Point cloud data encoding device, point cloud data encoding method, point cloud data decoding device, and point cloud data decoding method

Abstract

A method according to embodiments may comprise the steps of: decoding geometry data of point cloud data within a bitstream; and decoding attribute data of the point cloud data. A method according to embodiments may comprise the steps of: encoding geometry data of point cloud data; and encoding attribute data of the point cloud data.

Description

Point cloud data encoding device, point cloud data encoding method, point cloud data decoding device and point cloud data decoding method

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

[0002] Point cloud content is content represented as a point cloud, which is a set of points belonging to a coordinate system that represents three-dimensional space. Point cloud content can represent three-dimensional media and is used to provide various services such as VR (Virtual Reality), AR (Augmented Reality), MR (Mixed Reality), and autonomous driving services. However, representing point cloud content requires tens of thousands to hundreds of thousands of point data points. Therefore, a method is required to efficiently process a vast amount of point data.

[0003] The embodiments provide an apparatus and a method for efficiently processing point cloud data. The embodiments provide a method and apparatus for processing point cloud data to address latency and encoding / decoding complexity.

[0004] However, the scope of rights of the embodiments is not limited to the technical problems described above, and may be extended to other technical problems that a person skilled in the art can infer based on the entire content described.

[0005] A method according to the embodiments may include the step of decoding geometry data of point cloud data within a bitstream; and the step of decoding attribute data of point cloud data. A method according to the embodiments may include the step of encoding geometry data of point cloud data; and the step of encoding attribute data of point cloud data.

[0006] The device and method according to the embodiments can process point cloud data with high efficiency.

[0007] The device and method according to the embodiments can provide a high-quality point cloud service.

[0008] The device and method according to the embodiments can provide point cloud content for providing general-purpose services such as VR services and autonomous driving services.

[0009] Drawings are included to further understand the embodiments, and the drawings illustrate the embodiments along with descriptions related to the embodiments. For a better understanding of the various embodiments described below, one must refer to the description of the embodiments below in relation to the following drawings, which include parts corresponding to similar reference numerals throughout the drawings.

[0010] FIG. 1 shows an example of a point cloud content provision system according to embodiments.

[0011] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0012] FIG. 3 shows an example of a point cloud encoder according to embodiments.

[0013] FIG. 4 shows examples of octree and occupancy codes according to embodiments.

[0014] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0015] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0016] FIG. 7 shows an example of a point cloud decoder according to embodiments.

[0017] FIG. 8 is an example of a transmission device according to embodiments.

[0018] FIG. 9 is an example of a receiving device according to embodiments.

[0019] FIG. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0020] FIG. 11 illustrates the encoding and decoding process of point cloud data according to embodiments.

[0021] FIG. 12 shows a bitstream fragment according to embodiments.

[0022] FIG. 13 illustrates a layer-based bitstream fragment encoding according to embodiments.

[0023] FIG. 14 illustrates layer-based bitstream fragment decoding according to embodiments.

[0024] FIG. 15 shows the syntax of a NAL unit of a bitstream according to embodiments.

[0025] FIG. 16 shows the syntax of the header of the NAL unit of the bitstream according to the embodiments.

[0026] FIG. 17 shows the syntax of a sequence parameter set (SPS) in a bitstream according to embodiments.

[0027] FIG. 18 shows the syntax of a set of geometry parameters (GPS) in a bitstream according to embodiments.

[0028] FIG. 19 shows the syntax of an attribute parameter set (APS) in a bitstream according to embodiments.

[0029] FIG. 20 shows NAL unit-based encoding according to embodiments.

[0030] FIG. 21 illustrates NAL unit-based decoding according to embodiments.

[0031] FIG. 22 shows the relationship between the slice and FGS structures according to the embodiments.

[0032] FIG. 23 shows an FGS according to the embodiments.

[0033] FIG. 24 shows an FGS structure according to the embodiments.

[0034] FIG. 25 illustrates an example of partial decoding using a layer group and subgroup structure according to embodiments.

[0035] FIG. 26 shows examples of multi-resolution and multi-ROI according to embodiments.

[0036] FIG. 27 illustrates a scalable G-PCC bitstream transmission process according to embodiments.

[0037] FIG. 28 illustrates a layer group slicing-based decoding procedure according to embodiments.

[0038] FIGS. 29a and FIGS. 29b show the SPS (sequence parameter set) syntax in a bitstream according to the embodiments.

[0039] FIG. 30 shows the dependent geometry data unit header syntax within a bitstream according to embodiments.

[0040] FIG. 31 shows geometry octree coding according to embodiments.

[0041] FIG. 32 shows the APS (attribute parameter set) syntax in a bitstream according to embodiments.

[0042] FIG. 33 shows ASP syntax in a bitstream according to embodiments.

[0043] FIG. 34 shows the syntax of a dependent attribute data unit header within a bitstream according to embodiments.

[0044] FIG. 35 shows the delivery sequence of FSG according to the embodiments.

[0045] FIG. 36 shows the FGS delivery sequence according to the embodiments.

[0046] FIG. 37 shows the FGS delivery sequence according to the embodiments.

[0047] FIG. 38 shows the FGS sequence according to the embodiments.

[0048] FIG. 39 shows the FGS sequence according to the embodiments.

[0049] FIG. 40 shows the FGS sequence according to the embodiments.

[0050] FIG. 41 shows the syntax of a sequence parameter set (SPS) of a bitstream according to embodiments.

[0051] FIG. 42 shows the syntax of the FGS parameters of the bitstream according to the embodiments.

[0052] FIG. 43 shows slice boundary markers of a bitstream according to embodiments.

[0053] FIG. 44 illustrates partial encoding and partial decoding processes according to embodiments.

[0054] FIG. 45 illustrates partial encoding and partial decoding processes according to embodiments.

[0055] FIG. 46 illustrates scalable encoding and scalable decoding according to embodiments.

[0056] FIG. 47 illustrates partial encoding and partial decoding according to embodiments.

[0057] FIG. 48 illustrates a encoding method according to embodiments.

[0058] FIG. 49 illustrates a decoding method according to embodiments.

[0059] Preferred embodiments of the embodiments are described in detail, and examples thereof are shown in the accompanying drawings. The following detailed description, with reference to the accompanying drawings, is intended to describe preferred embodiments of the embodiments rather than merely embodiments that may be implemented according to the embodiments. The following detailed description includes details to provide a thorough understanding of the embodiments. However, it is obvious to those skilled in the art that the embodiments may be practiced without these details.

[0060] Most terms used in the embodiments are selected from those commonly used in the field, but some terms are chosen at the applicant's discretion, and their meanings are described in detail in the following description as necessary. Accordingly, the embodiments should be understood based on the intended meaning of the terms, rather than their mere names or meanings.

[0061] FIG. 1 shows an example of a point cloud content provision system according to embodiments.

[0062] The point cloud content providing system illustrated in FIG. 1 may include a transmission device (10000) and a reception device (10004). The transmission device (10000) and the reception device (10004) can communicate via wired or wireless means to transmit and receive point cloud data.

[0063] A transmission device (10000) according to embodiments can acquire, process, and transmit point cloud video (or point cloud content). According to embodiments, 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 server, etc. Additionally, according to embodiments, the transmission device (10000) may include a device that communicates with a base station and / or other wireless devices using wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)), a robot, a vehicle, an AR / VR / XR device, a mobile device, a home appliance, an Internet of Things (IoT) device, an AI device / server, etc.

[0064] A transmission device (10000) according to embodiments includes a point cloud video acquisition unit (10001), a point cloud video encoder (10002), and / or a transmitter (or communication module), 10003.

[0065] A point cloud video acquisition unit (10001) according to the embodiments acquires a point cloud video through processing steps such as capture, synthesis, or generation. The point cloud video is a point cloud content represented as a point cloud, which is a set of points located in a three-dimensional space, and may be referred to as point cloud video data, etc. The point cloud video according to the embodiments may include one or more frames. A frame represents a still image / picture. Accordingly, the point cloud video may include a point cloud image / frame / picture and may be referred to as any one of a point cloud image, a frame, and a picture.

[0066] A point cloud video encoder (10002) according to the embodiments encodes the obtained 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 the embodiments may include Geometry-based Point Cloud Compression (G-PCC) coding and / or Video-based Point Cloud Compression (V-PCC) coding or next-generation coding. Furthermore, the point cloud compression coding according to the embodiments is not limited to the embodiments described above. The point cloud video encoder (10002) can output a bitstream containing the encoded point cloud video data. The bitstream may include not only the encoded point cloud video data but also signaling information related to the encoding of the point cloud video data.

[0067] A transmitter (10003) according to the embodiments transmits a bitstream containing encoded point cloud video data. The bitstream according to the embodiments is encapsulated into a file or segment (e.g., a streaming segment) and transmitted through various networks such as a broadcast network and / or a broadband network. Although not illustrated in the drawings, the transmission device (10000) may include an encapsulation unit (or encapsulation module) that performs an encapsulation operation. Additionally, according to the embodiments, the encapsulation unit may be included in the transmitter (10003). According to the embodiments, the file or segment may be transmitted to a receiving device (10004) via a network or stored on a digital storage medium (e.g., USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.). The transmitter (10003) according to the embodiments can communicate wired or wirelessly with the receiving device (10004) (or receiver (10005)) via a network such as 4G, 5G, or 6G. Additionally, the transmitter (10003) can perform necessary data processing operations according to a network system (e.g., a communication network system such as 4G, 5G, 6G, etc.). Additionally, the transmission device (10000) can transmit encapsulated data according to an on-demand method.

[0068] A receiving device (10004) according to embodiments includes a receiver (10005), a point cloud video decoder (10006), and / or a renderer (10007). According to embodiments, the receiving device (10004) may include a device, robot, vehicle, AR / VR / XR device, mobile device, home appliance, IoT (Internet of Thing) device, AI device / server, etc., that communicates with a base station and / or other wireless device using wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)).

[0069] A receiver (10005) according to the embodiments receives a bitstream containing point cloud video data or a file / segment containing the bitstream from a network or a storage medium. The receiver (10005) can perform necessary data processing operations according to a network system (e.g., a communication network system such as 4G, 5G, 6G, etc.). The receiver (10005) according to the embodiments can output a bitstream by decapsulating the received file / segment. Additionally, according to the embodiments, the receiver (10005) may include a decapsulation unit (or decapsulation module) for performing a decapsulation operation. Additionally, the decapsulation unit may be implemented as an element (or component) separate from the receiver (10005).

[0070] A point cloud video decoder (10006) decodes a bitstream containing point cloud video data. The point cloud video decoder (10006) can decode the point cloud video data according to the way the point cloud video data is encoded (e.g., the reverse process of the operation of a point cloud video encoder (10002)). Accordingly, the point cloud video decoder (10006) can decode the point cloud video data by performing point cloud decompression coding, which is the reverse process of point cloud compression. Point cloud decompression coding includes G-PCC coding.

[0071] The renderer (10007) renders the decoded point cloud video data. The renderer (10007) can render not only the point cloud video data but also audio data to output point cloud content. According to embodiments, the renderer (10007) may include a display for displaying the point cloud content. According to embodiments, the display may not be included in the renderer (10007) but may be implemented as a separate device or component.

[0072] The arrows indicated by dotted lines in the drawing represent the transmission path of feedback information obtained from the receiving device (10004). The feedback information is information intended to reflect interaction with a user consuming point cloud content, and includes user information (e.g., head orientation information), viewport information, etc. In particular, if the point cloud content is content for a service requiring interaction with a user (e.g., autonomous driving service, etc.), the feedback information may be transmitted to the content transmission side (e.g., transmission device (10000)) and / or the service provider. Depending on the embodiments, the feedback information may be used in the receiving device (10004) as well as the transmission device (10000), or it may not be provided.

[0073] Head orientation information according to the embodiments is information regarding the user's head position, direction, angle, movement, etc. The receiving device (10004) according to the embodiments 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 looking at. The viewpoint refers to the point where the user is looking at the point cloud video, and may mean the exact center point of the viewport area. That is, the viewport is an 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 in addition to the head orientation information. In addition, the receiving device (10004) performs gaze analysis, etc., to check the user's point cloud consumption method, the point cloud video area the user is looking at, the gaze time, etc. According to embodiments, the receiving device (10004) may transmit feedback information including gaze analysis results to the transmitting device (10000). According to embodiments, the feedback information may be obtained during the rendering and / or display process. According to embodiments, the feedback information may be obtained by one or more sensors included in the receiving device (10004). Also, according to embodiments, the feedback information may be obtained by the renderer (10007) or a separate external element (or device, component, etc.). The dotted line in FIG. 1 indicates the process of transmitting the feedback information obtained from the renderer (10007). The point cloud content providing system may process (encode / decode) point cloud data based on the feedback information. Accordingly, the point cloud video data decoder (10006) may perform a decoding operation based on the feedback information.Additionally, the receiving device (10004) can transmit feedback information to the transmitting device (10000). The transmitting device (10000) (or the point cloud video data encoder (10002)) can perform an encoding operation based on the feedback information. Thus, 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 without processing (encoding / decoding) all point cloud data, and provide point cloud content to the user.

[0074] According to embodiments, the transmission device (10000) may be referred to as an encoder, transmission device, transmitter, etc., and the receiving device (10004) may be referred to as a decoder, receiving device, receiver, etc.

[0075] Point cloud data processed in the point cloud content providing system of FIG. 1 according to embodiments (processed through a series of processes of acquisition / encoding / transmission / decoding / rendering) may be referred to as point cloud content data or point cloud video data. According to embodiments, point cloud content data may be used as a concept including metadata or signaling information related to point cloud data.

[0076] The elements of the point cloud content delivery system illustrated in FIG. 1 can be implemented in hardware, software, processors, and / or combinations thereof.

[0077] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0078] The block diagram of FIG. 2 illustrates the operation of the point cloud content provision system described in FIG. 1. As described above, the point cloud content provision system can process point cloud data based on point cloud compression coding (e.g., G-PCC).

[0079] A point cloud content providing system according to the embodiments (e.g., a point cloud transmission device (10000) or a point cloud video acquisition unit (10001)) can acquire a point cloud video (20000). The point cloud video is represented as a point cloud belonging to a coordinate system representing a three-dimensional space. The point cloud video according to the embodiments may include a Ply (Polygon File format or the Stanford Triangle format) file. If the point cloud video has one or more frames, the acquired point cloud video may include one or more Ply files. The Ply file contains point cloud data such as the geometry and / or attributes of the points. The geometry includes the positions of the points. The position of each point may be represented by parameters (e.g., values ​​of the X-axis, Y-axis, and Z-axis, respectively) representing a three-dimensional coordinate system (e.g., a coordinate system consisting of XYZ axes). Attributes include attributes of points (e.g., texture information, color (YCbCr or RGB), reflectance (r), transparency, etc. of each point). A point has one or more attributes (or properties). For example, a point may have one attribute which is color, or two attributes which are color and reflectance. According to embodiments, geometry may be referred to as positions, geometry information, geometry data, etc., and attributes may be referred to as attributes, attribute information, attribute data, etc.In addition, a point cloud content provision system (e.g., a point cloud transmission device (10000) or a point cloud video acquisition unit (10001)) can obtain point cloud data from information related to the acquisition process of point cloud video (e.g., depth information, color information, etc.).

[0080] A point cloud content providing system (e.g., a transmission device (10000) or a point cloud video encoder (10002)) according to embodiments can encode point cloud data (20001). The point cloud content providing system can encode point cloud data based on point cloud compression coding. As described above, point cloud data may include geometry and attributes of points. Accordingly, the point cloud content providing system can output a geometry bitstream by performing geometry encoding to encode geometry. The point cloud content providing system can output an attribute bitstream by performing attribute encoding to encode attributes. According to embodiments, the point cloud content providing system can perform attribute encoding based on geometry encoding. The geometry bitstream and attribute bitstream according to embodiments can be multiplexed and output as a single bitstream. The bitstream according to the embodiments may further include signaling information related to geometry encoding and attribute encoding.

