zero or one or more IDCM nodes.

[0572] The following describes another embodiment of the fine-grained slicing (FGS) described above. In this case, some descriptions may overlap with the descriptions related to FGS provided above.

[0573] First, some terminology will be redefined.

[0574] Fine-grained slices (FGS) are subsets of slices that carry the geometry or attributes of a subgroup.

[0575] A layer group is a group of consecutive tree levels that occupy a tree or attribute assignment occupy a tree.

[0576] A subgroup is a spatial subset of a layer group, wherein the bounding box of a subgroup does not overlap with other subgroups in the same layer group.

[0577] The root group is a group that includes the root node of the occupancy tree or the attribute assignment occupancy tree.

[0578] A parent-child group is a subgroup within the layer group that is adjacent to the top minimum depth of the current subgroup, where the bounding box of the parent-child group is a superset of the bounding box of the current subgroup.

[0579] A subgroup is a subgroup within a layer group that is adjacent to the lowest depth of the current subgroup, where the bounding box of the subgroup is a subset of the bounding box of the current subgroup.

[0580] An assignment occupancy tree is an occupancy tree in which the attributes of nodes at each tree level are assigned by the attributes of their child nodes.

[0581] In this case, a slice can include FGS, and each FGS is mapped one-to-one with the FGS geometry or FGS attributes of a subgroup in the layer group.

[0582] FGS is indicated by pairs of layer group indexes and subgroup indexes.

[0583] The FGS within a slice is identified by a public slice identifier (slice_id).

[0584] Each FGS includes a geometric data unit (GDU) or associated geometric data unit (DGDU) that encodes and decodes the FGS geometry, or an attribute data unit (ADU) or associated attribute data unit (DADU) that encodes and decodes the FGS attributes.

[0585] An FGS begins with a GDU. This FGS may be followed by an FGS of a DGDU that depends on the GDU or the previously decoded DGDU.

[0586] (If present) The FGS attribute begins with an ADU. This FGS may be followed by the FGS of a DADU that depends on the corresponding ADU or the DADU decoded from the previous one. ADUs and DADUs appear after GDUs and DGDUs.

[0587] In the hierarchical structure disclosed herein, nodes within the occupancy tree or attribute assignment occupancy tree are grouped into hierarchical groups and subgroups.

[0588] A layer group is a group of consecutive tree levels, and in this case, all tree levels belong to exactly one layer group. The minimum depth of a subgroup is one greater than the maximum depth of its parent-child group, or 0 in the case of the root layer group. The maximum depth of a subgroup is one less than the minimum depth of its sub-child group, or the maximum depth of the occupied tree in the case of the last layer group. In this case, the layer group is identified by the layer group index (layer_group_idx).

[0589] A subgroup is a spatial subset of a layer group, and a node at a tree level belongs to one of the subgroups of the layer group.

[0590] The location of the output nodes of a subgroup is described by a bounding box, and the extent of the bounding box of a subgroup does not overlap with the extent of the bounding box of any other subgroup in the same layer group.

[0591] The set of output nodes for subgroups within a layer group is the same as the set of output nodes within the layer group.

[0592] The root layer group has only one subgroup. Each subgroup within the layer group is identified by the subgroup index (subgroup_idx).

[0593] Figure 34 (a) and Figure 34(b) illustrates the relationship between layer groups, subgroups, and fine-grained slices (FGS) according to the implementation method. Specifically, Figure 34 (a) illustrates the layer group structure of an occupancy tree with a depth of 8, and Figure 34 (b) illustrates the parent-child relationship between a subgroup and its corresponding FGS.

[0594] exist Figure 34 In (a), three layer groups are defined: layer group 0 includes depths 0 to 3, layer group 1 includes depths 4 to 6, and layer group 2 includes depths 7 and 8. That is, layer group 0 includes one subgroup, layer group 1 includes two subgroups, and layer group 2 includes three subgroups. In other words, except for the root layer group, each layer group can contain subgroups, and each subgroup is represented by a pair of layer group indices and subgroup indices. For example, the root layer group is represented as (0, 0).

[0595] exist Figure 34 In (b), Figure 34 Subgroups within each layer group shown in (a) are represented as bounded spatial regions in the xy-plane. Subgroups are considered parent-child when their bounding boxes contain one or more subgroups within the next layer group. That is, subgroups in adjacent layer groups are considered parent-child when the bounding boxes of a subgroup in a layer group are supersets of the bounding boxes of one or more subgroups in the next layer group. For example, subgroup (0, 0) is the parent of subgroups (1, 0) and (1, 1). Similarly, subgroups (2, 0) and (2, 1) are children of subgroup (1, 0). Each subgroup, represented by a pair of layer group indices and subgroup indices, is included in a different FGS.

[0596] exist Figure 34 (a) and Figure 34 In (b), S l,n This represents the subgroup n associated with layer group l. i Let i represent the depth of the octree.

[0597] Figure 35a and Figure 35b Another example of the syntax structure of a Sequence Parameter Set (SPS) according to an implementation is illustrated. The SPS may include sequence information of a point cloud data bitstream, and specifically, an example including information related to slices based on layer groups is illustrated.

[0598] Figure 35a and Figure 35b The syntax can be included Figure 29 The bitstream can be generated by a point cloud encoder according to the implementation method and can be decoded by a point cloud decoder.

[0599] The following section describes information related to layer-group-based slicing. For information other than that related to layer-group-based slicing, please refer to [link to relevant documentation]. Figure 30a and Figure 30b The description.