[0081] A point cloud content providing system according to embodiments (e.g., a transmission device (10000) or a transmitter (10003)) can transmit encoded point cloud data (20002). As described in FIG. 1, the encoded point cloud data can be represented as a geometry bitstream and an attribute bitstream. Additionally, the encoded point cloud data can be transmitted in the form of a bitstream along with signaling information related to the encoding of the point cloud data (e.g., signaling information related to geometry encoding and attribute encoding). Additionally, the point cloud content providing system can encapsulate the bitstream transmitting the encoded point cloud data and transmit it in the form of a file or segment.

[0082] A point cloud content providing system according to embodiments (e.g., a receiving device (10004) or a receiver (10005)) can receive a bitstream containing encoded point cloud data. Additionally, the point cloud content providing system (e.g., a receiving device (10004) or a receiver (10005)) can demultiplex the bitstream.

[0083] A point cloud content providing system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can decode encoded point cloud data (e.g., a geometry bitstream, an attribute bitstream) transmitted as a bitstream. A point cloud content providing system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can decode point cloud video data based on signaling information related to the encoding of point cloud video data included in the bitstream. A point cloud content providing system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can decode the geometry bitstream to restore the positions (geometry) of the points. A point cloud content providing system can decode the attribute bitstream based on the restored geometry to restore the attributes of the points. A point cloud content delivery system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can restore a point cloud video based on positions according to the restored geometry and decoded attributes.

[0084] A point cloud content providing system according to embodiments (e.g., a receiving device (10004) or a renderer (10007)) can render decoded point cloud data (20004). The point cloud content providing system (e.g., a receiving device (10004) or a renderer (10007)) can render geometry and attributes decoded through a decoding process according to various rendering methods. Points of the point cloud content may be rendered as vertices having a certain thickness, cubes having a specific minimum size with the vertex location as the center, or circles with the vertex location as the center, etc. All or part of the rendered point cloud content is provided to a user through a display (e.g., a VR / AR display, a general display, etc.).

[0085] A point cloud content providing system (e.g., a receiving device (10004)) according to the embodiments can obtain feedback information (20005). The point cloud content providing system can encode and / or decode point cloud data based on the feedback information. Since the feedback information and the operation of the point cloud content providing system according to the embodiments are the same as the feedback information and operation described in FIG. 1, a detailed description is omitted.

[0086] FIG. 3 shows an example of a point cloud encoder according to embodiments.

[0087] FIG. 3 shows an example of the point cloud video encoder (10002) of FIG. 1. The point cloud encoder reconstructs point cloud data (e.g., positions and / or attributes of points) and performs encoding operations to adjust the quality of point cloud content (e.g., lossless, lossy, near-lossless) according to network conditions or applications. If the total size of the point cloud content is large (e.g., point cloud content of 60 Gbps in the case of 30 fps), the point cloud content delivery system may not be able to stream the content in real time. Therefore, the point cloud content delivery system may reconstruct the point cloud content based on a maximum target bitrate to provide it according to the network environment.

[0088] As described in FIGS. 1 and 2, the point cloud encoder can perform geometry encoding and attribute encoding. Geometry encoding is performed before attribute encoding.

[0089] The point cloud encoder according to the embodiments comprises a coordinate system transformation unit (Transformation Coordinates, 30000), a quantization unit (Quantize and Remove Points (Voxelize), 30001), an octree analysis unit (Analyze Octree, 30002), a surface approximation analysis unit (Analyze Surface Approximation, 30003), an arithmetic encoder (Arithmetic Encode, 30004), a geometry reconstruction unit (Reconstruct Geometry, 30005), a color transformation unit (Transform Colors, 30006), an attribute transformation unit (Transfer Attributes, 30007), a RAHT transformation unit (30008), an LOD generation unit (Generated LOD, 30009), a lifting transformation unit (Lifting) (30010), and a coefficient quantization unit (Quantize Coefficients, 30011). Includes an and / or arithmetic encoder (Arithmetic Encode, 30012).

[0090] The coordinate system transformation unit (30000), quantization unit (30001), octree analysis unit (30002), surface approximation analysis unit (30003), arismetic encoder (30004), and geometry reconstruction unit (30005) can perform geometry encoding. Geometry encoding according to the embodiments may include octree geometry coding, direct coding, trisoup geometry encoding, and entropy encoding. Direct coding and trisoup geometry encoding are applied optionally or in combination. Additionally, geometry encoding is not limited to the above examples.

[0091] As illustrated in the drawings, the coordinate system conversion unit (30000) according to the embodiments receives positions and converts them into a coordinate system. For example, the positions can be converted into position information in a three-dimensional space (e.g., a three-dimensional space expressed in an XYZ coordinate system). The position information in the three-dimensional space according to the embodiments may be referred to as geometry information.

[0092] The quantization unit (30001) according to the embodiments quantizes the geometry. For example, the quantization unit (30001) can quantize points based on the minimum position values ​​of all points (e.g., minimum values ​​on each axis for the X-axis, Y-axis, and Z-axis). The quantization unit (30001) performs a quantization operation to find the nearest integer value by multiplying the difference between the minimum position value and the position value of each point by a preset quantization scale value and then performing rounding down or rounding up. Thus, one or more points may have the same quantized position (or position value). The quantization unit (30001) according to the embodiments performs voxelization based on the quantized positions to reconstruct the quantized points. Just as the minimum unit containing 2D image / video information is a pixel, the points of the point cloud content (or 3D point cloud video) according to the embodiments may be contained in one or more voxels. A voxel is a combination of volume and pixel, and refers to a three-dimensional cubic space that is generated when a three-dimensional space is divided into units (unit=1.0) based on axes representing the three-dimensional space (e.g., X-axis, Y-axis, Z-axis). The quantization unit (40001) can match groups of points in the three-dimensional space to voxels. According to embodiments, a single voxel may contain only one point. According to embodiments, a single voxel may contain one or more points. In addition, to represent a single voxel as a single point, the position of the center of the voxel can be set based on the positions of one or more points included in the voxel. In this case, the attributes of all positions included in the voxel can be combined and assigned to the voxel.

[0093] The octree analysis unit (30002) according to the embodiments performs octree geometry coding (or octree coding) to represent the voxels in an octree structure. The octree structure represents points matched to the voxels based on an octree structure.

[0094] The surface approximation analysis unit (30003) according to the embodiments can analyze and approximate an octree. The octree analysis and approximation according to the embodiments is a process of analyzing to voxelize an area containing multiple points in order to efficiently provide octree and voxelization.

[0095] An arithmetic encoder (30004) according to the embodiments entropy-encodes an octree and / or an approximated octree. For example, the encoding method includes an arithmetic encoding method. As a result of the encoding, a geometry bitstream is generated.

[0096] The color conversion unit (30006), attribute conversion unit (30007), RAHT conversion unit (30008), LOD generation unit (30009), lifting conversion unit (30010), coefficient quantization unit (30011) and / or arismetic encoder (30012) perform attribute encoding. As described above, a point may have one or more attributes. The attribute encoding according to the embodiments is applied equally to the attributes of a point. However, if a single attribute (e.g., color) includes one or more elements, independent attribute encoding is applied to each element. The attribute encoding according to the embodiments may include color conversion coding, attribute conversion coding, Region Adaptive Hierarchical Transform (RAHT) coding, prediction transformation (Interpolaration-based hierarchical nearest-neighbour prediction-Prediction Transform) coding, and lifting transformation (interpolation-based hierarchical nearest-neighbour prediction with an update / lifting step (Lifting Transform)) coding. Depending on the point cloud content, the above-described RAHT coding, prediction transformation coding, and lifting transformation coding may be used optionally, or a combination of one or more of the codings may be used. Furthermore, the attribute encoding according to the embodiments is not limited to the examples described above.

[0097] The color conversion unit (30006) according to the embodiments performs color conversion coding that converts color values ​​(or textures) included in attributes. For example, the color conversion unit (30006) can convert the format of color information (e.g., convert from RGB to YCbCr). The operation of the color conversion unit (30006) according to the embodiments may be applied optionally depending on the color values ​​included in attributes.

[0098] The geometry reconstruction unit (30005) according to the embodiments reconstructs (decompresses) an octree and / or an approximated octree. The geometry reconstruction unit (30005) reconstructs an octree / voxel based on the results of analyzing the distribution of points. The reconstructed octree / voxel may be referred to as the reconstructed geometry (or restored geometry).

[0099] The attribute transformation unit (30007) according to the embodiments performs attribute transformation that transforms attributes based on positions where geometry encoding has not been performed and / or reconstructed geometry. As described above, since attributes are dependent on geometry, the attribute transformation unit (30007) can transform attributes based on reconstructed geometry information. For example, the attribute transformation unit (30007) can transform the attributes of a point at a position based on the position value of a point included in a voxel. As described above, when the position of the center point of a voxel is set based on the positions of one or more points included in a voxel, the attribute transformation unit (30007) transforms the attributes of one or more points. When trisoop geometry encoding is performed, the attribute conversion unit (30007) can convert attributes based on the trisoop geometry encoding.

[0100] The attribute transformation unit (30007) can perform attribute transformation by calculating the average value of attributes or attribute values ​​(e.g., the color or reflectance of each point) of neighboring points within a specific location / radius from the position (or position value) of the center point of each voxel. The attribute transformation unit (30007) can apply a weight based on the distance from the center point to each point when calculating the average value. Thus, each voxel has a position and a calculated attribute (or attribute value).

[0101] The attribute conversion unit (30007) can search for neighboring points within a specific location / radius from the position of the center point of each voxel based on a KD tree or a Molton code. A KD tree is a binary search tree that supports a data structure capable of managing points based on their positions to enable rapid Nearest Neighbor Search (NNS). A Molton code is generated by representing the coordinate values ​​(e.g., (x, y, z)) representing the 3D positions of all points as bit values ​​and mixing the bits. For example, if the coordinate values ​​representing the position of a point are (5, 9, 1), the bit values ​​of the coordinate values ​​are (0101, 1001, 0001). When the bit values ​​are mixed according to the bit indices in the order of z, y, and x, it becomes 010001000111. When this value is represented in decimal, it becomes 1095. That is, the Molton code value of the point with coordinates (5, 9, 1) is 1095. The attribute transformation unit (30007) sorts the points based on the Molton code value and can perform shortest neighbor search (NNS) through a depth-first traversal process. After the attribute transformation operation, if shortest neighbor search (NNS) is required in other transformation processes for attribute coding, a KD tree or Molton code is utilized.

[0102] As shown in the drawing, the converted attributes are input to the RAHT conversion unit (30008) and / or LOD generation unit (30009).

[0103] The RAHT transformation unit (30008) according to the embodiments performs RAHT coding to predict attribute information based on reconstructed geometry information. For example, the RAHT transformation unit (30008) can predict attribute information of a node at an upper level of the octree based on attribute information associated with a node at a sub-level of the octree.

[0104] The LOD generation unit (30009) according to the embodiments generates a Level of Detail (LOD) to perform predictive transformation coding. The LOD according to the embodiments represents the degree of detail of the point cloud content, and indicates that the smaller the LOD value, the lower the detail of the point cloud content, and the larger the LOD value, the higher the detail of the point cloud content. Points can be classified according to the LOD.

[0105] The lifting transformation unit (30010) according to the embodiments performs lifting transformation coding that transforms the attributes of the point cloud based on weights. As described above, the lifting transformation coding may be applied optionally.

[0106] The coefficient quantization unit (30011) according to the embodiments quantizes attribute-coded attributes based on coefficients.

[0107] An arismetic encoder (30012) according to the embodiments encodes quantized attributes based on arismetic coding.

[0108] The elements of the point cloud encoder of FIG. 3 may be implemented in hardware, software, firmware, or a combination thereof, comprising one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing device, although not illustrated in the drawing. One or more processors may perform at least one of the operations and / or functions of the elements of the point cloud encoder of FIG. 3 described above. Additionally, one or more processors may operate or execute a set of software programs and / or instructions for performing the operations and / or functions of the elements of the point cloud encoder of FIG. 3. One or more memories according to the embodiments may include high-speed random access memory and may include non-volatile memory (e.g., one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices).

[0109] FIG. 4 shows examples of octree and occupancy codes according to embodiments.

[0110] As described in FIGS. 1 to 3, a point cloud content providing system (point cloud video encoder (10002)) or a point cloud encoder (e.g., an octree analysis unit (30002)) performs octree geometry coding based on an octree structure (or octree coding) to efficiently manage the area and / or position of a voxel.

[0111] The top of FIG. 4 shows an octree structure. The three-dimensional space of the point cloud content according to the embodiments is represented by the axes of the coordinate system (e.g., X-axis, Y-axis, Z-axis). The octree structure has two poles (0,0,0) and (2 d , 2 d , 2 d It is generated by recursively subdividing the bounding box (cubical axis-aligned bounding box) defined by ). 2d can be set to the value that constitutes the smallest bounding box enclosing all points of the point cloud content (or point cloud video). d represents the depth of the octree. The value of d is determined according to the following equation. In the equation below, (x int n , y int n , z int n ) represents the positions (or position values) of quantized points.

[0112] d =Ceil(Log2(Max(x_n^int,y_n^int,z_n^int,n=1,…,N)+1))

[0113] As illustrated in the middle of the top of Fig. 4, the entire three-dimensional space can be divided into eight spaces according to the division. Each divided space is represented as a cube having six faces. As illustrated in the right of the top of Fig. 4, each of the eight spaces is further divided based on the axes of the coordinate system (e.g., X-axis, Y-axis, Z-axis). Thus, each space is again divided into eight smaller spaces. The divided smaller spaces are also represented as cubes having six faces. This division method is applied until the leaf nodes of the octree become voxels.

[0114] The bottom of Fig. 4 shows the occupancy code of an octree. The occupancy code of an octree is generated to indicate whether each of the eight partitioned spaces resulting from the partitioning of a single space contains at least one point. Therefore, one occupancy code is represented by eight child nodes. Each child node represents the occupancy of the partitioned space, and the child node has a value of 1 bit. Thus, the occupancy code is represented as an 8-bit code. That is, if the space corresponding to the child node contains at least one point, the node has a value of 1. If the space corresponding to the child node does not contain a point (empty), the node has a value of 0. Since the occupancy code shown in Fig. 4 is 00100001, it indicates that the spaces corresponding to the 3rd and 8th child nodes among the eight child nodes each contain at least one point. As illustrated in the drawing, the 3rd child node and the 8th child node each have 8 child nodes, and each child node is represented by an 8-bit Occupancy code. The drawing indicates that the Occupancy code of the 3rd child node is 10000111 and the Occupancy code of the 8th child node is 01001111. A point cloud encoder according to the embodiments (e.g., an arismetic encoder (30004)) can entropy-encode the Occupancy code. Additionally, to increase compression efficiency, the point cloud encoder can intra- / inter-encode the Occupancy code. A receiving device according to the embodiments (e.g., a receiving device (10004) or a point cloud video decoder (10006)) reconstructs the octree based on the Occupancy code.

[0115] A point cloud encoder according to the embodiments (e.g., the point cloud encoder of FIG. 3, or the octree analysis unit (30002)) can perform voxelization and octree coding to store the positions of the points. However, since points in a three-dimensional space are not always evenly distributed, there may be specific areas where few points exist. Therefore, performing voxelization on the entire three-dimensional space is inefficient. For example, if there are almost no points in a specific area, there is no need to perform voxelization up to that area.