[0600] For example, in Figure 35a and Figure 35b In the context, simple_profile_compliant corresponds to Figure 30a and Figure 30b In the `simple_profile_compatibility_flag`, `dense_profile_compliant` corresponds to `dense_profile_compatibility_flag`, and `predictive_profile_compliant` corresponds to `predictive_profile_compatibility_flag`. `main_profile_compliant` corresponds to `main_profile_compatibility_flag`, `slice_reordering_constraint` corresponds to `slice_reordering_constraint_flag`, and `unique_point_positions_constraint` corresponds to `unique_point_positions_constraint_flag`. `sps_seq_parameter_set_id` corresponds to `sps_seq_parameter_set_id`, `seq_origin_bits` corresponds to `seq_origin_bits`, and `seq_origin_xyz[k]` and `seq_origin_log2_scale` correspond to `seq_origin_xyz[k]` and `seq_origin_log2_scale`, respectively. seq_bbox_size_bits corresponds to seq_bounding_box_size_bits, and seq_bbox_size_minus1_xyz[k] corresponds to seq_bounding_box_size_minus1_xyz[k].

[0601] exist Figure 35a and Figure 35bIn this context, when the value of `sps_layer_group_slicing_flag` is 1, the specified slice includes multiple FGS carrying FGS geometry or FGS attributes. When the value of `sps_layer_group_slicing_flag` is 0, the specified slice does not include FGS.

[0602] The value obtained by adding 1 to num_layer_groups_minus1 represents the number of layer groups, and in this case, a layer group represents a group of consecutive tree layers as part of a geometric coding tree structure. num_layer_groups_minus1 needs to be in the range of 0 to the number of coding tree layers.

[0603] `layer_group_id[i]` specifies the layer group index (i.e., identifier) ​​to identify the i-th layer group of the slice. The range of `layer_group_id` must be from 0 to `num_layer_groups_minus1`.

[0604] The total number of coding layers in a layer group can be derived by summing all i values ​​from 0 to num_layer_groups_minus1 (num_layers_minus1[i] + 1).

[0605] A value of 1 for `subgroup_enabled_flag[i]` indicates that the i-th layer group is divided into two or more subgroups, where the set of points in the subgroups of the layer group is the same as the set of points in the layer group. When `subgroup_enabled_flag[i]` of the i-th layer group is equal to 1, `subgroup_enabled_flag[j]` of the j-th layer group must also be equal to 1 if `j` is greater than or equal to `i`. A value of 0 for `subgroup_enabled_flag[i]` indicates that the i-th layer group is not subdivided into multiple subgroups and is included in a single slice.

[0606] The subgroup_bbox_origin_bits_minus1 + 1 specifies the bit length of the subgroup_bbox_origin field (or syntax element).

[0607] `subgroup_bbox_size_bits_minus1 + 1` specifies the bit length of the `subgroup_bbox_size` field.

[0608] Figure 36 An example of the syntax structure of the geometric data unit header according to the implementation method is shown. Figure 36 The syntax can be included Figure 29The bitstream can be generated by the point cloud encoder according to the embodiment, can be sent to the decoder of the receiving device, and can be decoded by the point cloud decoder.

[0609] exist Figure 36 In the configuration, gdu_geometry_parameter_set_id specifies the GPS activated by gps_geom_parameter_set_id.

[0610] gdu_temporal_id specifies the time ID of the frame associated with the geometric data unit (GDU).

[0611] The slice_id identifies the slice for reference by another DU.

[0612] The slice_tag identifies a slice as a member of a slice group with the same slice_tag value. When a tile list DU exists, the slice group is a tile identified by its tile ID. Conversely, when a tile list DU does not exist, the interpretation of the slice_tag depends on the application.

[0613] frame_ctr_lsb specifies the frame_ctr_lsb_bits LSB of FrameCtr (conceptual frame counter).

[0614] When `slice_entropy_continuation` is 1, it specifies that an entropy resolution recovery process should be applied at the beginning of the GDU and at the beginning of all ADUs within the slice. When `slice_entropy_continuation` is 0, it specifies that when `slice_inter_entropy_continuation` is 0, the resolution of the GDU and all ADUs within the slice is independent of other slices within the frame. When `slice_entropy_continuation` does not exist, it can be inferred to be 0.

[0615] The prev_slice_id must be the same as the GDU slice_id of the previous slice in the bitstream order.

[0616] The following describes information related to layer-based slicing.

[0617] When the sps_layer_group_slicing_flag included in the sequence parameter set is true (i.e., 1), the geometry data cell header may include num_subsequent_subgroups. When the value of sps_layer_group_slicing_flag is 1, it indicates that the slice includes multiple FGS carrying FGS geometry or FGS attributes.

[0618] num_subsequent_subgroups specifies the number of subsequent related data units that reference the current data unit or related data unit.

[0619] When the occtree_planar_enabled included in the geometry parameter set is true (i.e., 1) and geom_angular_enabled is false (i.e., 0), the geometry data cell header may include the number of planar_eligibility_by_density[i] syntax elements corresponding to the value of num_layers_minus1[0].

[0620] `occtree_planar_enabled` specifies whether to partially perform (when it is 1) or not (when it is 0) the encoding of the node occupancy bitmap by signaling occupied and unoccupied planes. When `occtree_planar_enabled` is not present, it can be inferred to be 0.

[0621] geom_angular_enabled specifies whether to encode the slice geometry using a set of beams that are positioned along the V-axis at the origin of the angle (when it is 1) or not (when it is 0).

[0622] When `planar_eligibility_by_density[i]` is 1, it indicates that the planar eligibility flag is enabled for the i-th depth of the current subgroup. In other words, when `planar_eligibility_by_density[i]` is 1, it indicates that planar eligibility is enabled for the i-th depth of the current subgroup. When `planar_eligibility_by_density` is 0, it indicates that the planar eligibility flag is disabled for the i-th depth of the current subgroup. In other words, when `planar_eligibility_by_density[i]` is 0, it indicates that planar eligibility is disabled for the i-th depth of the current subgroup.

[0623] Figure 37 Another example of the syntax structure of the header of the relevant geometric data unit according to the implementation method is shown. Figure 37 An example is shown where slice-related information based on layer groups is included in the header of the relevant geometric data cell.

[0624] According to the implementation method, Figure 37 The syntax can be included Figure 29 The bitstream can be generated by the point cloud encoder, sent to the decoder of the receiving device, and decoded by the point cloud decoder.

[0625] `dgsh_geometry_parameter_set_id` indicates the active GPS as indicated by `gps_geom_parameter_set_id`. The value of `dgsi_geometry_parameter_set_id` is the same as the value of `gdu_geometry_parameter_set_id` for the corresponding slice.

[0626] slice_id specifies the geometric slice to which the current relevant geometric data cell belongs.

[0627] `layer_group_id` is an indicator of the layer group of a slice, i.e., the layer group index used to identify the layer group. The range of `layer_group_id` can be from 0 to `num_layer_groups_minus1`. When it does not exist, it is inferred to be 0.

[0628] `subgroup_id` is an indicator of a subgroup within a layer group referenced by `layer_group_id`; it is the subgroup index used to identify the subgroup. The range of `subgroup_id` can be from 0 to `num_subgroups_minus1[layer_group_id]`. Here, `subgroup_id` represents the sub-region within the layer group identified by `layer_group_id`. When it does not exist, `subgroup_id` is inferred to be 0.

[0629] subgroup_bbox_origin represents the minimum position of the subgroup bounding box of the j-th layer group indicated by layer_group_id and the i-th subgroup indicated by subgroup_id.

[0630] subgroup_bbox_size represents the size of the subgroup bounding box of the j-th layer group, as indicated by layer_group_id, and the i-th subgroup, as indicated by subgroup_id.

[0631] According to the implementation, the bounding boxes of points in a subgroup are described by `subgroup_bbox_origin` and `subgroup_bbox_size`. In this case, when i is not equal to j, the region within the bounding box of the i-th subgroup does not overlap with the bounding box of the j-th subgroup.

[0632] `ref_layer_group_id` indicates the layer group identifier used for the context reference of the current related data cell. The range of `ref_layer_group_id` is from 0 to the value of the `layer_group_id` of the current related data cell.

[0633] `ref_subgroup_id` represents the indicator of the reference subgroup of the layer group indicated by `ref_layer_group_id`. The range of `ref_subgroup_id` is from 0 to `num_subgroup_id_minus1` of the layer group indicated by `ref_layer_group_id`.

[0634] When `context_reference_indication_flag` is 1, it indicates that the context state of the current related data unit is inherited by one or more subsequent related data units. When `context_reference_indication_flag` is 0, it indicates that the context state of the current related slice (i.e., data unit) is not inherited by subsequent related slices (i.e., data units). When this flag is not present, the `context_reference_indication_flag` of the data unit is inferred to be 1.

[0635] The decoder of the receiving device can manage the context buffer using `context_reference_indication_flag`. When `context_reference_indication_flag` is 1, the context state of the currently relevant slice (i.e., data unit) is stored in the context buffer when the decoding of the current data unit is completed. When `context_reference_indication_flag` is 0, the context state of the currently relevant slice (i.e., data unit) is not stored in the context buffer. Here, the decoder can be... Figure 1 , Figure 2 , Figure 7 , Figure 9 and Figure 28 Any of the decoders shown in the example.

[0636] When context_reference_indication_flag is true (i.e., 1), the header of the relevant geometric data cell can include num_subsequent_subgroups.

[0637] num_subsequent_subgroups specifies the number of subsequent related data units that reference the current data unit or related data unit.

[0638] When the occtree_planar_enabled included in the geometry parameter set is true (i.e., 1) and geom_angular_enabled is false (i.e., 0), the header of the relevant geometry data cell may include the number of planar_eligibility_by_density[i] syntax elements corresponding to the value of num_layers_minus1[layer_group_id].

[0639] When `planar_eligibility_by_density[i]` is 1, it indicates that the planar eligibility flag is enabled for the i-th depth of the current subgroup. In other words, when `planar_eligibility_by_density[i]` is 1, it indicates that planar eligibility is enabled for the i-th depth of the current subgroup. When `planar_eligibility_by_density` is 0, it indicates that the planar eligibility flag is disabled for the i-th depth of the current subgroup. In other words, when `planar_eligibility_by_density[i]` is 0, it indicates that planar eligibility is disabled for the i-th depth of the current subgroup.

[0640] The bitstream according to the implementation may include at least one slice or subdivided slice, and may include geometric data units and attribute data units. Each data unit includes a header (e.g., a geometric data unit header, a related geometric data unit header, an attribute data unit header, or a related attribute data unit header). Data units may include independent data units and related data units. Related data units may represent inclusion relationships based on dependencies between parent and child nodes.

[0641] Depend on Figure 1 , Figure 2 , Figure 7 , Figure 9 and Figure 28 The decoding process performed by any of the devices illustrated is as follows.

[0642] The following is a description of the slice decoding process.

[0643] According to the implementation method, the slices in the point cloud frame encoded by the encoder of the transmitting device are decoded as follows.

[0644] 1) Decode point positions from a GDU within a slice. When sps_layer_group_slicing_flag is 1, decode point positions from a GDU and zero or more DGDUs.

[0645] 2) Configure default attribute values ​​for each attribute.

[0646] 3) Decode point attributes from each ADU within the slice. When `sps_layer_group_slicing_flag` is 1, decode point attributes from one ADU and zero or more DADUs. Decode the ADUs and DADUs after decoding the GDUs and DGDUs indicated by the same index pair (layer_group_id and subgroup_id).

[0647] 4) Offset the position of the decoded point and increment the output point count.

[0648] For each group of slices with the same slice_id value in the encoded point cloud frame, only one slice is decoded.

[0649] The following is a description of the fine-grained slice decoding process.

[0650] The GDU is decoded before all DGDUs in the slice. The DGDUs of the child-child group are decoded after the DGDUs of the parent-child group are decoded.

[0651] When decoding GDU, startDepth and endDepth are configured to 0 and num_layers_minus1+1, respectively.

[0652] When decoding DGDU, for each k in the range from 0 to (layer_group_id – 1), startDepth is configured as the cumulative value of num_layers_minus1[k] + 1, and for each k in the range from 0 to layer_group_id, endDepth is configured as the cumulative value of num_layers_minus1[k] + 1.

[0653] The occupancy tree represents the slice geometry as occupancy tree nodes. Parsing or traversing the encoded occupancy tree implicitly generates the representation of the slice geometry.