[0116] Accordingly, the point cloud encoder according to the embodiments can perform direct coding, which directly codes the positions of points included in a specific region (or nodes excluding leaf nodes of an octree) without performing voxelization on the aforementioned specific region. The coordinates of the points directly coded according to the embodiments are referred to as the Direct Coding Mode (DCM). Additionally, the point cloud encoder according to the embodiments can perform trisoup geometry encoding, which reconstructs the positions of points within a specific region (or node) based on voxels using a surface model. Trisoup geometry encoding is a geometry encoding that represents an object as a series of triangle meshes. Therefore, the point cloud decoder can generate a point cloud from the mesh surface. Direct coding and trisoup geometry encoding according to the embodiments may be performed optionally. In addition, direct coding and trisoop geometry encoding according to the embodiments can be performed in combination with octree geometry coding (or octree coding).

[0117] To perform direct coding, the option to use direct mode for applying direct coding must be enabled, the node to which direct coding is to be applied must not be a leaf node, and there must be points within a specific node that are below a threshold. In addition, the total number of points subject to direct coding must not exceed a preset threshold. If the above conditions are satisfied, the point cloud encoder (or arismetic encoder (30004)) according to the embodiments can entropy-code the positions (or position values) of the points.

[0118] A point cloud encoder according to the embodiments (e.g., a surface approximation analysis unit (30003)) can determine a specific level of an octree (where the level is smaller than the depth d of the octree) and, starting from that level, perform trisoop geometry encoding to reconstruct the position of points within a node region based on voxels using a surface model (trisoop mode). The point cloud encoder according to the embodiments can specify the level to which trisoop geometry encoding is applied. For example, if the specified level is equal to the depth of the octree, the point cloud encoder does not operate in trisoop mode. That is, the point cloud encoder according to the embodiments can operate in trisoop mode only when the specified level is smaller than the depth value of the octree. A three-dimensional cubic region of nodes at a specified level according to the embodiments is referred to as a block. A block may include one or more voxels. A block or a voxel may correspond to a brick. Within each block, geometry is represented as a surface. A surface according to the embodiments may intersect each edge of the block at most once.

[0119] Since one block has 12 edges, there are at least 12 intersection points within one block. Each intersection point is referred to as a vertex. A vertex along an edge is detected if there is at least one occupied voxel adjacent to that edge among all blocks sharing that edge. An occupied voxel according to the embodiments means a voxel containing a point. The position of a vertex detected along an edge is the average position along the edge of all voxels adjacent to that edge among all blocks sharing that edge.

[0120] When a vertex is detected, the point cloud encoder according to the embodiments can entropy-code the edge start point (x, y, z), edge direction vector (Δx, Δy, Δz), and vertex position value (relative position value within the edge). When trisoop geometry encoding is applied, the point cloud encoder according to the embodiments (e.g., geometry reconstruction unit (30005)) can generate restored geometry (reconstructed geometry) by performing triangle reconstruction, up-sampling, and voxelization processes.

[0121] The vertices located on the edges of the block determine the surface passing through the block. The surface according to the embodiments is a non-planar polygon. The triangle reconstruction process reconstructs the surface represented by triangles based on the edge start point, the edge direction vector, and the vertex position value. The triangle reconstruction process is as follows: ① calculate the centroid value of each vertex, ② subtract the centroid value from each vertex value, ③ square the result, and add all the result together.

[0122]

[0123] The minimum sum is calculated, and a projection process is performed along the axis where the minimum value is located. For example, if the x-element is at its minimum, each vertex is projected along the x-axis relative to the center of the block and onto the (y, z) plane. If the value obtained from projecting onto the (y, z) plane is (ai, bi), the θ value is calculated using atan2(bi, ai), and the vertices are aligned based on the θ value. The table below shows the combinations of vertices to generate triangles depending on the number of vertices. The vertices are aligned in order from 1 to n. The table below indicates that for four vertices, two triangles can be formed depending on the combination of vertices. The first triangle can be formed from the 1st, 2nd, and 3rd vertices among the aligned vertices, and the second triangle can be formed from the 3rd, 4th, and 1st vertices among the aligned vertices.

[0124] Table 2-1. Triangles formed from vertices ordered 1,… ,n

[0125] n triangles

[0126] 3 (1,2,3)

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

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

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

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

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

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

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

[0134] 11 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,1,3), (3,5,7), (7,9,11), (11,3,7)

[0135] 12 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,12,1), (1,3,5), (5,7,9), (9,11,1), (1,5,9)

[0136] The upsampling process is performed to voxelize by adding intermediate points along the edges of the triangle. Additional points are generated based on the upsampling factor value and the width of the block. The additional points are referred to as refined vertices. A point cloud encoder according to the embodiments can voxelize the refined vertices. Additionally, the point cloud encoder can perform attribute encoding based on the voxelized positions (or position values).

[0137] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0138] As described in FIGS. 1 to 4, the encoded geometry is reconstructed (decompressed) before attribute encoding is performed. When direct coding is applied, the geometry reconstruction operation may include changing the arrangement of the direct-coded points (e.g., placing the direct-coded points at the front of the point cloud data). When trisoop geometry encoding is applied, the geometry reconstruction process involves triangle reconstruction, upsampling, and voxelization. Since attributes depend on geometry, attribute encoding is performed based on the reconstructed geometry.

[0139] A point cloud encoder (e.g., an LOD generation unit (30009)) can reorganize points by LOD. The drawing shows point cloud content corresponding to the LOD. The left side of the drawing shows the original point cloud content. The second figure from the left of the drawing shows the distribution of points of the lowest LOD, and the rightmost figure of the drawing shows the distribution of points of the highest LOD. That is, the points of the lowest LOD are sparsely distributed, while the points of the highest LOD are densely distributed. In other words, according to the direction of the arrow indicated at the bottom of the drawing, as the LOD increases, the spacing (or distance) between points becomes shorter.

[0140] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0141] As described in FIGS. 1 to 5, a point cloud content providing system or a point cloud encoder (e.g., a point cloud video encoder (10002), the point cloud encoder of FIG. 3, or an LOD generation unit (30009)) can generate an LOD. The LOD is generated by reorganizing points into a set of refinement levels according to a set LOD distance value (or a set of Euclidean distances). The LOD generation process is performed in a point cloud decoder as well as a point cloud encoder.

[0142] The top of Fig. 6 shows examples of points (P0 to P9) of point cloud content distributed in three-dimensional space. The Original Order in Fig. 6 represents the order of points P0 to P9 prior to LOD generation. The LOD-based Order in Fig. 6 represents the order of points following LOD generation. Points are rearranged by LOD. Additionally, higher LODs include points belonging to lower LODs. As illustrated in Fig. 6, LOD0 includes P0, P5, P4, and P2. LOD1 includes the points of LOD0 and P1, P6, and P3. LOD2 includes the points of LOD0, the points of LOD1, and P9, P8, and P7.

[0143] As described in FIG. 3, the point cloud encoder according to the embodiments can perform predictive transform coding, lifting transform coding, and RAHT transform coding selectively or in combination.

[0144] The point cloud encoder according to the embodiments can generate predictors for points and perform predictive transformation coding to set the predicted attribute (or predicted attribute value) of each point. That is, N predictors can be generated for N points. The predictor according to the embodiments can calculate a weight (=1 / distance) value based on the LOD value of each point, indexing information for neighboring points within a set distance per LOD, and the distance value to the neighboring points.

[0145] According to the embodiments, the predicted attribute (or attribute value) is set as the average value of the values ​​obtained by multiplying the attributes (or attribute values, e.g., color, reflectance, etc.) of neighboring points 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 according to the embodiments (e.g., coefficient quantization unit (30011)) can quantize and inverse quantize the residual values ​​(which may be referred to as residual attributes, residual attribute values, attribute prediction residual values, etc.) obtained by subtracting the predicted attribute (attribute value) from the attribute (attribute value) of each point. The quantization process is as shown in the following table.

[0146] graph. Attribute prediction residuals quantization pseudo code

[0147] int PCCQuantization(int value, int quantStep) {

[0148] if( value >=0) {

[0149] return floor(value / quantStep + 1.0 / 3.0);

[0150] } else {

[0151] return -floor(-value / quantStep + 1.0 / 3.0);

[0152] }

[0153] }

[0154] graph. Attribute prediction residuals inverse quantization pseudo code

[0155] int PCCInverseQuantization(int value, int quantStep) {

[0156] if( quantStep ==0) {

[0157] return value;

[0158] } else {

[0159] return value * quantStep;

[0160] }

[0161] }

[0162] A point cloud encoder according to the embodiments (e.g., an arismetic encoder (30012)) can entropy-code the quantized and inversely quantized residual values ​​as described above when there are neighboring points in the predictor of each point. A point cloud encoder according to the embodiments (e.g., an arismetic encoder (30012)) can entropy-code the attributes of the corresponding point without performing the process described above when there are no neighboring points in the predictor of each point.

[0163] A point cloud encoder according to the embodiments (e.g., a lifting transformation unit (30010)) can perform lifting transformation coding by generating a predictor for each point, setting the LOD calculated in the predictor, registering neighboring points, and setting weights based on the distance to neighboring points. The lifting transformation coding according to the embodiments is similar to the prediction transformation coding described above, but differs in that weights are cumulatively applied to attribute values. The process of cumulatively applying weights to attribute values ​​according to the embodiments is as follows.

[0164] 1) Create an array QW (QuantizationWight) to store the weight values ​​of each point. The initial value of all elements in QW is 1.0. Add the value obtained by multiplying the current point's predictor weight by the QW value of the predictor index of the neighboring node registered in the predictor.

[0165] 2) Lift prediction process: To calculate the predicted attribute value, the value obtained by multiplying the point's attribute value by a weight is subtracted from the existing attribute value.

[0166] 3) Create temporary arrays named updateweight and update, and initialize the temporary arrays to 0.

[0167] 4) For all predictors, the calculated weight is additionally multiplied by the weight stored in the QW corresponding to the predictor index, and the resulting weight is accumulated in the update weight array with the neighbor node index. In the update array, the value obtained by multiplying the attribute value of the neighbor node index by the calculated weight is accumulated.

[0168] 5) Lift update process: For all predictors, the attribute value of the update array is divided by the weight value of the update weight array at the predictor index, and the original attribute value is added back to the divided value.

[0169] 6) For all predictors, the predicted attribute value is calculated by additionally multiplying the attribute value updated through the lift update process by the weight (stored in QW) updated through the lift prediction process. A point cloud encoder according to the embodiments (e.g., coefficient quantizer (30011)) quantizes the predicted attribute value. Additionally, a point cloud encoder (e.g., arismetic encoder (30012)) entropies the quantized attribute value.

[0170] A point cloud encoder according to the embodiments (e.g., a RAHT transform unit (30008)) can perform RAHT transform coding to predict attributes of upper-level nodes using attributes associated with nodes at lower levels of the octree. RAHT transform coding is an example of attribute intra-coding through octree backward scanning. A point cloud encoder according to the embodiments scans from voxels to the entire region and repeats the merging process up to the root node, merging voxels into larger blocks at each step. The merging process according to the embodiments is performed only on occupied nodes. The merging process is not performed on empty nodes, and the merging process is performed on the node immediately above the empty node.

[0171] The following equation represents the RAHT transformation matrix. g l x, y, z represents the average attribute value of the voxels at level l. g l x, y, z can be calculated from gl+1 2x, y, z and gl+1 2x+1, y, z. The weights of gl 2x, y, z and gl 2x+1, y, z are w1=wl 2x, y, z and w2=wl 2x+1, y, z.

[0172]

[0173] gl-1 x, y, z are low-pass values ​​used in the merging process at the next higher level. hl-1 x, y, z are high-pass coefficients, and the high-pass coefficients at each step are quantized and entropy-coded (e.g., encoding of an arismetic encoder (400012)). The weights are calculated as wl-1 x, y, z = wl 2x, y, z + wl 2x + 1, y, z. The root node is the last g 1 0, 0, 0 and g 1 0, 0, 1 It is generated as follows through.

[0174]

[0175] The gDC value is also quantized and entropy-coded, just like the high-pass coefficient.

[0176] FIG. 7 shows an example of a point cloud decoder according to embodiments.

[0177] The point cloud decoder illustrated in FIG. 7 is an example of a point cloud decoder and can perform a decoding operation, which is the reverse process of the encoding operation of the point cloud encoder described in FIG. 1 to 6.

[0178] As described in Fig. 1, the point cloud decoder can perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

[0179] A point cloud decoder according to the embodiments comprises an arithmetic decoder (7000), a synthesize octree (7001), a synthesize surface approximation (7002), a reconstruct geometry (7003), an inverse transform coordinates (7004), an arithmetic decoder (7005), an inverse quantize (7006), a RAHT transform (7007), an LOD generater (7008), an inverse lifting (7009), and / or an inverse transform colors (7010).

[0180] An arismetic decoder (7000), an octree composite unit (7001), a surface offset composite unit (7002), a geometry reconstruction unit (7003), and a coordinate system inverse transformation unit (7004) can perform geometry decoding. Geometry decoding according to the embodiments may include direct coding and trisoup geometry decoding. Direct coding and trisoup geometry decoding are applied optionally. Additionally, geometry decoding is not limited to the above examples and is performed as the reverse process of geometry encoding described in FIGS. 1 through 6.

[0181] The arismetic decoder (7000) according to the embodiments decodes the received geometry bitstream based on arismetic coding. The operation of the arismetic decoder (7000) corresponds to the reverse process of the arismetic encoder (30004).

[0182] The octree synthesis unit (7001) according to the embodiments can generate an octree by obtaining an Occupancy code from a decoded geometry bitstream (or information regarding the geometry obtained as a result of decoding). A specific description of the Occupancy code is as described in FIGS. 1 to 6.

[0183] The surface off-relation synthesis unit (7002) according to the embodiments can synthesize a surface based on the decoded geometry and / or the generated octree when trisoop geometry encoding is applied.

[0184] The geometry reconstruction unit (7003) according to the embodiments can regenerate geometry based on a surface and / or decoded geometry. As described in FIGS. 1 through 6, direct coding and trisoop geometry encoding are applied optionally. Accordingly, the geometry reconstruction unit (7003) directly retrieves and adds position information of points to which direct coding has been applied. In addition, when trisoop geometry encoding is applied, the geometry reconstruction unit (7003) can restore geometry by performing reconstruction operations of the geometry reconstruction unit (30005), such as triangle reconstruction, up-sampling, and voxelization operations. Specific details are omitted as they are the same as those described in FIG. 4. The restored geometry may include a point cloud picture or frame that does not contain attributes.

[0185] The coordinate system inverse transformation unit (7004) according to the embodiments can obtain the positions of the points by transforming the coordinate system based on the restored geometry.

[0186] The arismetic decoder (7005), inverse quantization unit (7006), RAHT transformation unit (7007), LOD generation unit (7008), inverse lifting unit (7009), and / or color inverse transformation unit (7010) can perform attribute decoding as described in FIG. 10. Attribute decoding according to the embodiments may include Region Adaptive Hierarchial Transform (RAHT) decoding, Interpolaration-based hierarchical nearest-neighbour prediction-Prediction Transform) decoding, and interpolation-based hierarchical nearest-neighbour prediction with an update / lifting step (Lifting Transform) decoding. The three decodings described above may be used optionally, or a combination of one or more decodings may be used. Furthermore, attribute decoding according to the embodiments is not limited to the examples described above.

[0187] The arismetic decoder (7005) according to the embodiments decodes the attribute bitstream into arismetic coding.

[0188] The inverse quantization unit (7006) according to the embodiments inverse quantizes information about the decoded attribute bitstream or the attribute obtained as a result of decoding and outputs the inverse quantized attributes (or attribute values). Inverse quantization may be optionally applied based on the attribute encoding of the point cloud encoder.