[0654] When `sps_layer_group_slicing_flag` is 1, the partial occupancy tree represents fine-grained slice geometry as subtrees of occupancy tree nodes in terms of spatial region and depth. Parsing or traversing the encoded partial occupancy tree implicitly generates this representation of fine-grained slice geometry.

[0655] Each point's location is represented by direct nodes, which encode the position of the leaf nodes in the tree or the relative position of the nodes.

[0656] Occupy tree nodes identify the presence of at least one point included in the volume of an axis-aligned cuboid. When `sps_layer_group_slicing_flag` is 1, occupy tree nodes identify the presence of at least one point included in the value of the cube. This volume is defined in the slice coordinate system by the inclusion lower corner `p_min` and the exclusive upper corner `p_max`.

[0657] The following is a description of plane occupancy coding.

[0658] Specifically, the conditions for each axis's compliance (or conformity) are as follows. In other words, the compliance of the plane occupancy code for each axis can be determined.

[0659] In Equation 5 below, PointDensity[dpth] is a factor that identifies the density of points at a depth (dpth) in the tree hierarchy. In other words, Equation 5 represents a method for calculating the density of points at a specific depth in the tree.

[0660] [Formula 5]

[0661] According to Equation 5, PointDensity[dpth] can be obtained by subtracting DirectNodePointCnt from "slice_num_points_minus1 + 1", multiplying the result by a constant 10, and then dividing the result by OccNodeCnt[dpth].

[0662] In Equation 5 above, slice_num_points_minus1+1 specifies the number of encoded points in the corresponding DU. slice_num_points_minus1 can be included in the geometric data unit tail semantics of the geometric data unit. According to the bitstream consistency requirement, slice_num_points_minus1+1 equals the number of decodable points in the corresponding DU. When sps_layer_group_slicing_flag is 1 and layer_group_id is greater than 0, slice_num_points_minus1+1 equals the number of nodes at the maximum depth of the tree level within the corresponding DGDU.

[0663] DirectNodePointCnt represents the cumulative number of points encoded in direct nodes. In other words, the number of points encoded in direct nodes is accumulated and counted as DirectNodePointCnt. This variable is initialized to 0 at the beginning of each slice or at the beginning of a fine-grained slice. DirectNodePointCnt can be used to calculate the number of remaining points, excluding points directly encoded at the corresponding depth.

[0664] OccNodeCnt[dpth] represents the cumulative count (i.e., cumulative number) of nodes at depth (dpth). When layer_group_slicing_flag is 1, OccNodeCnt[dpth] represents the cumulative count (i.e., cumulative number) of nodes at depth (dpth) of the corresponding subgroup. OccNodeCnt[dpth] is used as the denominator in Equation 5 to normalize the density of points represented by each node.

[0665] The constant 10 can be used to normalize point density or to convert point density to a specific scale.

[0666] According to the implementation, when sps_layer_group_slicing_flag (or fgs_layer_group_enabled) is 1, PointDensity[dpth] can be specified by planar_eligibility_by_density in the geometry data unit header (GDUH) or associated geometry data unit header (DGDUH), as shown in Equation 6 below. When the value of sps_layer_group_slicing_flag included in the SPS is 1, it means that the slice includes multiple FGS carrying FGS geometry or FGS attributes.

[0667] [Formula 6]

[0668] In other words, when a slice is divided into fine-grained slices, the value of planar_eligibility_by_density, which is signaled and sent in the geometric data unit header (GDUH) or the associated geometric data unit header (DGDUH), can be used to determine whether each depth has planar eligibility.

[0669] In Equation 6, [dpth – startDepth] represents the relative depth within the slice. The planar compliance value for the corresponding depth can be obtained using an index aligned with the slice's start depth (startDepth).

[0670] According to the implementation, when the value of sps_layer_group_slicing_flag included in the SPS is 1, the specified slice includes multiple FGS carrying FGS geometry or FGS attributes. When the value of sps_layer_group_slicing_flag is 0, the specified slice does not include FGS. As another example, when the value of sps_layer_group_slicing_flag is 1, the specified slice includes multiple FGS carrying partial slice geometry or partial slice attributes.

[0671] According to the implementation, when occtree_planar_enabled included in the geometry parameter set is true (i.e., 1) and geom_angular_enabled is false (i.e., 0), the geometry data unit header (GDUH) or associated geometry data unit header (DGDUH) may include a number of planar_eligibility_by_density syntax elements corresponding to the value of num_layers_minus1.

[0672] occtree_planar_enabled specifies whether to partially perform (when it is 1) or not (when it is 0) the encoding of the node occupancy bitmap by signaling the occupancy and non-occupancy planes.

[0673] geom_angular_enabled specifies whether to encode the slice geometry using a set of beams that are positioned along the V-axis at the origin of the angle (when it is 1) or not (when it is 0).

[0674] When `planar_eligibility_by_density` is 1, it indicates that the planar eligibility flag is enabled for the i-th depth of the current subgroup. In other words, when `planar_eligibility_by_density[i]` is 1, it indicates that planar eligibility is enabled for the i-th depth of the current subgroup. When `planar_eligibility_by_density` is 0, it indicates that the planar eligibility flag is disabled for the i-th depth of the current subgroup. In other words, when `planar_eligibility_by_density[i]` is 0, it indicates that planar eligibility is disabled for the i-th depth of the current subgroup.

[0675] Thus, in this disclosure, PointDensity[dpth], i.e., the point density at each depth, can be obtained directly in the decoder of the receiving device as shown in Equation 5, or it can be obtained using planar_eligibility_by_density as shown in Equation 6.

[0676] For example, when the point density (PointDensity[dpth]) at a specific depth (or layer or level) of the occupied tree obtained by Equation 5 or 6 is less than a threshold, it indicates that a planar mode is used at that depth, and when it is not less than the threshold, it indicates that a planar mode is not used at that depth. Here, the threshold can be predefined.