[0189] According to embodiments, the RAHT transformation unit (7007), LOD generation unit (7008), and / or inverse lifting unit (7009) can process the reconstructed geometry and inverse quantized attributes. As described above, the RAHT transformation unit (7007), LOD generation unit (7008), and / or inverse lifting unit (7009) can optionally perform a corresponding decoding operation according to the encoding of the point cloud encoder.

[0190] The color inverse conversion unit (7010) according to the embodiments performs inverse conversion coding to inversely convert the color value (or texture) included in the decoded attributes. The operation of the color inverse conversion unit (7010) may be selectively performed based on the operation of the color conversion unit (30006) of the point cloud encoder.

[0191] The elements of the point cloud decoder of FIG. 7 may be implemented in hardware, software, firmware, or a combination thereof, comprising one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing device, although not illustrated in the drawing. One or more processors may perform at least one of the operations and / or functions of the elements of the point cloud decoder of FIG. 7 described above. Additionally, one or more processors may operate or execute a set of software programs and / or instructions for performing the operations and / or functions of the elements of the point cloud decoder of FIG. 7.

[0192] FIG. 8 is an example of a transmission device according to embodiments.

[0193] The transmission device illustrated in FIG. 8 is an example of the transmission device (10000) of FIG. 1 (or the point cloud encoder of FIG. 3). The transmission device illustrated in FIG. 8 can perform at least one of the same or similar operations and methods as the operations and encoding methods of the point cloud encoder described in FIG. 1 to 6. A transmission device according to embodiments may include a data input unit (8000), a quantization processing unit (8001), a voxelization processing unit (8002), an octree occupancy code generation unit (8003), a surface model processing unit (8004), an intra / inter coding processing unit (8005), an arithmetic coder (8006), a metadata processing unit (8007), a color conversion processing unit (8008), an attribute conversion processing unit (or attribute conversion processing unit) (8009), a prediction / lifting / RAHT conversion processing unit (8010), an arithmetic coder (8011) and / or a transmission processing unit (8012).

[0194] The data input unit (8000) according to the embodiments receives or acquires point cloud data. The data input unit (8000) may perform an operation and / or acquisition method identical or similar to the operation and / or acquisition method of the point cloud video acquisition unit (10001) (or the acquisition process (20000) described in FIG. 2).

[0195] The data input unit (8000), quantization processing unit (8001), voxelization processing unit (8002), octree occupancy code generation unit (8003), surface model processing unit (8004), intra / inter coding processing unit (8005), and arithmetic coder (8006) perform geometry encoding. Since the geometry encoding according to the embodiments is identical or similar to the geometry encoding described in FIGS. 1 to 6, a detailed description is omitted.

[0196] The quantization processing unit (8001) according to the embodiments quantizes geometry (e.g., location values ​​of points, or position values). The operation and / or quantization of the quantization processing unit (8001) is the same or similar to the operation and / or quantization of the quantization unit (30001) described in FIG. 3. The specific description is the same as that described in FIG. 1 through 6.

[0197] The voxelization processing unit (8002) according to the embodiments voxelizes the position values ​​of the quantized points. The voxelization processing unit (80002) may perform the same or similar operation and / or process as the operation and / or voxelization process of the quantization unit (30001) described in FIG. 3. The specific description is the same as that described in FIG. 1 to 6.

[0198] The octree occupancy code generation unit (8003) according to the embodiments performs octree coding on the positions of voxelized points based on an octree structure. The octree occupancy code generation unit (8003) can generate an occupancy code. The octree occupancy code generation unit (8003) can perform operations and / or methods identical or similar to the operations and / or methods of the point cloud encoder (or octree analysis unit (30002)) described in FIGS. 3 and 4. The specific description is the same as that described in FIGS. 1 through 6.

[0199] The surface model processing unit (8004) according to the embodiments can perform trisup geometry encoding that reconstructs the positions of points within a specific region (or node) based on a voxel based on a surface model. The surface model processing unit (8004) can perform operations and / or methods identical or similar to the operations and / or methods of the point cloud encoder (e.g., surface approximation analysis unit (30003)) described in FIG. 3. The specific description is the same as that described in FIG. 1 through 6.

[0200] According to the embodiments, the intra / inter coding processing unit (8005) can intra / inter code point cloud data. The intra / inter coding processing unit (8005) can perform coding identical or similar to the intra / inter coding described in FIG. 7. The specific description is the same as that described in FIG. 7. According to the embodiments, the intra / inter coding processing unit (8005) may be included in an arismetic coder (8006).

[0201] An arismetic coder (8006) according to the embodiments entropy-encodes an octree and / or approximated octree of point cloud data. For example, the encoding method includes an arismetic encoding method. The arismetic coder (8006) performs the same or similar operation and / or method as the arismetic encoder (30004).

[0202] A metadata processing unit (8007) according to the embodiments processes metadata regarding point cloud data, such as setting values, and provides it to necessary processing processes such as geometry encoding and / or attribute encoding. Additionally, a metadata processing unit (8007) according to the embodiments may generate and / or process signaling information related to geometry encoding and / or attribute encoding. The signaling information according to the embodiments may be encoded separately from geometry encoding and / or attribute encoding. Additionally, the signaling information according to the embodiments may be interleaved.

[0203] The color conversion processing unit (8008), attribute conversion processing unit (8009), prediction / lifting / RAHT conversion processing unit (8010), and arithmetic coder (8011) perform attribute encoding. Since the attribute encoding according to the embodiments is identical or similar to the attribute encoding described in FIGS. 1 to 6, a detailed description is omitted.

[0204] The color conversion processing unit (8008) according to the embodiments performs color conversion coding that converts color values ​​included in attributes. The color conversion processing unit (8008) may perform color conversion coding based on reconstructed geometry. The description of the reconstructed geometry is the same as that described in FIGS. 1 through 6. In addition, it performs the same or similar operation and / or method as the operation and / or method of the color conversion unit (30006) described in FIG. 3. A detailed description is omitted.

[0205] The attribute transformation processing unit (8009) according to the embodiments performs attribute transformation that transforms attributes based on positions where geometry encoding has not been performed and / or reconstructed geometry. The attribute transformation processing unit (8009) performs operations and / or methods identical or similar to the operations and / or methods of the attribute transformation unit (30007) described in FIG. 3. A detailed description is omitted. The prediction / lifting / RAHT transformation processing unit (8010) according to the embodiments may code the transformed attributes by RAHT coding, prediction transformation coding, and lifting transformation coding, or a combination thereof. The prediction / lifting / RAHT transformation processing unit (8010) performs at least one of operations identical or similar to the operations of the RAHT transformation unit (30008), LOD generation unit (30009), and lifting transformation unit (30010) described in FIG. 3. In addition, the descriptions of predictive transformation coding, lifting transformation coding, and RAHT transformation coding are the same as those described in Figures 1 to 6, so a detailed description is omitted.

[0206] The arismetic coder (8011) according to the embodiments can encode coded attributes based on arismetic coding. The arismetic coder (8011) performs the same or similar operation and / or method as the operation and / or method of the arismetic encoder (300012).

[0207] A transmission processing unit (8012) according to embodiments may transmit each bitstream containing encoded geometry and / or encoded attributes and metadata information, or may transmit the encoded geometry and / or encoded attributes and metadata information by configuring them into a single bitstream. When the encoded geometry and / or encoded attributes and metadata information according to embodiments is configured into a single bitstream, the bitstream may include one or more sub-bitstreams. The bitstream according to embodiments may include signaling information and slice data, including SPS (Sequence Parameter Set) for sequence-level signaling, GPS (Geometry Parameter Set) for signaling of geometry information coding, APS (Attribute Parameter Set) for signaling of attribute information coding, and TPS (Tile Parameter Set) for tile-level signaling. The slice data may include information about one or more slices. One slice according to embodiments is one geometry bitstream (Geom0 0 ) and one or more attribute bitstreams (Attr0 0 , Attr1 0 It may include ).

[0208] A slice refers to a series of syntax elements representing all or part of a coded point cloud frame.

[0209] According to the embodiments, the TPS may include information regarding each tile (e.g., coordinate value information of a bounding box and height / size information, etc.) for one or more tiles. The geometry bitstream may include a header and a payload. The header of the geometry bitstream according to the embodiments may include identification information of a parameter set included in the GPS (geom_parameter_set_id), a tile identifier (geom_tile_id), a slice identifier (geom_slice_id), and information regarding data included in the payload, etc. As described above, the metadata processing unit (8007) according to the embodiments may generate and / or process signaling information and transmit it to the transmission processing unit (8012). According to the embodiments, the elements performing geometry encoding and the elements performing attribute encoding may share data / information with each other as indicated by the dotted lines. The transmission processing unit (8012) according to the embodiments may perform an operation and / or transmission method identical or similar to the operation and / or transmission method of the transmitter (10003). A detailed explanation is omitted as it is the same as that described in FIGS. 1 and 2.

[0210] FIG. 9 is an example of a receiving device according to embodiments.

[0211] The receiving device illustrated in FIG. 9 is an example of the receiving device (10004) of FIG. 1 (or the point cloud decoder of FIG. 10 and FIG. 11). The receiving device illustrated in FIG. 9 can perform at least one of the same or similar operations and methods as the operations and decoding methods of the point cloud decoder described in FIG. 1 to FIG. 11.

[0212] A receiving device according to the embodiments may include a receiving unit (9000), a receiving processing unit (9001), an arithmetic decoder (9002), an occupancy code-based octree reconstruction processing unit (9003), a surface model processing unit (triangle reconstruction, up-sampling, voxelization) (9004), an inverse quantization processing unit (9005), a metadata parser (9006), an arithmetic decoder (9007), an inverse quantization processing unit (9008), a prediction / lifting / RAHT inverse transformation processing unit (9009), a color inverse transformation processing unit (9010), and / or a renderer (9011). Each component of the decoding according to the embodiments may perform the inverse process of the components of the encoding according to the embodiments.

[0213] A receiver (9000) according to the embodiments receives point cloud data. The receiver (9000) may perform an operation and / or a receiving method identical or similar to the operation and / or receiving method of the receiver (10005) of FIG. 1. A detailed description is omitted.

[0214] A receiving processing unit (9001) according to the embodiments can obtain a geometry bitstream and / or an attribute bitstream from the received data. The receiving processing unit (9001) may be included in the receiving unit (9000).

[0215] The arismetic decoder (9002), the Occupancy code-based octree reconstruction processing unit (9003), the surface model processing unit (9004), and the inverse quantization processing unit (9005) can perform geometry decoding. Since the geometry decoding according to the embodiments is identical or similar to the geometry decoding described in FIGS. 1 to 10, a detailed description is omitted.

[0216] The arismetic decoder (9002) according to the embodiments can decode a geometry bitstream based on arismetic coding. The arismetic decoder (9002) performs the same or similar operation and / or coding as the operation and / or coding of the arismetic decoder (7000).

[0217] According to the embodiments, the Occupancy code-based octree reconstruction processing unit (9003) can reconstruct an octree by obtaining an Occupancy code from a decoded geometry bitstream (or information regarding geometry obtained as a result of decoding). The Occupancy code-based octree reconstruction processing unit (9003) performs the same or similar operations and / or methods as the octree synthesis unit (7001) and / or octree generation method. According to the embodiments, the surface model processing unit (9004) can perform trisup geometry decoding and related geometry reconstruction (e.g., triangle reconstruction, up-sampling, voxelization) based on the surface model method when trisup geometry encoding is applied. The surface model processing unit (9004) performs the same or similar operations as the surface offset synthesis unit (7002) and / or geometry reconstruction unit (7003).

[0218] The inverse quantization processing unit (9005) according to the embodiments can inverse quantize the decoded geometry.

[0219] A metadata parser (9006) according to the embodiments can parse metadata included in the received point cloud data, such as setting values, etc. The metadata parser (9006) can pass the metadata to geometry decoding and / or attribute decoding. A specific description of the metadata is omitted as it is the same as described in FIG. 8.

[0220] The arismetic decoder (9007), inverse quantization processing unit (9008), prediction / lifting / RAHT inverse transformation processing unit (9009), and color inverse transformation processing unit (9010) perform attribute decoding. Since attribute decoding is identical or similar to the attribute decoding described in FIGS. 1 to 10, a detailed description is omitted.

[0221] The arismetic decoder (9007) according to the embodiments can decode an attribute bitstream into arismetic coding. The arismetic decoder (9007) can perform decoding of the attribute bitstream based on reconstructed geometry. The arismetic decoder (9007) performs the same or similar operation and / or coding as the operation and / or coding of the arismetic decoder (7005).

[0222] The inverse quantization processing unit (9008) according to the embodiments can inverse quantize the decoded attribute bitstream. The inverse quantization processing unit (9008) performs the same or similar operation and / or method as the operation and / or inverse quantization method of the inverse quantization unit (7006).

[0223] According to the embodiments, the prediction / lifting / RAHT inverse transformation processing unit (9009) can process the reconstructed geometry and inverse quantized attributes. The prediction / lifting / RAHT inverse transformation processing unit (9009) performs at least one of the same or similar operations and / or decodings as the operations and / or decodings of the RAHT transformation unit (7007), LOD generation unit (7008), and / or inverse lifting unit (7009). According to the embodiments, the color inverse transformation processing unit (9010) performs inverse transformation coding to inversely transform the color values ​​(or textures) included in the decoded attributes. The color inverse transformation processing unit (9010) performs the same or similar operations and / or inverse transformation coding as the operations and / or inverse transformation coding of the color inverse transformation unit (7010). A renderer (9011) according to the embodiments can render point cloud data.

[0224] FIG. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0225] The structure of FIG. 10 represents a configuration in which at least one of a server (1060), a robot (1010), an autonomous vehicle (1020), an XR device (1030), a smartphone (1040), a home appliance (1050) and / or an HMD (1070) is connected to a cloud network (1010). The robot (1010), the autonomous vehicle (1020), the XR device (1030), the smartphone (1040), or the home appliance (1050) are referred to as devices. Additionally, the XR device (1030) may correspond to a point cloud data (PCC) device according to the embodiments or may be linked with a PCC device.

[0226] The cloud network (1000) may refer to a network that constitutes part of the cloud computing infrastructure or exists within the cloud computing infrastructure. Here, the cloud network (1000) may be configured using a 3G network, a 4G or LTE (Long Term Evolution) network, or a 5G network, etc.

[0227] The server (1060) is connected to at least one of a robot (1010), an autonomous vehicle (1020), an XR device (1030), a smartphone (1040), a home appliance (1050) and / or an HMD (1070) via a cloud network (1000) and can assist in at least some of the processing of the connected devices (1010 to 1070).

[0228] The HMD (Head-Mount Display) (1070) represents one of the types in which an XR device and / or PCC device according to the embodiments may be implemented. A device of the HMD type according to the embodiments includes a communication unit, a control unit, a memory unit, an I / O unit, a sensor unit, and a power supply unit, etc.

[0229] Hereinafter, various embodiments of the device (1010 to 1050) to which the above-described technology is applied are described. Here, the device (1010 to 1050) illustrated in FIG. 10 may be linked / coupled with a point cloud data transmission / reception device according to the above-described embodiments.

[0230] <PCC+XR>

[0231] The XR / PCC device (1030) may be implemented as a Head-Mount Display (HMD), a Head-Up Display (HUD) equipped in a vehicle, a television, a mobile phone, a smartphone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a stationary robot, or a mobile robot by applying PCC and / or XR (AR+VR) technology.

[0232] The XR / PCC device (1030) can obtain information about surrounding space or real objects by analyzing 3D point cloud data or image data obtained through various sensors or from an external device to generate position data and attribute data for 3D points, and can render and output an XR object to be output. For example, the XR / PCC device (1030) can output an XR object containing additional information about a recognized object by associating it with the recognized object.

[0233] <PCC+XR+모바일폰>

[0234] The XR / PCC device (1030) can be implemented as a mobile phone (1040) or the like by applying PCC technology.

[0235] The mobile phone (1040) can decode and display point cloud content based on PCC technology.

[0236] <PCC+자율주행+XR>

[0237] The autonomous vehicle (1020) can be implemented as a mobile robot, vehicle, unmanned aerial vehicle, etc. by applying PCC technology and XR technology.

[0238] An autonomous vehicle (1020) equipped with XR / PCC technology may refer to an autonomous vehicle equipped with means for providing XR images, or an autonomous vehicle that is the subject of control / interaction within the XR images. In particular, the autonomous vehicle (1020) that is the subject of control / interaction within the XR images is distinguished from the XR device (1030) and can be interconnected with it.

[0239] An autonomous vehicle (1020) equipped with means for providing XR / PCC images can acquire sensor information from sensors including cameras and output XR / PCC images generated based on the acquired sensor information. For example, the autonomous vehicle (1020) can provide an XR / PCC object corresponding to a real object or an object in the screen to the occupant by providing an XR / PCC object by outputting an XR / PCC image with a HUD.

[0240] At this time, when the XR / PCC object is displayed on the HUD, at least a portion of the XR / PCC object may be displayed so as to overlap with the actual object to which the occupant's gaze is directed. On the other hand, when the XR / PCC object is displayed on a display provided inside the autonomous vehicle, at least a portion of the XR / PCC object may be displayed so as to overlap with an object on the screen. For example, the autonomous vehicle (1220) may display XR / PCC objects corresponding to objects such as lanes, other vehicles, traffic lights, traffic signs, motorcycles, pedestrians, buildings, etc.

[0241] VR (Virtual Reality) technology, AR (Augmented Reality) technology, MR (Mixed Reality) technology and / or PCC (Point Cloud Compression) technology according to the embodiments can be applied to various devices.

[0242] In other words, VR technology is a display technology that provides real-world objects or backgrounds solely as CG images. On the other hand, AR technology refers to a technology that displays virtual CG images alongside images of real objects. Furthermore, MR technology is similar to the aforementioned AR technology in that it mixes and combines virtual objects with the real world. However, it is distinguished from AR technology in that while AR technology maintains a clear distinction between real-world objects and virtual objects created from CG images, using virtual objects to complement real-world objects, MR technology regards virtual objects as having the same nature as real-world objects. To give a more specific example, the aforementioned MR technology is applied in hologram services.

[0243] However, recently, rather than clearly distinguishing between VR, AR, and MR technologies, they are also referred to as XR (extended Reality) technology. Therefore, embodiments of the present invention are applicable to all VR, AR, MR, and XR technologies. These technologies may utilize encoding / decoding based on PCC, V-PCC, and G-PCC technologies.

[0244] The PCC method / device according to the embodiments can be applied to a vehicle providing autonomous driving services.

[0245] Vehicles providing autonomous driving services are connected to PCC devices to enable wired / wireless communication.

[0246] When a point cloud data (PCC) transceiver according to the embodiments is connected to a vehicle for wired or wireless communication, it can receive and process content data related to AR / VR / PCC services that can be provided along with an autonomous driving service, and transmit it to the vehicle. Additionally, when the point cloud data transceiver is mounted on a vehicle, the point cloud transceiver can receive and process content data related to AR / VR / PCC services according to a user input signal received through a user interface device and provide it to the user. A vehicle or a user interface device according to the embodiments can receive a user input signal. The user input signal according to the embodiments may include a signal indicating an autonomous driving service.

[0247] A point cloud data transmission method / device (or encoding method and device) according to embodiments comprises a transmission device (10000) of FIG. 1, a point cloud video encoder (10002), a transmitter (10003), an acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, an encoder of FIG. 11, bitstream segment-based encoding of FIG. 12 to 16, syntax generation of FIG. 17 to 19, NAL unit-based bitstream encoding of FIG. 20, bitstream segment (FGS, Fine Granularity Slice) of FIG. 22, alignment of FGS order of FIG. 23, layer group and subgroup-based encoding of FIG. 24 to 27, syntax generation of FIG. 29a and FIG. 29b to 30. The method may include and perform an octree-based subgroup encoding (Fig. 31), syntax generation (Figs. 32 to 34), FGS alignment (Figs. 35 to 40), syntax generation (Figs. 41 to 43), partial encoding (Figs. 44 to 47), and an encoding method (Fig. 48).

[0248] A method / device for receiving point cloud data (or a decoding method and device) according to embodiments comprises a receiving device (10004) of FIG. 1, a receiver (10005), a point cloud video decoder (10006), a transmission-decoding-rendering (20002-20003-20004) of FIG. 2, a decoder of FIG. 7, a receiving device of FIG. 9, a device of FIG. 10, a decoder of FIG. 11, bitstream segment-based decoding of FIG. 12 to 16, syntax acquisition of FIG. 17 to 19, NAL unit-based bitstream parsing of FIG. 21, bitstream segment (FGS, Fine Granularity Slice)-based decoding of FIG. 22, FGS-based sorting order-based decoding of FIG. 23, layer group and subgroup-based decoding of FIG. 24 to 27, layer group-based decoding of FIG. 28, syntax acquisition of FIG. 29a and FIG. 29b to FIG. 30. The method may include and perform syntax acquisition in FIGS. 32 to 34, FGS alignment order-based decoding in FIGS. 35 to 40, syntax acquisition in FIGS. 41 to 43, partial decoding in FIGS. 44 to 47, decoding method in FIG. 49, etc.

[0249] In addition, the point cloud data transmission / reception method / device according to the embodiments may be referred to simply as the method / device according to the embodiments.

[0250] According to the embodiments, geometry data, geometry information, location information, etc. constituting the point cloud data are interpreted as having the same meaning. Attribute data, attribute information, attribute information, etc. constituting the point cloud data are interpreted as having the same meaning.

[0251] The embodiments include a method for encoding point cloud data based on a data unit of a fine granularity slice (FGS), a method for transmitting a bitstream including encoded point cloud data and associated syntax, a method for receiving a bitstream, a method for decoding FGS-based point cloud data, and the like.

[0252] The embodiments include a method for efficiently supporting selective decoding of a portion of data when it is necessary due to receiver performance or transmission speed when transmitting and receiving point cloud data. The method according to the embodiments includes a method for selecting necessary information or removing unnecessary information at the bitstream level by dividing geometry and attributes, which are conventionally transmitted as data units, into semantic units such as geometry octree and Level of Detail (LoD). In this case, a network abstraction layer (NAL) unit is defined as the unit for selecting information, and a high-level syntax (HLS) for PCC structure restoration is defined.

[0253] The embodiments cover techniques for constructing a data structure composed of a point cloud. Specifically, they describe packing and signaling methods for effectively transmitting PCC data configured based on layers, and propose a method for applying this to scalable PCC-based services.

[0254] Referring to FIGS. 3 and FIGS. 7, point cloud data consists of the location (geometry: e.g., XYZ coordinates) and attributes (attributes: e.g., color, reflectance, intensity, grayscale, opacity, etc.) of each data point. Point Cloud Compression (PCC) performs octree-based compression to efficiently compress distribution characteristics that are non-uniformly distributed in three-dimensional space, and compresses attribute information based on this.

[0255] FIG. 11 illustrates the encoding and decoding process of point cloud data according to embodiments.

[0256] FIG. 11 is a bitstream according to an encoding method according to embodiments (transmitting device (10000) of FIG. 1, point cloud video encoder (10002), transmitter (10003), acquire-encode-transmit (20000-20001-20002) of FIG. 2, bitstream segment-based encoding of FIG. 12 to 16, syntax generation of FIG. 17 to 19, NAL unit-based bitstream encoding of FIG. 20, bitstream segment (FGS, Fine Granularity Slice) of FIG. 22, alignment of FGS order of FIG. 23, layer group and subgroup-based encoding of FIG. 24 to 27, syntax generation of FIG. 29a and 29b to 30, octree-based subgroup encoding of FIG. 31, syntax generation of FIG. 32 to 34, FGS alignment of FIG. 35 to 40, syntax generation of FIG. 41 to 43, partial encoding of FIG. 44 to 47, encoding method of FIG. 48). It represents the process of creating and transmitting.

[0257] FIG. 11 is a decoding method according to embodiments (receiving device (10004), receiver (10005), point cloud video decoder (10006) of FIG. 1, transmission-decoding-rendering (20002-20003-20004) of FIG. 2, decoder of FIG. 7, receiving device of FIG. 9, device of FIG. 10, bitstream segment-based decoding of FIG. 12 to 16, syntax acquisition of FIG. 17 to 19, NAL unit-based bitstream parsing of FIG. 21, bitstream segment (FGS, Fine Granularity Slice)-based decoding of FIG. 22, FGS-based sorting order-based decoding of FIG. 23, layer group and subgroup-based decoding of FIG. 24 to 27, layer group-based decoding of FIG. 28, syntax acquisition of FIG. 29a and 29b to 30, syntax acquisition of FIG. 32 to 34, FGS of FIG. 35 to 40 It illustrates the process of acquiring and decoding a bitstream according to alignment order-based decoding, syntax acquisition (Figs. 41 to 43), partial coding (Figs. 44 to 47), and decoding method (Fig. 49).

[0258] Point cloud data compresses (encodes) and transmits the location information (geometry) of data points as well as attribute information such as color, brightness, and reflectivity. Depending on the level of detail, point cloud data can be represented using an octree structure with layers or based on the Level of Detail (LoD); based on this, scalable point cloud data coding and representation are possible. However, depending on the performance or transmission speed of the receiver (or decoder), it is possible to decode or represent only a portion of the point cloud data; currently, there is no method to remove unnecessary data in advance. In other words, when only a portion of the scalable point cloud bitstream needs to be transmitted (e.g., decoding only a subset of layers during scalable decoding), it is not possible to select and send only the necessary parts. Therefore, one must either 1) re-encode the necessary parts after decoding, or 2) transmit the entire data and then selectively apply the necessary components at the receiver. However, in case 1), there may be a delay due to the time required for decoding and re-encoding, and in case 2), bandwidth efficiency is reduced because unnecessary data is transmitted, and there is a disadvantage that data quality must be lowered when using a fixed bandwidth.

[0259] The embodiments include an efficient FGS data unit delivery method, data unit order types (FGS unit, slice and attribute unit, geometry priority location, ROI priority), and additional signaling methods in the case of ROI priority.

[0260] FIG. 12 shows a bitstream fragment according to embodiments.

[0261] FIG. 12 is an encoding method according to embodiments (transmitting device (10000) of FIG. 1, point cloud video encoder (10002), transmitter (10003), acquire-encode-transmit (20000-20001-20002) of FIG. 2, FIG. 11 encoding, bitstream segment-based encoding of FIG. 13 to 16, syntax generation of FIG. 17 to 19, NAL unit-based bitstream encoding of FIG. 20, bitstream segment (FGS, Fine Granularity Slice) of FIG. 22, alignment of FGS order of FIG. 23, layer group and subgroup-based encoding of FIG. 24 to 27, syntax generation of FIG. 29a and FIG. 29b to FIG. 30, octree-based subgroup encoding of FIG. 31, syntax generation of FIG. 32 to 34, FGS alignment of FIG. 35 to 40, syntax generation of FIG. 41 to 43, partial encoding of FIG. 44 to 47, encoding of FIG. 48 Represents bitstream fragment-based encoding according to the method.

[0262] FIG. 12 is a decoding method according to embodiments (receiving device (10004) of FIG. 1, receiver (10005), point cloud video decoder (10006), transmission-decoding-rendering (20002-20003-20004) of FIG. 2, decoder of FIG. 7, receiving device of FIG. 9, device of FIG. 10, decoding of FIG. 11, bitstream segment-based decoding of FIG. 13 to 16, syntax acquisition of FIG. 17 to 19, NAL unit-based bitstream parsing of FIG. 21, bitstream segment (FGS, Fine Granularity Slice)-based decoding of FIG. 22, FGS-based sorting order-based decoding of FIG. 23, layer group and subgroup-based decoding of FIG. 24 to 27, layer group-based decoding of FIG. 28, syntax acquisition of FIG. 29a and 29b to 30, syntax acquisition of FIG. 32 to 34, Figs. 35 to 40 show FGS alignment order-based decoding, Figs. 41 to 43 show syntax acquisition, Figs. 44 to 47 show partial coding, and Fig. 49 show bitstream fragment-based decoding according to the decoding method.

[0263] Bitstream fragments according to the embodiments may be referred to as layer groups, FGS, etc.

[0264] The embodiments include a bitstream packing method for efficiently performing scalability, sub-sampling, subset extraction, etc. at the bitstream level based on the characteristics of point cloud data consisting of layers.

[0265] Bitstream fragment composition:

[0266] Figure 12 illustrates a case where a bitstream obtained through point cloud compression is divided into a geometry data bitstream and an attribute data bitstream and transmitted according to the type of data. In this case, each bitstream can be generated and transmitted in slice units, and the geometry data bitstream and the attribute data bitstream can each be generated and transmitted as a single unit regardless of layer information or LoD information. In this case, since the information required to reconstruct geometry and attributes exists within a single slice, there is a small possibility of restoration errors due to information loss, and there is the advantage of being able to easily construct the bitstream. However, if only a portion of the information within the bitstream is to be used, the entire bitstream must be decoded, and if a subset of point cloud data is transmitted or used in scalable coding-based applications, it is necessary to reconstruct the information.

[0267] For efficient information selection at the bitstream level, geometry bitstreams and attribute bitstreams can be separated, and this separation can be based on the layering characteristics of PCC. For example, in the case of geometry data, compression is performed based on an octree structure, and information belonging to each octree depth level can be bundled into a single unit for transmission. In this way, information can be selectively restored at the bitstream level in applications that require only a portion of the information. In the case of attributes, RAHT can be divided by octree depth levels similar to geometry, and Pred-Lifting can be divided into layers according to the Level of Depth. When dividing the geometry bitstream into bitstream units based on octree depth levels, if the attributes are encoded based on RAHT, bitstream fragments can be composed of units similar to geometry, and can signal structural similarity as synchronous partitioning in Fig. 12. If attributes are encoded using Pred-Lifting, and the LoD is configured independently of the geometry octree structure, bitstream fragments can be configured in a different format from the geometry.

[0268] In this case, the method of configuring bitstream fragments can be based not only on layer-based distinctions but also on different methods depending on the application. If the distinction method affects decoding, information regarding this can be signaled separately. Furthermore, not only is information from a single layer matched per bitstream fragment, but information from multiple or partial layers can also be matched.

[0269] Synchronized partitioning according to the embodiments means the case where the method of generating a geometry bitstream fragment (geometry layer, or geometry FGS) and the method of generating an attribute bitstream fragment (attribute layer, or attribute FGS) are the same. For example, a geometry bitstream fragment may be generated based on the depth of a geometry tree (which may be referred to as an octree or accusation tree, etc.), and an attribute bitstream fragment may be generated based on the same tree depth for the attributes of points included in a tree structure aligned with the geometry tree.

[0270] Asynchronous partitioning according to the embodiments means a case where the method of generating geometry bitstream fragments (geometry layer, or geometry FGS) and the method of generating attribute bitstream fragments (attribute layer, or attribute FGS) are different. For example, geometry bitstream fragments may be generated based on the depth of a geometry tree (which may be referred to as an octree or accusation tree, etc.), and attribute bitstream fragments may be generated based on the LoD.

[0271] FIG. 13 illustrates a layer-based bitstream fragment encoding according to embodiments.