[0677] According to the implementation method, the existing formula for calculating PointDensity[dpth] can be used without modification to reduce the number of bits required to send planar_eligibility_by_density. In this case, slice_num_points_minus1 may not refer to the total number of points in the slice, but rather to the number of nodes or points generated when decoding the FGS.

[0678] The following is a description of the partial decoding of fine-grained slices.

[0679] The decoder can decode and recover some of the FGS. Slices divided into FGS support partial decoding in terms of density and / or spatial regions. In one implementation, when using partial decoding, data units not needed for generating partial output are filtered out before decoding the occupancy tree or attribute coefficients.

[0680] The following is a description of partial density decoding.

[0681] The decoder generates a low-density slice point cloud. The low-density slice point cloud is specified by the following variables.

[0682] The variable SkippedLayerGroup represents the number of specific layer groups applied for partial decoding skipping in the density direction. The value of SkippedLayerGroup must be in the range of 0 to num_layer_groups_minus1.

[0683] The variable MinNodeSizeLog2 represents the minimum occupied tree node size specified by SkippedLayerGroup.

[0684] The array SubgroupNodePos[layerGroupIdx][subgroupIdx][ptIdx][k] represents the output node of the subgroup identified by the layer group index (layerGroupIdx or layer_group_id) and the subgroup index (subgroupIdx or subgroup_id).

[0685] The array SubgroupNodeCnt[layerGroupIdx][subgroupIdx] represents the number of output nodes in the subgroup identified by the layer group index (layerGroupIdx or layer_group_id) and the subgroup index (subgroupIdx or subgroup_id).

[0686] The following is a description of the selection of FGS in partial density decoding.

[0687] When SkippedLayerGroup is greater than 0, layer groups with indices from 0 to OutLayerGroup are selected for decoding. OutLayerGroup is the maximum value of the layer group indices used for partial decoding, which is set to the value obtained by subtracting SkippedLayerGroup from num_layer_groups_minus1, as shown in Equation 7 below.

[0688] [Formula 7]

[0689] OutLayerGroup := num_layer_groups_minus1 – SkippedLayerGroup

[0690] The following code indicates whether to decode or skip a data unit or related data unit.

[0691] For example, when layer_group_id == 0, decode the geometry data unit (GDU) or attribute data unit (ADU). Otherwise, decode the associated geometry data unit (DGDU) or associated attribute data unit (DADU). Otherwise, skip the associated geometry data unit (DGDU) or associated attribute data unit (DADU).

[0692] if (layer_group_id == 0)

[0693] decode GDU or ADU

[0694] else if (layer_group_id ≤ OutLayerGroup)

[0695] decode DGDU or DADU

[0696] else

[0697] skip DGDU or DADU

[0698] Therefore, the depth of the geometric occupancy tree in partial decoding is inferred as the sum of the number of layers (or levels) of the layer groups whose indices are in the range of 0 to OutLayerGroup, where the number of layers in each layer group is num_layers_minus1[i] + 1, as shown in the following code.

[0699] PartialDepth = 0

[0700] for (i = 0; i < NumOutLayerGroup; i++)

[0701] PartialDepth += num_layers_minus1[i] + 1

[0702] The following is a description of geometric position correction.

[0703] When decoding all layer groups, the maximum depth (TotalDepth) of the geometric occupancy tree is inferred as the sum of the number of layers (or levels) of the layer groups whose indices are in the range of 0 to num_layer_groups_minus1, where the number of layers in each layer group is num_layers_minus1[i] + 1, as shown in the following code.

[0704] TotalDepth = 0

[0705] for (i = 0; i < num_layer_groups_minus1; i++)

[0706] TotalDepth += num_layers_minus1[i] + 1

[0707] MinNodeSizeLog2 is inferred as the difference between TotalDepth and PartialDepth, as shown below.

[0708] MinNodeSizeLog2 = TotalDepth - PartialDepth

[0709] When MinNodeSizeLog2 is greater than 1, the point is placed at the center of the block, as shown in the following code. That is, when the value of MinNodeSizeLog2 is greater than 1, the following code adjusts the coordinate values ​​to place each point at the center of the block. In the following code, the first for loop iterates through all points (or nodes) belonging to the corresponding subgroup, and the second for loop iterates through each axis (x, y, and z) of each point.

[0710] for (ptIdx = 0; ptIdx < SubgroupNodeCnt[layerGroupIdx][subgroupIdx];ptIdx++)

[0711] for (k = 0; k < 3; k++)

[0712] SubgroupNode[layerGroupIdx][subgroupIdx][ptIdx][k] |=(MinNodeSizeLog2 > 1) << (MinNodeSizeLog2 – 1)

[0713] The following is a description of the decoding of a partial region.

[0714] The decoder generates a point cloud of a portion of the slice. The low-density slice point cloud is specified by the following variables.

[0715] The arrays RoiBoundingBoxMin and RoiBoundingBoxMax are application-specific arrays that specify the minimum and maximum positions of the bounding box for the region of interest (ROI).

[0716] The array SubgroupNodePos[layerGroupIdx][subgroupIdx] represents the output node of the subgroup identified by the layer group index (layerGroupIdx or layer_group_id) and the subgroup index (subgroupIdx or subgroup_id).

[0717] The array SubgroupNodeCnt[layerGroupIdx][subgroupIdx] represents the number of output nodes in the subgroup identified by the layer group index (layerGroupIdx or layer_group_id) and the subgroup index (subgroupIdx or subgroup_id).

[0718] The following is a description of the selection of FGS in partial region decoding.

[0719] When RoiBoundingBoxMin and RoiBoundingBoxMax exist, select the subgroup with bounding boxes that overlap with the bounding boxes of the ROI for decoding.

[0720] The following code indicates whether to decode or skip a data unit or related data unit.

[0721] For example, when layer_group_id == 0, decode the geometry data unit (GDU) or attribute data unit (ADU). When RoiBoundingBoxMin < SubgroupBoundingBoxMax[layerGroupIdx][subgroupIdx] && RoiBoundingBoxMax < SubgroupBoundingBoxMin[layerGroupIdx][subgroupIdx], decode the associated geometry data unit (DGDU) or associated attribute data unit (DADU). Otherwise, skip the associated geometry data unit (DGDU) or associated attribute data unit (DADU).