[0272] FIG. 13 is an encoding method according to embodiments (transmitting device (10000) of FIG. 1, point cloud video encoder (10002), transmitter (10003), acquire-encode-transmit (20000-20001-20002) of FIG. 2, encoder of FIG. 11, bitstream segment-based encoding of FIG. 12 to 16, syntax generation of FIG. 17 to 19, NAL unit-based bitstream encoding of FIG. 20, bitstream segment (FGS, Fine Granularity Slice) of FIG. 22, alignment of FGS order of FIG. 23, layer group and subgroup-based encoding of FIG. 24 to 27, syntax generation of FIG. 29a and FIG. 29b to FIG. 30, octree-based subgroup encoding of FIG. 31, syntax generation of FIG. 32 to 34, FGS alignment of FIG. 35 to 40, syntax generation of FIG. 41 to 43, partial encoding of FIG. 44 to 47, encoding of FIG. 48 This is an example of encoding bitstream fragments using a layer structure according to the method.

[0273] Encoder Operation: Matching bitstream fragments according to PCC layer structure

[0274] Figure 13 illustrates the relationship between the proposed bitstream fragment configuration method and the layer structure of actual geometry data and attributes. First, assuming that the geometry consists of three octree depth levels, numbering from 0 to 3 can be applied from the root to the leaf, and the same numbering can be applied to the geometry bitstream fragments that match each octree layer. At this time, each bitstream fragment can be packed and transmitted as a unit called a Network Abstract Layer (NAL). By defining information about the included bitstream (data type: geometry data, layer number = octree depth layer) in the NAL header, the decision to include or exclude information can be made at the NAL level without parsing the bitstream down to the sub-level. In the case of attributes, when encoding is performed using LoD-based Pred-Lifting, as shown in Figure 13, newly included information for each LoD can be assumed to be information constituting each layer and packed at the attribute NAL unit level. For example, attribute NAL unit 0 can be seen as information constituting LoD 0, attribute NAL unit 0 & 1 as LoD 1, and attribute NAL unit 0 & 1 & 2 as LoD 2. In this case, it can be seen that asynchronous partitioning has been applied because the method of configuring bitstream fragments for geometry and attributes is different.

[0275] FIG. 14 illustrates layer-based bitstream fragment decoding according to embodiments.

[0276] FIG. 14 is a decoding method according to embodiments (receiving device (10004) of FIG. 1, receiver (10005), point cloud video decoder (10006), transmission-decoding-rendering (20002-20003-20004) of FIG. 2, decoder of FIG. 7, receiving device of FIG. 9, device of FIG. 10, decoder of FIG. 11, bitstream segment-based decoding of FIG. 12 to 16, syntax acquisition of FIG. 17 to 19, NAL unit-based bitstream parsing of FIG. 21, bitstream segment (FGS, Fine Granularity Slice)-based decoding of FIG. 22, FGS-based sorting order-based decoding of FIG. 23, layer group and subgroup-based decoding of FIG. 24 to 27, layer group-based decoding of FIG. 28, syntax acquisition of FIG. 29a and 29b to 30, syntax acquisition of FIG. 32 to 34, Figures 35 to 40 show FGS alignment order-based decoding, Figures 41 to 43 show syntax acquisition, Figures 44 to 47 show partial decoding, and Figure 49 show decoding method) illustrate an example of bitstream fragment (FGS) based decoding.

[0277] Decoder Operation: Decoding bitstream fragments and matching them to layers of the PCC structure

[0278] For bitstream fragments transmitted via the PCC layer structure, the receiver can select the bitstream based on the information in the NAL unit header, thereby reducing the amount of data transmitted to the decoder in advance. When the layer to be decoded is selected for the geometry bitstream and attribute bitstream, respectively (which may be defined by the receiver system or provided by the transmitter based on the decoder's performance), information following that layer can be removed based on the information in the NAL unit header. FIG. 14 defines that all four layers are used for the geometry octree level, but only up to LoD 1 is used for attributes; in this case, attribute NAL 2 may not be used based on the spatial ID information in the attribute NAL unit header. The information selected in this way can be matched to the geometry octree layer and the LoD layer, respectively.

[0279] At this time, information that serves as a criterion for selecting bitstream fragments, such as the entire layer configuration information and information that matches the bitstream fragments, can be used. This information can be transmitted through parameter sets such as SPS, GPS, and APS, or through information such as SEI messages.

[0280] When extracting / selecting a bitstream through a NAL unit, the following process may be performed.

[0281] a) Parse the NAL unit corresponding to nal unit type (nal_unit_type) = 0 (SPS_NUT) and determine whether the slice is divided (sps_slice_segmentation_flag = 1).

[0282] Geometry Bitstream Selection / Extraction

[0283] b) In the case of division, parse the NAL unit corresponding to nal_unit_type = 2 (GPS_NUT). Here, geometry layer information matching the nal_spatial_id of the NAL unit header can be obtained. For example, according to an embodiment of the present invention, information such as gps_max_spatial_id = 3, gps_max_geom_layer_idx=3, nal_spatial_id =0 -> gps_geom_layer_idx = 0, nal_spatial_id =1 -> gps_geom_layer_idx = 1, nal_spatial_id =2 -> gps_geom_layer_idx = 2, nal_spatial_id =3 -> gps_geom_layer_idx = 3 can be identified. If there is a target geometry layer, geometry NAL units having a value greater than the nal_spatial_id matching that layer can be discarded.

[0284] c) For the selected (select / extracted) bitstream fragment, geometry slice segments can be obtained by parsing the NAL unit. For all selected slices, parse them according to the transmission order (or the separately signaled order) and send the slice_layer_rbsp() to the decoder. At this time, the order in which the data is reconstructed is very important because prediction is performed based on similarity between layers.

[0285] d) The decoded geometry data can be reconstructed based on the layer structure identified in b).

[0286] Select / Extract Attribute Bittree

[0287] ㅁ) In SPS, if the synchronous geometry / attribute segment flag (sps_synchronous_geom_attr_segment_flag) = 0, it can be seen that the attribute NAL unit is configured in the same way as the geometry NAL unit. In this case, the attribute target layer is set to the geometry target layer value, and bitstreams below a specific layer can be extracted and selected through the nal spasial ID (nal_spatial_id) of the attribute NAL unit.

[0288] b) If sps_synchronous_geom_attr_segment_flag = 1, it can be seen that the attribute NAL unit is configured differently from the geometry NAL unit. In this case, the information corresponding to nal_unit_type = 3 (APS_NUT) is parsed. Here, attribute layer information matching the nal_spatial_id of the attribute NAL unit header can be obtained. For example, according to the embodiments, the attribute layer structure can be identified as aps_max_spatial_id = 3, aps_max_geom_layer_idx=2, nal_spatial_id = 0 -> aps_geom_layer_idx = 0, nal_spatial_id = 1 -> aps_geom_layer_idx = 1, nal_spatial_id = 2 -> aps_geom_layer_idx = 2. If there is a target geometry layer, attribute NAL units with a value greater than nal_spatial_id matching that layer can be discarded. The embodiments do not use layer 2, in which case only the information corresponding to LoD1 is selected by discarding nal_spatial_id = 2.

[0289] s) For a selected bitstream fragment, attribute slice segments can be obtained by parsing the NAL unit. For all selected slices, the slice layers (slice_layer_rbsp()) are parsed in the transmission order (or separately signaled order) and sent to the decoder. At this time, the order in which the data is reconstructed is very important because prediction is performed based on similarity between layers. Additionally, if necessary, reconstructed geometry data can be used for attribute decoding.

[0290] o) The decoded data can be reconstructed based on the layer structure identified in b).

[0291] A encoding method according to embodiments (transmitting device (10000) of FIG. 1, point cloud video encoder (10002), transmitter (10003), acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, encoder of FIG. 11, bitstream segment-based encoding of FIG. 12 to 16, syntax generation of FIG. 17 to 19, NAL unit-based bitstream encoding of FIG. 20, bitstream segment (FGS, Fine Granularity Slice) of FIG. 22, alignment of FGS order of FIG. 23, layer group and subgroup-based encoding of FIG. 24 to 27, syntax generation of FIG. 29a and 29b to 30, octree-based subgroup encoding of FIG. 31, syntax generation of FIG. 32 to 34, FGS alignment of FIG. 35 to 40, syntax generation of FIG. 41 to 43, partial encoding of FIG. 44 to 47, encoding method of FIG. 48) is a point It is possible to generate a bitstream by encoding cloud data and generating related parameters.

[0292] Decoding method according to embodiments (receiving device (10004) of FIG. 1, receiver (10005), point cloud video decoder (10006), transmission-decoding-rendering (20002-20003-20004) of FIG. 2, decoder of FIG. 7, receiving device of FIG. 9, device of FIG. 10, decoder of FIG. 11, bitstream segment-based decoding of FIG. 12 to 16, syntax acquisition of FIG. 17 to 19, NAL unit-based bitstream parsing of FIG. 21, bitstream segment (FGS, Fine Granularity Slice)-based decoding of FIG. 22, FGS-based sorting order-based decoding of FIG. 23, layer group and subgroup-based decoding of FIG. 24 to 27, layer group-based decoding of FIG. 28, syntax acquisition of FIG. 29a and 29b to 30, syntax acquisition of FIG. 32 to 34, FIG. 35 to Fig. 40 (FGS alignment order-based decoding), Figs. 41 to 43 (syntax acquisition), Figs. 44 to 47 (partial decoding), and Fig. 49 (decoding method) can acquire syntax within a bitstream and, based on the syntax, decode point cloud data within the bitstream. Hereinafter, syntax and semantics according to embodiments will be described with reference to each figure.

[0293] Syntax and Semantics

[0294] According to the embodiments, information regarding separated slices may be defined in the sequence parameter set, geometry slice header, and attribute slice header as follows, and depending on the application or system, it may be defined in a corresponding location or a separate location to use different scopes of application, application methods, etc. Although the embodiments are described as examples of defining the information independently of the attribute coding technique, it may be defined in conjunction with the attribute coding method and may be defined in the geometry parameter set for geometry scalability. In addition, if the syntax element defined below can be applied to multiple point cloud data streams as well as the current point cloud data stream, it may be transmitted through a higher-level concept parameter set, etc.

[0295] The embodiments define a PCC NAL (Network abstract layer) unit as a method to increase the efficiency of selection at the bitstream level when the bitstream is divided into fragments. In this case, the NAL unit can be classified into geometry NAL units (nal_unit_type = 16, 17) and attribute NAL units (nal_unit_type = 18, 19) according to nal_unit_type, and in addition, non-coding layer information such as parameter sets or SEI messages can be classified.

[0296] FIG. 15 shows the syntax of a NAL unit of a bitstream according to embodiments.

[0297] rbsp_byte[i] : Represents the i-th byte of RBSP.

[0298] emulation_prevention_three_byte : has a value of 0x03, and if the value exists, it must not be used during the decoding process.

[0299] FIG. 16 shows the syntax of the header of the NAL unit of the bitstream according to the embodiments.

[0300] Spatial ID (nuh_spatial_id_plus1): When a bitstream containing point cloud data is composed of spatial layers, the value of nuh_spatial_id_plus1 - 1 can be used to distinguish spatial layers at the bitstream level. If the slice is divided considering the spatial layer, the values ​​of gsh_slice_id and ash_slice_id can be defined to be linked to nuh_spatial_id_plus1.

[0301] NAL unit type (nal_unit_type): Represents the NAL (network abstract layer) unit type and can be defined as shown in the table below.

[0302] Layer ID (nuh_layer_id_plus1): nuh_layer_id_plus1 - 1 can indicate which layer the CL (coding layer) NAL unit or non-CL NAL unit is information about.

[0303] nal_unit_typeName of NalUnitTypeContent of NAL unit and RBSP syntax structureNAL unittype class0SPS_NUTSequence parameter setseq_parameter_set_rbsp( )non-CL1TPS_NUTSequence parameter setseq_parameter_set_rbsp( )non-CL2GPS_NUTSequence parameter setseq_parameter_set_rbsp( )non-CL3APS_NUTSequence parameter setseq_parameter_set_rbsp( )non-CL4AUD_NUTAccess unit delimiteraccess_unit_delimiter_rbsp( )non-CL56PREFIX_SEI_NUTSUFFIX_SEI_NUTSupplemental enhancement informationsei_rbsp( )non-CL7EOS_NUTEnd of sequenceend_of_seq_rbsp( )non-CL8EOB_NUTEnd of bitstreamend_of_bitstream_rbsp( )non-CL9...15RSV_NCL9..RSV_NCL15Reservednon-CL16IDG_NUTCoded slice of a independent decodable geomeryslice_layer_rbsp( )GCL17DG_NUTCoded slice of a dependent geomeryslice_layer_rbsp( )GCL18IDA_NUTCoded slice of a independent decodable attributeslice_layer_rbsp( )ACL19DA_NUTCoded slice of a dependent attributeslice_layer_rbsp( )ACL20...27RSV_NVCL20..RSV_NVCL27ReservedCL28...31UNSPEC28..UNSPEC31UnspecifiedCL

[0304] FIG. 17 shows the syntax of a sequence parameter set (SPS) in a bitstream according to embodiments.

[0305] Slice Segment Flag (sps_slice_segment_flag): If 1, it indicates that the slice is divided. If 0, it indicates that the geometry and attributes each consist of a single slice.

[0306] Synchronized geometry and attribute segment flag (sps_synchronous_geom_attr_segment_flag): If 1, it indicates that geometry slices and attribute slices are separated into the same structure. If 0, it indicates that geometry slices and attribute slices are separated into independent structures. If sps_slice_segment_flag is 0, sps_synchronous_geom_attr_segment_flag must be 0.

[0307] If the profile compatibility flag (profile_compatibility_flags[j]) is 1, it indicates that the bitstream complies with the profile where profile_idc is j as specified in Appendix A. For values ​​of j that are not specified as allowed values ​​for profile_idc in Appendix A, the value of profile_compatibility_flags[j] is 0.

[0308] The level indicator (level_idc) indicates the level that the bitstream adheres to as specified in Annex A. The bitstream must not contain a level_idc value other than that specified in Annex A. Other values ​​for level_idc are reserved by ISO / IEC for future use.

[0309] If the bounding box present flag (sps_bounding_box_present_flag) is 1, it indicates that source bounding box offset and size information is signaled to the SPS. If sps_bounding_box_present_flag is 0, it indicates that source bounding box information is not signaled.

[0310] The bounding box offset X (sps_bounding_box_offset_x) represents the x-offset of the source bounding box in Cartesian coordinates. If this value is not present, the sps_bounding_box_offset_x value is inferred to be 0.

[0311] The bounding box offset Y (sps_bounding_box_offset_y) represents the y-offset of the source bounding box in Cartesian coordinates. If this value is not present, the sps_bounding_box_offset_y value is inferred to be 0.

[0312] The bounding box offset Z (sps_bounding_box_offset_z) represents the z-offset of the source bounding box in Cartesian coordinates. If this value is missing, the sps_bounding_box_offset_z value is inferred to be 0.

[0313] The bounding box scale factor (sps_bounding_box_scale_factor) represents the scale of the source bounding box in Cartesian coordinates. If the sps_bounding_box_scale_factor value is not present, it is inferred to be 1.

[0314] The bounding box size width (sps_bounding_box_size_width) represents the width of the source bounding box in Cartesian coordinates. If this value is not present, sps_bounding_box_size_width is inferred to be 1.

[0315] The bounding box size height (sps_bounding_box_size_height) represents the height of the source bounding box in Cartesian coordinates. If this value is not present, the sps_bounding_box_size_height value is inferred to be 1.

[0316] The bounding box size depth (sps_bounding_box_size_depth) represents the depth of the source bounding box in Cartesian coordinates. If this value is not present, the sps_bounding_box_size_depth value is inferred to be 1.

[0317] The source scale factor (sps_source_scale_factor) represents the scale factor of the source point cloud.

[0318] The sequence parameter set ID (sps_seq_parameter_set_id) provides an SPS identifier that other syntax elements can reference. In bitstreams compliant with this version of this specification, the sps_seq_parameter_set_id value is 0. Non-zero values ​​for sps_seq_parameter_set_id are reserved for future use by ISO / IEC.

[0319] The number of attributes (sps_num_attribute_sets) represents the number of attributes encoded in the bitstream. The sps_num_attribute_sets value is in the range of 0 to 63.

[0320] Attribute dimension (attribute_dimension[ i ]) represents the number of components of the i-th attribute.

[0321] The attribute instance ID (attribute_instance_id[ i ]) represents the instance ID of the i-th attribute.

[0322] Attribute bit depth (attribute_bitdepth[ i ]) represents the bit depth of the i-th attribute signal.

[0323] The attribute color (attribute_cicp_colour_primaries[ i ]) represents the chromaticity coordinates of the source primary colors of the color attribute of the i-th attribute.

[0324] The attribute transfer characteristic (attribute_cicp_transfer_characteristics[ i ]) represents a reference photoelectric transfer characteristic function of a color attribute as a function of source input linear light intensity Lc with a nominal real value range of 0 to 1, or represents the reciprocal of a reference electrophotographic transfer characteristic function as a function of output linear light intensity Lo with a nominal real value range of 0 to 1.

[0325] The attribute matrix coefficients (attribute_cicp_matrix_coeffs[ i ]) describe the matrix coefficients used to derive luminance and chroma signals from green, blue, red, or Y, Z, X primary colors.

[0326] The attribute video full range flag (attribute_cicp_video_full_range_flag[ i ]) indicates the black level and range of the luminance and chroma signals derived from the E'Y, E'PB, E'PR, or E'R, E'G, E'B real value component signals.

[0327] If the attribute label flag (known_attribute_label_flag[ i ]) is 1, it indicates that the known_attribute_label is signaled for the i-th attribute. If the known_attribute_label_flag[ i ] is 0, it indicates that the attribute_label_four_bytes is signaled for the i-th attribute.

[0328] If attribute label (known_attribute_label[ i ]) is 0, it indicates that the attribute is color. If known_attribute_label[ i ] is 1, it indicates that the attribute is reflectance. If known_attribute_label[ i ] is 2, it indicates that the attribute is farm index.

[0329] The attribute label byte (attribute_label_four_bytes[ i ]) represents a known attribute type using a 4-byte code. The following table describes the list of supported attributes and their relationship with attribute_label_four_bytes[ i ].

[0330] The value of attribute_label_four_bytes is as follows.

[0331] attribute_label_four_bytes[ i ]Attribute type0Colour1Reflectance0xffffffffunspecified

[0332] If sps_extension_present_flag is 1, it indicates that the sps_extension_data syntax structure exists in the SPS syntax structure. If sps_extension_present_flag is 0, it indicates that this syntax structure does not exist. If it does not exist, the sps_extension_present_flag value is inferred to be 0.

[0333] sps_extension_data_flag can have any value. The presence or value of this value does not affect the decoder's suitability for the profile specified in Appendix A. A decoder that complies with the profile specified in Appendix A.

[0334] FIG. 18 shows the syntax of a set of geometry parameters (GPS) in a bitstream according to embodiments.

[0335] Max spatial ID (gps_max_spatial_id): Represents the maximum value of spatial_id for the current geometry. gps_max_spatial_id can have a value between 0 and the maximum value of nuh_spatial_id_plus1 when the NAL unit type class is GCL (Geometry coding layer).

[0336] Max Geometry Layer Index (max_geom_layer_idx): Represents the maximum value of a geometry layer defined in geometry coding. For example, geometry composed of an octree structure can have a max octree depth level value.

[0337] Geometry layer index (gps_geom_layer_idx[i]): Represents the geometry layer matching the i-th spatial_id. For example, for geometry composed of an octree structure, it can indicate the octree depth level matching the spatial_id.

[0338] The geometry parameter set ID (gps_geom_parameter_set_id) provides a GPS identifier that other syntax elements can reference. The gps_seq_parameter_set_id value ranges from 0 to 15.

[0339] The sequence parameter set ID (gps_seq_parameter_set_id) represents the sps_seq_parameter_set_id value for the active SPS. The gps_seq_parameter_set_id value is in the range of 0 to 15.

[0340] If the box present flag (gps_box_present_flag) is 1, it indicates that additional bounding box information is provided in the geometry header referencing the current GPS. If gps_bounding_box_present_flag is 0, it indicates that no additional bounding box information is displayed in the geometry header.

[0341] If the box log scale present flag (gps_gsh_box_log2_scale_present_flag) is 1, it indicates that gsh_box_log2_scale is displayed in each geometry slice header referencing the current GPS. If gps_gsh_box_log2_scale_present_flag is 0, it indicates that gsh_box_log2_scale is not signaled in each geometry slice header and the common scale of all slices is signaled from the current GPS's gps_gsh_box_log2_scale.

[0342] Box log scale (gps_gsh_box_log2_scale) represents the common scale factor of the bounding box origin for all slices referencing the current GPS.

[0343] If the unique_geometry_points_flag is 1, it indicates that all output points have unique locations. If unique_geometry_points_flag is 0, it indicates that two or more output points can have the same location.

[0344] If the neighbor_context_restriction_flag is 0, it indicates that octree occupancy coding uses context determined from 6 adjacent parent nodes. If neighbor_context_restriction_flag is 1, it indicates that octree coding uses only context determined from sibling nodes.

[0345] If the inferred_direct_coding_mode_enabled_flag is 1, it indicates that the direct_mode_flag may exist in the geometry node syntax. If the inferred_direct_coding_mode_enabled_flag is 0, it indicates that the direct_mode_flag does not exist in the geometry node syntax.

[0346] If bitwise_occupancy_coding_flag is 1, it indicates that geometry node occupancy is encoded using bitwise contexting of the syntax element occupancy_map. If bitwise_occupancy_coding_flag is 0, it indicates that geometry node occupancy is encoded using the pre-encoded syntax element occypancy_byte.

[0347] If the adjacent_child_contextualization_enabled_flag is 1, it indicates that the adjacent child nodes of an adjacent octree node are used for bitwise hold contextualization. If the adjacent_child_contextualization_enabled_flag is 0, it indicates that the child nodes of an adjacent octree node are not used for hold contextualization.

[0348] The neighbor boundary (log2_neighbour_avail_boundary) represents the value of the NeighbAvailBoundary variable used in the decoding process as follows.

[0349] NeighborAvailBoundary = 2log2_neighbour_avail_boundary

[0350] If neighbor_context_restriction_flag is 1, NeighbAvailabilityMask is set to 1. Otherwise, if neighbor_context_restriction_flag is 0, NeighbAvailabilityMask is set to 1 << log2_neighbour_avail_boundary.

[0351] The intra-pred maximum node size (log2_intra_pred_max_node_size) represents the octree node size suitable for occupancy internal prediction.

[0352] The trisoup node size (log2_trisoup_node_size) represents the TrisoupNodeSize variable as the size of the triangle node as follows.

[0353] TrisoupNodeSize = 1 << log2_trisoup_node_size

[0354] If log2_trisoup_node_size is 0, the geometry bitstream contains only octree coding syntax. If log2_trisoup_node_size is greater than 0, the following is required for bitstream conformance.

[0355] inferred_direct_coding_mode_enabled_flag must be 0, and unique_geometry_points_flag is 1.

[0356] If gps_extension_present_flag is 1, it indicates that the gps_extension_data syntax structure exists in the GPS syntax structure. If gps_extension_present_flag is 0, it indicates that this syntax structure does not exist. If it does not exist, the gps_extension_present_flag value is inferred to be 0.

[0357] gps_extension_data_flag can have any value. Its presence and value do not affect decoder suitability for the profiles specified in Appendix A. Decoders suitable for the profiles specified in Appendix A.

[0358] FIG. 19 shows the syntax of an attribute parameter set (APS) in a bitstream according to embodiments.

[0359] Max Spatial ID (aps_max_spatial_id): Represents the maximum value of spatial_id for the current attribute. aps_max_spatial_id can have a value between 0 and the maximum value of nuh_spatial_id_plus1 when the NAL unit type class is an ACL (Attribute coding layer).

[0360] Max Attribute Layer Index (aps_max_attr_layer_idx): Represents the maximum value of the attribute layer constituting the attribute. For example, when performing attribute coding based on LoD, it can have the maximum value of the LoD layer.

[0361] Attribute layer index (aps_attr_layer_idx[i]): Represents the attribute layer matching the i-th spatial_id. For example, if attribute coding is performed based on the LoD structure, it can indicate the LoD layer matching the spatial_id of the attribute layer.

[0362] The attribute parameter set ID (aps_attr_parameter_set_id) provides an APS identifier that other syntax elements can reference. The value of aps_attr_parameter_set_id ranges from 0 to 15.

[0363] The sequence parameter set ID (aps_seq_parameter_set_id) represents the value of sps_seq_parameter_set_id for the active SPS. The value of aps_seq_parameter_set_id is in the range of 0 to 15.

[0364] The attribute coding type (attr_coding_type) indicates the coding type for the attributes in the table below for a given attr_coding_type value. In bitstreams complying with this version of this specification, the value of attr_coding_type is 0, 1, or 2.

[0365] The attr_coding_type value is as follows.

[0366] attr_coding_typecoding type0Predicting Weight Lifting1Region Adaptive Hierarchical Transform (RAHT)2Fix Weight Lifting

[0367] The above types can be defined with a consistent name for *?* in prediction and lifting transformations.

[0368] The attribute initialQP (aps_attr_initial_qp) represents the initial value of the variable SliceQp for each slice referencing APS. The initial value of SliceQp is modified in the attribute slice segment hierarchy when slice_qp_delta_luma or a non-zero value of slice_qp_delta_luma is decoded. The value of aps_attr_initial_qp ranges from 0 to 52.

[0369] The attribute chroma QP offset (aps_attr_chroma_qp_offset) represents the offset for the initial quantization parameter passed as a signal in the aps_attr_initial_qp syntax.

[0370] If the slice QP delta present flag (aps_slice_qp_delta_present_flag) is 1, it indicates that ash_attr_qp_delta_luma and ash_attr_qp_delta_luma syntax elements are present in ASH. If aps_slice_qp_present_flag is 0, it indicates that ash_attr_qp_delta_luma and ash_attr_qp_delta_luma syntax elements are not present in ASH.

[0371] The number of lifting nearest predictors (lifting_num_pred_nearest_neighbours) indicates the maximum number of nearest neighbors to use for prediction. The value of lifting_num_pred_nearest_neighbours ranges from 1 to xx.

[0372] The maximum number of lifting direct predictors (lifting_max_num_direct_predictors) indicates the maximum number of predictor variables to use for direct prediction. The value of lifting_max_num_direct_predictors ranges from 0 to lifting_num_pred_nearest_neighbours.

[0373] The value of the variable MaxNumPredictors used in the decoding process is as follows.

[0374] MaxNumPredictors = lifting_max_num_direct_predictors + 1

[0375] The lifting_search_range represents the search range used to determine the nearest neighbors to use for prediction and to build the distance-based level of detail (LOD).

[0376] If the lifting_lod_regular_sampling_enabled_flag is 1, it indicates that the level of detail is built using a regular sampling strategy. If lifting_lod_regular_sampling_enabled_flag is 0, it indicates that a distance-based sampling strategy is used instead.

[0377] The number of lifting detail levels (lifting_num_detail_levels_minus1) represents the number of detail levels for attribute coding. The value of lifting_num_detail_levels_minus1 is in the range from 0 to xx.

[0378] The lifting sampling period (lifting_sampling_period[idx]) represents the sampling period for the detail level idx. The values ​​of lifting_sampling_period[ ] are in the range of 0 to xx.

[0379] Lifting Sampling Distance Squared (lifting_sampling_distance_squared[ idx ]) represents the square of the sampling distance for the detail level idx. The values ​​of lifting_sampling_distance_squared[ ] are in the range of 0 to xx.

[0380] The lifting_adaptive_prediction_threshold represents the threshold that enables adaptive prediction. The values ​​of lifting_adaptive_prediction_threshold[ ] are in the range of 0 to xx.

[0381] The number of lifting intra LoD prediction layers (lifting_intra_lod_prediction_num_layers) indicates the number of LoD layers that can reference points decoded from the same LoD layer to generate predicted values ​​for a target point. If lifting_intra_lod_prediction_num_layers is equal to num_detail_levels_minus1 + 1, it indicates that the target point can reference points decoded from the same LoD layer for all LoD layers. If lifting_intra_lod_prediction_num_layers is 0, it indicates that the target point cannot reference points decoded from the same LoD layer in any LoD layer. The range of lifting_intra_lod_prediction_num_layers is from 0 to lifting_num_detail_levels_minus1 + 1.

[0382] If aps_extension_present_flag is 1, it indicates that the aps_extension_data syntax structure exists in the APS syntax structure. If aps_extension_present_flag is 0, it indicates that this syntax structure does not exist. If it does not exist, the value of aps_extension_present_flag is assumed to be 0.

[0383] aps_extension_data_flag can have any value.

[0384] FIG. 20 shows NAL unit-based encoding according to embodiments.

[0385] FIG. 20 is an example of a detailed functional configuration for encoding / transmitting PCC data using the present invention. When point cloud data is input, the encoder can encode location information (geometry data: e.g., XYZ coordinates, phi-theta coordinates, etc.) and attribute information (attribute data: e.g., color, reflectance, intensity, grayscale, opacity, medium, material, glossiness, etc.), respectively. The compressed data is divided into units for transmission, and can be packed into NAL unit units according to layering structure information such as SPS, APS, GPS, TPS, etc. through a packing module.

[0386] FIG. 21 illustrates NAL unit-based decoding according to embodiments.

[0387] FIG. 21 is an example of a detailed functional configuration for receiving / decoding PCC data. When a bitstream is input, the receiver can process it by distinguishing between the bitstream for location information and the bitstream for attribute information. At this time, the NAL unit parser can distinguish between no-CL information such as SPS, GPS, and APS, and CLs such as geometry NAL units and attribute NAL units. The metadata parser can identify the geometry layer and attribute layer structure of the point cloud data based on the information transmitted from SPS, GPS, and APS, and set the target layer to be decoded. During the NAL unit selection process, layers excluded from decoding can be removed at the NAL unit level via nal_spatial_id. The classified bitstreams can be restored into geometry data and attribute data by the geometry decoder and attribute decoder, respectively, depending on the characteristics of the data, and then converted into a format for final output by the renderer.

[0388] FIG. 22 shows the relationship between the slice and FGS structures according to the embodiments.

[0389] The encoding / decoding method according to the embodiments can encode point cloud data, generate a bitstream, and decode point cloud data based on an FGS structure as shown in FIG. 22.

[0390] Fine granularity slice (FGS) is a technique that divides a slice into multiple slices, offering benefits such as error resistance, partial decoding, progressive decoding, and scalable transmission. In this case, slice geometry and slice attributes exist within the slice. What was originally composed of geometry data units and attribute data units can be divided into multiple geometry data units and multiple attribute data units, and each DU can be configured as an FGS. In this case, a method can be used to transmit (decode) the geometry and then transmit (decode) the attributes for each FGS.

[0391] For example, as shown in Fig. 22, transmitting FGS can cause delay. When performing partial decoding, all previously transmitted DGDUs must be waited for in order to decode a specific DADU. As in the example, if there are two or more attributes, the magnitude of the delay increases further. This delay can be a significant factor in delay when transmission is performed sequentially, taking into account the FGS decoding time, in environments with low bandwidth or limited receiver buffers.