[0722] if (layerGroupIdx == 0)

[0723] decode GDU or ADU

[0724] else if (RoiBoundingBoxMin < SubgroupBoundingBoxMax[layerGroupIdx][subgroupIdx] &&

[0725] RoiBoundingBoxMax < SubgroupBoundingBoxMin[layerGroupIdx][subgroupIdx])

[0726] decode DGDU or DADU

[0727] else

[0728] skip DGDU or DADU

[0729] As described above, this disclosure proposes a method for using a region-adaptive planar mode based on subgroups / slices. Specifically, this disclosure has described in detail methods for calculating the planarEligibleKOctreeDepth at the transmitter and receiver using the number of points sent on a subgroup basis (numPoints), methods for calculating the planarEligibleKOctreeDepth at the transmitter and receiver using the number of predicted points (numPoints), methods for calculating the planarEligibleKOctreeDepth flag at the transmitter and sending this planarEligibleKOctreeDepth flag to the receiver, and methods for calculating the planarEligibleKOctreeDepth based on the number of output nodes / points on a subgroup basis. This disclosure also describes in detail parts of the decoding process and the FGS decoding preprocessing and post-processing procedures.

[0730] Therefore, the receiving method / apparatus according to the embodiments can provide the following effects.

[0731] This disclosure describes a method for partitioning and transmitting compressed point cloud data based on specific criteria. Specifically, when using layered coding (or hierarchical coding) as an application of this disclosure, compressed data can be partitioned and transmitted according to layers, which can increase the storage and transmission efficiency on the transmitting side.

[0732] Figure 38 This diagram illustrates an example of providing services by compressing the geometry and attributes of point cloud data according to an implementation method. Specifically, in a point cloud compression (PCC) based service, the compression ratio or the number of data points can be adjusted based on receiver performance or transmission environment. Figure 38 As shown, when point cloud data is bundled into a single slice, if receiver performance or transmission environment changes: 1) the bitstream should be pre-converted to suit each environment and stored separately for selection during transmission, or 2) transcoding is required before transmission. In this case, if the number of receiver environments to be supported increases or the transmission environment changes frequently, problems related to storage space or latency caused by conversion may arise.

[0733] Figure 39 This is a diagram illustrating another example of providing services by compressing the geometry and attributes of point cloud data according to an implementation method.

[0734] As proposed in this disclosure, when compressed data is partitioned and delivered according to layers, the following advantages exist: for pre-compressed data, only the necessary portions can be selectively delivered at the bitstream stage via a bitstream selector without additional conversion processes. This scheme is efficient in terms of storage space, as only one storage space is required per stream. Furthermore, since the bitstream selector selects only the necessary layers before transmission, efficient transmission in terms of bandwidth can be achieved.

[0735] From the receiver's perspective, regarding the effects of the features of this disclosure, when using hierarchical coding (or layered coding) as an application of this disclosure, compressed data can be divided and transmitted according to layers. In this case, the efficiency of the receiving side can be improved. Specifically, although applying scalable attribute coding will lead to latency and computational burden on the receiver because the entire encoded geometry must be received and decoded, the proposal of this disclosure allows decoding only the geometry that matches the tree level used in scalable attribute coding, thereby reducing latency factors and increasing decoder efficiency by saving the computational power required for decoding.

[0736] Figure 40 This diagram illustrates the operations of the transmitting and receiving sides when transmitting point cloud data consisting of layers. In this regard, if information for reconstructing the entire point cloud data is delivered regardless of the receiver's performance, the receiver needs to perform a process of decoding the reconstructed point cloud data and then selecting only the point cloud data corresponding to the necessary layers (e.g., data selection or subsampling). Since the delivered bitstream is decoded in this case, it can lead to latency in receivers designed for low latency, or, depending on the receiver's performance, decoding may not be performed at all.

[0737] However, as proposed, when receiving only the necessary compressed data according to the layer, the receiver is allowed to perform selective decoding of specific layers. Therefore, decoder efficiency can be increased and decoders with various performance levels can be supported.

[0738] Figure 41 This is a flowchart illustrating a point cloud data transmission method according to an implementation method.

[0739] The point cloud data transmission method according to the embodiment may include acquiring point cloud data (71001), encoding the point cloud data (71002), and transmitting the encoded point cloud data and signaling information (71003). In this case, the bit stream containing the encoded point cloud data and signaling information can be encapsulated into a file and transmitted.

[0740] In the operation of acquiring point cloud data (71001), the following can be executed: Figure 1 Some or all of the operations of the point cloud video acquisition unit 10001, or can be executed Figure 8Part or all of the operations of the data input unit 8000.

[0741] In the operation of encoding point cloud data (71002), the following can be performed: Figure 1 Point cloud video encoder 10002, Figure 2 The code 20001 Figure 3 Point cloud video encoder, Figure 8 Point cloud video encoder, Figure 24 encoder, Figure 25 Attribute encoding and Figure 27 The geometry and attribute encoder performs some or all of its operations to encode geometric and attribute information.

[0742] The operation of encoding point cloud data (71002) according to the implementation method may include compressing geometric information related to the input point cloud data and compressing attribute information.

[0743] According to the implementation, compressed geometry information may include compressed geometry information based on layer groups and / or subgroups. Slices used for geometry may be divided into one or more FGS and compressed. In this case, subgroups may correspond to FGS. Furthermore, based on the density of points at a specific depth (or layer or level) of the occupies the tree, it can be determined whether a planar pattern is used at that depth. The compression of geometry information and FGS segmentation have been described in detail above, so their redundant descriptions will be omitted here.

[0744] The compressed geometric information is entropy encoded and then output as a geometric bitstream.

[0745] According to implementation methods, compressing attribute information may include compressing the attribute information based on location and / or reconstructed geometric information that has not yet undergone geometric encoding. In one implementation, the attribute information may be encoded using any one or a combination of two or more of RAHT coding, LOD-based predictive transform coding, and lifting transform coding. Slices for attributes may be divided into one or more FGS and compressed. In this case, subgroups may correspond to FGS.

[0746] The compressed attribute information is entropy encoded and then output as an attribute bitstream.

[0747] In this specification, signaling information may include layer-group-based slice-related information for encoding geometric and attribute information on a layer group and / or subgroup basis, and for decoding such geometric and attribute information at the receiving end.

[0748] Layer-group-based slice-related information may be included in at least one of SPS, APS, LGSI, geometric data cell header (or associated geometric data cell header), and / or attribute data cell header (or associated attribute data cell header), and may be sent to the receiving side. For details regarding the inclusion of layer-group-based slice-related information, please refer to... Figures 29 to 33 as well as Figure 34 The Figure 37 Its description is omitted here.

[0749] Figure 42 A flowchart illustrating a point cloud data receiving method according to an embodiment is provided.

[0750] The point cloud data receiving method according to the implementation method may include receiving encoded point cloud data and signaling information (81001), decoding the point cloud data based on the signaling information (81002), and rendering the decoded point cloud data (81003).

[0751] The receiving point cloud data and signaling information (81001) according to the implementation method can be provided by Figure 1 Receiver 10005 Figure 2 Transmission 20002 or decoding 20003, or Figure 9 The receiver 9000 or receiver processor 9001 executes this.

[0752] Decoding the point cloud data according to the implementation method (81002) can be performed by executing... Figure 1 Point cloud video decoder 10006 Figure 2 Decoding 20003 Figure 8 Point cloud video decoder, Figure 9 Point cloud video decoder, Figure 24 decoder Figure 26 Decoding or Figure 28 The geometry decoder and attribute decoder perform some or all of their operations to decode the geometric and attribute information.

[0753] Decoding point cloud data according to the implementation method (81002) includes decoding geometric information and decoding attribute information.

[0754] Decoding geometric information can include decoding (i.e., reconstructing) the geometric information based on layer groups and / or subgroups, using slice-related information based on layer groups included in the signaling information. When the slice used for geometry is divided into one or more FGS and encoded on the transmitting side, decoding the geometric information can be based on the FGS to decode (i.e., reconstruct) the geometry. In this case, the subgroup can correspond to the FGS. Furthermore, whether to use a planar mode at a specific depth (or layer or level) of the occupies the tree can be determined based on the density of points at that depth. A detailed description of the decoding of geometric information has been sufficiently provided above, so its redundant description will be omitted here.

[0755] Decoding attribute information involves decoding (i.e., decompressing) the attribute information based on layer groups and / or subgroups, using the recovered geometric information and slice-related information included in the signaling information. In one implementation, attribute information can be decoded using any one or a combination of two or more of RAHT coding, LOD-based predictive transform coding, and lifting transform coding. When the slice used for attributes is divided into one or more FGSs and encoded on the transmitting side, decoding of attribute information can be based on FGS (i.e., recovering) the attribute information. In this case, subgroups may correspond to FGSs. A detailed description of attribute information decoding has been sufficiently provided above, and therefore its redundant description will be omitted here.

[0756] The rendering (81003) according to the embodiment may include reconstructing point cloud data based on reconstructed geometric and attribute information, and performing rendering according to various rendering methods. For example, points in the point cloud content may be rendered as vertices with a specific thickness, cubes with a specific minimum size centered at vertex locations, or circles centered at vertex locations. All or part of the rendered point cloud content is provided to the user through a display (e.g., a VR / AR display, a general display, etc.). The operation of rendering point cloud data (81003) according to the embodiment can be performed by… Figure 1 Renderer 10007 Figure 2 Rendering 20004 or Figure 9 The renderer 9011 is executed.

[0757] Each of the above components, modules, or units can be a software, processor, or hardware component that performs a series of processes stored in a memory (or storage unit). Each of the steps described in the above embodiments can be executed by a processor, software component, or hardware component. Each module / block / unit described in the above embodiments can operate as a processor, software, or hardware. Furthermore, the methods presented by the embodiments can be executed as code. This code can be written to a processor-readable storage medium and thus read by a processor provided by the device.

[0758] In this specification, when a component “comprises” or “includes” an element, unless otherwise stated, it means that the component also includes or comprises another element. Furthermore, the term “…module (or unit)” disclosed in this specification refers to a unit for performing at least one function or operation, and can be implemented by hardware, software, or a combination of hardware and software.

[0759] Although embodiments have been explained with reference to each of the accompanying drawings for simplicity, new embodiments can be designed by incorporating the embodiments illustrated in the drawings. If a person skilled in the art designs a computer-readable recording medium containing a program for performing the embodiments described above, it may fall within the scope of the appended claims and their equivalents.

[0760] The apparatus and methods are not limited to the configurations and methods of the above embodiments. The above embodiments can be configured by selectively or wholly combining with each other to achieve various modifications.

[0761] Although preferred embodiments of the embodiments have been shown and described, the embodiments are not limited to the specific embodiments described above, and various modifications can be made by those skilled in the art without departing from the spirit of the embodiments claimed in the claims, and these modifications should not be understood in isolation from the technical concept or view of the embodiments.

[0762] Various elements of the device according to the embodiments can be implemented by hardware, software, firmware, or a combination thereof. Various elements in the embodiments can be implemented by a single chip (e.g., a single hardware circuit). According to the embodiments, the components according to the embodiments can be implemented as separate chips. According to the embodiments, at least one or more components of the device according to the embodiments can include one or more processors capable of executing one or more programs. One or more programs can perform any one or more operations / methods according to the embodiments or include instructions for performing them. Executable instructions for performing the methods / operations of the device 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 transient CRM or other computer program product configured to be executed by one or more processors. Additionally, the memory according to the embodiments can be used to encompass not only volatile memory (e.g., RAM) but also the concepts of non-volatile memory, flash memory, and PROM. It can also be implemented in the form of a carrier wave (e.g., transmission via the Internet). Furthermore, the processor-readable recording medium can be distributed to computer systems connected via a network, such that processor-readable code can be stored and executed in a distributed manner.

[0763] In this disclosure, the terms “ / ” and “,” should be interpreted as indicating “and / or”. For example, the expression “A / B” can mean “A and / or B”. Furthermore, “A, B” can mean “A and / or B”. Additionally, “A / B / C” can mean “at least one of A, B, and / or C”. Also, “A / B / C” can mean “at least one of A, B, and / or C”. Furthermore, in this document, the term “or” should be interpreted as indicating “and / or”. For example, the expression “A or B” can include 1) only A, 2) only B, and / or 3) both A and B. In other words, the term “or” in this disclosure should be interpreted as indicating “additionally or alternatively”.

[0764] The various elements of the embodiments can be implemented by hardware, software, firmware, or a combination thereof. The various elements of the embodiments can be executed by a single chip, such as a single hardware circuit. According to the embodiments, the elements can be selectively executed by separate chips. According to the embodiments, at least one of the elements of the embodiments can be executed in one or more processors including instructions for performing the operations according to the embodiments.

[0765] Operations according to the embodiments described in this specification can be performed by a transmitting / receiving device including one or more memories and / or one or more processors according to the embodiments. One or more memories may store programs for processing / controlling the operations according to the embodiments, and one or more processors may control the various operations described in this specification. One or more processors may be referred to as controllers, etc. In the embodiments, operations can be performed by firmware, software, and / or combinations thereof. Firmware, software, and / or combinations thereof may be stored in a processor or memory.

[0766] 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. These terms are used only to distinguish one element from another. 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 without departing from the scope of the various embodiments. Both the first user input signal and the second user input signal are user input signals, but they do not mean the same user input signal unless the context clearly specifies otherwise.

[0767] The terminology used to describe embodiments is for the purpose of describing particular embodiments only and is not intended to limit the embodiments. As used in the description of embodiments and claims, the singular form includes the plural of the referred objects unless the context clearly specifies otherwise. The expression “and / or” is used to include all possible combinations of terms. Terms such as “comprising” or “having” are intended to indicate the presence of figures, quantities, steps, elements, and / or components and should be understood not to exclude the possibility of additional figures, quantities, steps, elements, and / or components. 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 or interpreting a related definition based on a specific condition when that condition is met. Embodiments may include variations / modifications within the scope of the claims and their equivalents.

[0768] Invention Model

[0769] As described above, the relevant content is presented in the best mode for implementing the embodiment.

[0770] Industrial applicability

[0771] As described above, the embodiments can be applied in whole or in part to point cloud data transmitting / receiving apparatuses and systems. It will be apparent to those skilled in the art that various changes or modifications can be made to the embodiments within their scope. Therefore, the embodiments are intended to cover modifications and variations as long as they fall within the scope of the appended claims and their equivalents.

Claims

1. A decoding method, the decoding method comprising the following steps: Receives geometric data, attribute data, and signaling information; Decode the geometric data; as well as The attribute data is decoded.

2. The decoding method according to claim 1, wherein, The steps for decoding the attribute data include: Generate a geometric tree with multiple depths; and The geometric data is decoded based on the geometric tree, and The decoding includes determining whether each depth of the geometry tree is planar qualified and decoding the geometric data at the depths determined to be planar qualified based on the planar pattern.

3. The decoding method according to claim 2, wherein, Whether a depth is planar qualified is determined based on the density of points at that depth.

4. The decoding method according to claim 3, wherein, The density of the points is determined based on at least one of the following: the number of points encoded in the data unit of the geometric data including the depth, the number of points encoded in the direct nodes, and / or the number of points present at the depth.

5. The decoding method according to claim 3, wherein, The density of the points is determined based on information specifying the plane qualification included in the signaling information.

6. The decoding method according to claim 2, wherein, The decoding includes: Decoding the geometric data based on subgroups; and Determine whether each depth has the stated plane qualification based on the subgroup, and Each subgroup is identified by a subgroup index.

7. A decoding device, the decoding device comprising: Memory; as well as At least one processor, said at least one processor being connected to the memory, The at least one processor is configured to receive geometric data, attribute data, and signaling information, decode the geometric data, and decode the attribute data.

8. The decoding apparatus according to claim 7, wherein, The processor includes a geometry decoder configured to decode the geometry data. The geometry decoder is configured to generate a geometry tree with multiple depths and to decode the geometry data based on the geometry tree. The geometry decoder is configured to determine whether each depth of the geometry tree is planar qualified and to decode the geometry data at the depths determined to be planar qualified based on a planar pattern.

9. The decoding apparatus according to claim 8, wherein, Whether a depth is planar qualified is determined based on the density of points at that depth.

10. The decoding apparatus according to claim 9, wherein, The density of the points is determined based on at least one of the following: the number of points encoded in the data unit of the geometric data including the depth, the number of points encoded in the direct nodes, and / or the number of points present at the depth.

11. The decoding apparatus according to claim 9, wherein, The density of the points is determined based on information specifying the plane qualification included in the signaling information.

12. The decoding apparatus according to claim 8, wherein, The geometry decoder is configured to decode the geometry data on a subgroup basis, and to determine whether each depth has the plane qualification on a subgroup basis. Each subgroup is identified by a subgroup index.

13. An encoding method, the encoding method comprising the following steps: Encode the geometric data; Encode the attribute data; as well as Transmit the encoded geometric data, the encoded attribute data, and signaling information.

14. The encoding method according to claim 13, wherein, The steps for encoding the geometric data include: Generate a geometric tree with multiple depths; and The geometric data is encoded based on the geometric tree, and The encoding includes determining whether each depth of the geometry tree is planar qualified and encoding the geometric data at the depths determined to be planar qualified based on a planar pattern.

15. The encoding method according to claim 14, wherein, Whether a depth is planar qualified is determined based on the density of points at that depth.

16. A non-transitory computer-readable storage medium storing a bit stream generated by the method according to claim 13.

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