[0392] As another example, when a G-PCC bitstream is configured and transmitted as FGS, progressive decoding is possible, allowing for sequential decoding and display. However, in cases where attributes are transmitted after geometry, progressive decoding / progressive display can be performed only on the geometry, or progressive decoding / progressive display can be performed during the attribute decoding process after geometry decoding is finished.

[0393] FIG. 23 shows an FGS according to the embodiments.

[0394] To improve upon the problem described in FIG. 22, the method according to the embodiments may use a method of configuring FGS in subgroup units. FIG. 23 illustrates the process of rearranging DUs (Data Units) encoded in the order of geometry and attributes in subgroup units. When geometry and attributes are sliced ​​based on the same layer-group structure, the geometry data and attribute data corresponding to each subgroup are transmitted as a single bundle. In this case, the FGS can be considered as a unit for transmitting all data within the subgroup, and geometry data and attribute data can be transmitted as a bundle.

[0395] In this case, FGS can be defined similarly to the definition of a G-PCC slice. That is, a G-PCC slice is defined as a set of points existing within a certain space within a tile, and slice geometry and slice attributes are coded as separate data units, but can be grouped by a slice ID (slice_id) and transmitted as a single slice. In the case of FGS, slices are organized into a layer-group structure, and data units are coded at the node level belonging to a certain space and depth, but FGS geometry and FGS attributes can be grouped to define a single unit called FGS.

[0396] When using the FGS according to the embodiments, the geometry and attributes of the necessary parts can be restored without delay according to various use cases in the decoder. For example, in the case of partial decoding, since the geometry and attributes are a set, it is possible to determine whether the geometry is a necessary DU and apply the result directly to the attribute DU. In addition, since the geometry DU and the attribute DU are transmitted consecutively, time used to filter out unnecessary DUs or to transmit unnecessary DUs can be saved.

[0397] In terms of progressive decoding and progressive display, since geometry and attributes are transmitted as a bundle, the geometry and attributes of the corresponding area and depth can be displayed sequentially and simultaneously.

[0398] To this end, based on the current signaling method, geometry data units and attribute data units belonging to the same FGS can be distinguished using pairs of layer group IDs and subgroup IDs. In this case, it is defined as a concept to group GDU / ADU or DGDU / DADU, and the layer group ID and subgroup ID, which are the indices of the subgroups, can be used as FGS indices. (Signaling Example 2)

[0399] Alternatively, an FGS index can be defined separately. In this case, FGS geometry and FGS attributes can be identified by the FGS ID (fgs_id), and common information can be transmitted via GDU or DGDU, while duplicate information may not be transmitted via ADU or DADU. (Signaling Example 1)

[0400] FIG. 24 shows an FGS structure according to the embodiments.

[0401] The FGS described in FIGS. 22 and FIGS. 23, etc., can be defined as follows.

[0402] Fine-Granularity Slicing Using Layer-Group Structure

[0403] When fine-grained slicing is enabled, the G-PCC bitstream is split into multiple sub-bitstreams. To effectively utilize the hierarchical structure of G-PCC, each slice contains coded data in a partial coding layer or a partial region. When slice splitting is combined with the coding hierarchy, it can efficiently support scalable transmission or spatial random access use cases.

[0404] Layer group-based slice segmentation:

[0405] In fine-grained slicing, each slice segment contains coded data from a layer group defined as follows. A layer group is defined as a group of consecutive tree layers where the starting and ending depths can be any number of tree depths and the starting depth is smaller than the ending depth. The order of the coded data in a slice segment is the same as the order of the coded data in a single slice.

[0406] For example, consider a geometry coding hierarchy consisting of eight layers as illustrated in FIG. 24(a). In this example, there are three layer groups, and each layer group is matched with a different slice. Layer group 1 is used to code layers 0 through 4, layer group 2 is used to code layer 5, and layer group 3 is used to code layers 6 through 7. When the first two slices are transmitted or selected, the decoded output becomes a partial layer of layers 0 through 5, as illustrated in FIG. 24(b). Using slices in a layer group structure allows for partial decoding of the coding layer without accessing the entire bitstream.

[0407] FIG. 24 shows partial decoding using layer group-based geometry slices, FIG. 24 (a) shows three slices generated by a layer group structure in an encoder, and FIG. 24 (b) shows an example of partial decoding using two slices in a decoder and output.

[0408] In addition to the layer group structure, dividing layer groups into multiple subgroups is also used when considering spatial random access use cases.

[0409] A subgroup is a subset of a layer group, and the points in that subgroup are adjacent to each other. The subgroups of a layer group are mutually exclusive, and the set of points belonging to a subgroup of a layer group is identical to the set of points in the layer group.

[0410] FIG. 25 illustrates an example of partial decoding using a layer group and subgroup structure according to embodiments.

[0411] Since the points of each subgroup form boundaries within a spatial domain, the boundaries of the subgroups can be represented by subgroup bounding box information. By utilizing this spatial information, the layer-group and subgroup structures can efficiently access the ROI by selecting a slice that includes the ROI.

[0412] FIG. 25 (a) shows partial decoding using a layer group and subgroup structure, and FIG. 25 (b) shows subgroup bounding box information.

[0413] Figure 25 illustrates an example of a subgroup structure and its corresponding bounding box. In Figure 25, layer groups 2 and 3 from the previous example are divided into 2 and 4 subgroups, respectively, and contained within different slices. Given a layer group and a subgroup slice containing bounding box information, spatial access can be performed by 1) comparing the bounding box of each slice with the ROI, 2) selecting the slice whose subgroup bounding box overlaps with the ROI, and 3) decoding the selected slice. If the ROI is considered in region 3-3, slices 1, 3, and 6 are selected as the subgroup bounding boxes of layer group 1, subgroup 2-2, and 3-3, respectively, covering the ROI region. For effective spatial access, it is assumed that there are no dependencies between subgroups of the same layer group. For live streaming or low-latency use cases, time efficiency is improved by performing selection and decoding at the time each slice segment is received.

[0414] FIG. 26 shows examples of multi-resolution and multi-ROI according to embodiments.

[0415] Based on scalability and spatial access capabilities, layer group slicing provides efficient access to large-scale or high-density point cloud data. Due to the large number of points and large data size, rendering or displaying such content takes a significant amount of time. As an alternative approach, the level of detail can be adjusted according to the viewer's interest. For example, when a viewer is far from a scene or object, structural or global area information is more important than local details. Conversely, as the viewer approaches a specific area or object, detailed information regarding the area of ​​interest is required. Using an adaptive method, the renderer can efficiently provide the viewer with data of sufficient quality. Fig. 26 shows an example of incremental detail changes for three viewing distances where the viewing distance changes according to the ROI.

[0416] 1) High-level view (coarse detail)

[0417] 2) Mid-level view (medium-level detail)

[0418] 3) Low-level view (fine-grained detail).

[0419] FIG. 27 illustrates a scalable G-PCC bitstream transmission process according to embodiments.

[0420] Using layer group slicing for G-PCC bitstream generation enables support for multiple resolution ROIs. As shown in FIG. 27, an encoder according to the embodiments generates bitstream slices for octree layer groups or spatial subgroups of each layer group. Upon request, a slice matching the ROI of each resolution is selected and transmitted. Since the bitstream does not contain details other than the requested ROI, the total bitstream size is smaller than that of a tile-based method. At the receiver, the decoder can combine the slices to generate three outputs: 1) an upper-level view output from layer group slice 1; 2) an intermediate-level view output from layer group slice 1 and a selected subgroup of layer group 2; and 3) a lower-level view detail output from layer group 1 and selected subgroups of layer groups 2 and 3. Since the outputs can be generated progressively, the receiver can provide a viewing experience such as zooming in / out from an upper-level view to a lower-level view, and the resolution increases progressively.

[0421] Parameters describing the hierarchy-group structure are signaled at multiple levels. Common structural information is described in the SPS, and details of each hierarchy-group or subgroup are signaled in the slice header. Additionally, a hierarchy-group structure inventory and dependent slice headers are introduced to describe the overall hierarchy-group structure. The definition of G-PCC slices and the signaling method for fine granularity slicing are as follows.

[0422] 1) Definition of G-PCC Slice

[0423] A. Slice: A set of points coded by one independent fine-grained slice and zero or more dependent fine-grained slices.

[0424] B. Dependent Fine-grained Slice: A data unit of a slice that depends on the previous data unit within the same slice.

[0425] C. Independent Fine-grained Slice: The first [geometric] data unit of the slice.

[0426] 2) Fine granular slices are activated in SPS.

[0427] 3) Essential information required to decode dependent micro-unit slices is conveyed through dependent data unit headers, which include context inheritance and slice-specific bounding boxes.

[0428] 4) Define a layer group structure inventory to explain the relationships between micro-unit slices.

[0429] FIG. 28 illustrates a layer group slicing-based decoding procedure according to embodiments.

[0430] FIG. 28 is a decoding method according to embodiments (receiving device (10004) of FIG. 1, receiver (10005), point cloud video decoder (10006), transmission-decoding-rendering (20002-20003-20004) of FIG. 2, decoder of FIG. 7, receiving device of FIG. 9, device of FIG. 10, bitstream segment-based decoding of FIG. 12 to 16, syntax acquisition of FIG. 17 to 19, NAL unit-based bitstream parsing of FIG. 21, bitstream segment (FGS, Fine Granularity Slice)-based decoding of FIG. 22, FGS-based sorting order-based decoding of FIG. 23, layer group and subgroup-based decoding of FIG. 24 to 27, layer group-based decoding of FIG. 28, syntax acquisition of FIG. 29a and 29b to 30, syntax acquisition of FIG. 32 to 34, FGS of FIG. 35 to 40 FGS-based decoding according to the sort order-based decoding, syntax acquisition (Figs. 41 to 43), partial coding (Figs. 44 to 47), and decoding method (Fig. 49) is illustrated.

[0431] Geometry Slicing:

[0432] Decoder Process:

[0433] The decoding process of the layer group slicing reference SW for the first fine grain slice (FGS) is identical to existing geometry slice decoding, namely, parameter set parsing, data unit header parsing, and data unit decoding. When layer group slicing is enabled, subsequent dependent geometry data units with the same slice ID are considered the FGS of the first slice. Considering context references and node inheritance between parent and child subgroups, it is assumed that the order of FGS is sorted in ascending order based on layer_group_id and subgroup_id.

[0434] Before decoding the dependent data unit, the context state, output node, and layer group parameters of the previous slice are stored in the buffer of the subsequent slice. After parsing the dependent data unit header, the parent of the current subgroup is detected by finding the subgroup whose current subgroup bounding box is a superset of the current subgroup bounding box. Once the parent subgroup is determined, the parent node of the current dependent data unit is selected. Using the selected node as the initial node for the decoding process, the current dependent data unit is decoded up to the tree layer contained within the current layer group. The decoding process of the dependent data unit is performed recursively until the end of the geometric bitstream.

[0435] The following describes in more detail the additional process of finding parent subgroups and parent nodes.

[0436] Parent Subgroup Detection

[0437] In fine-grained unit slice decoding, nodes of the parent subgroup are used as inputs for the child subgroup, providing continuous decoding at the layer group boundary. This is due to the hierarchical structure of layer group slicing and can be derived using parent-child spatial relationships. A child subgroup is a subset of the parent subgroup, and the bounding box of a child subgroup is spatially exclusive from the bounding box of another child subgroup within the same layer group.

[0438] Based on this relationship, the spatial superset of the current slice can be identified to detect slices containing the parent subgroup. The parent subgroup can be found by using the subgroup_bbox_origin and subgroup_bbox_size specified in the data unit header and comparing them with the bounding box information of the subgroup at the previous layer group level.

[0439] parentLayerGroup = curLayerGroup - 1;

[0440] for (i = 0; i < numSubgroups[parentLayerGroup]; i++) {

[0441] if (_bboxMin[parentLayerGroup][i] <= curBboxMin

[0442] && _bboxMax[parentLayerGroup][i] > curBboxMin) {

[0443] parentSubgroup = i;

[0444] break;}}

[0445] Select input parent node

[0446] During dependent slice decoding, the output nodes of the parent subgroup are used as inputs for the decoding of the child subgroup. If the subgroup bounding boxes of the parent and child subgroups are identical, all nodes generated from the parent subgroup are used. Conversely, if the subgroup bounding box of the child subgroup is a subset of the parent subgroup, the decoder selects the actual parent node. To find the parent node, each node of the parent subgroup is compared with the bounding box boundaries of the child subgroup.

[0447] for (node ​​= inNodes.begin(); node != inNodes.end(); node++) {

[0448] if (node.Pos >= bbox_min && node.Pos < bbox_max)

[0449] fifo.emplace_back(node);

[0450] else

[0451] continue;}

[0452] FIGS. 29a and FIGS. 29b show the SPS (sequence parameter set) syntax in a bitstream according to the embodiments.

[0453] If the layer_group_enabled_flag is 1, it indicates that the geometry bitstream of a slice is contained in multiple slices that correspond to a coding layer group or its subgroups. If layer_group_enabled_flag is 0, it indicates that the geometry bitstream is contained in a single slice.

[0454] Layer group count (num_layer_groups_minus1): Adding 1 to this value represents the number of layer groups. Here, a layer group represents a group of consecutive tree layers that are part of the geometry coding tree structure. num_layer_groups_minus1 ranges from 0 to the number of coding tree layers.

[0455] The layer group ID (layer_group_id) represents the layer group indicator of the slice. The range of layer_group_id is from 0 to num_layer_groups_minus1.

[0456] Number of layers (num_layers_minus1): Adding 1 to this value represents the number of coding layers included in the i-th layer group. The total number of layer groups can be obtained by adding all (num_layers_minus1[i] + 1) to num_layer_groups_minus1 when i is 0.

[0457] Subgroup enable flag(if subgroup_enabled_flag is 1, it indicates that the i-th layer group is divided into two or more subgroups and that the set of points in the layer group's subgroups is identical to the set of points in the layer group. If subgroup_enabled_flag of the i-th layer group is 1, then when j is greater than or equal to i, subgroup_enabled_flag of the j-th layer group becomes 1. If subgroup_enabled_flag is 0, it indicates that the current layer group is not divided into multiple subgroups and is contained in a single slice.

[0458] Subgroup bounding box origin (subgroup_bbox_origin_bits_minus1): This value plus 1 is the bit length of the subgroup_bbox_origin syntax element.

[0459] Subgroup bounding box size (subgroup_bbox_size_bits_minus1): This value plus 1 is the bit length of the syntax element subgroup_bbox_size.

[0460] If unified_layer_group_structure_enabled is 1, it indicates that the same layer group structure is used for geometry and attribute coding. If unified_layer_group_structure_enabled is 0, it indicates that the layer group structures used for geometry coding and attribute coding may be different.

[0461] FIG. 30 shows the dependent geometry data unit header syntax within a bitstream according to embodiments.

[0462] The geometry parameter set ID (dgdu_geometry_parameter_set_id) represents the active GPS indicated by gps_geom_parameter_set_id. The value of dgdu_geometry_parameter_set_id is the same as the value of gdu_geometry_parameter_set_id of the corresponding slice.

[0463] The slice ID (dgdu_slice_id) represents the slice to which the current dependent geometry data unit belongs.

[0464] The FGS ID (fgs_id) represents an indicator indicating a slice at a fine unit level.

[0465] The layer group ID (layer_group_id) represents an indicator of the layer group of a slice. The range of layer_group_id is from 0 to num_layer_groups_minus1. If this value is missing, layer_group_id is assumed to be 0.

[0466] The subgroup_id represents an indicator of a subgroup of the layer group referenced by layer_group_id. The range of subgroup_id is from 0 to num_subgroups_minus1[layer_group_id], where subgroup_id represents the order of the slices within the same layer_group_id. If there is no subgroup_id, it is inferred to be 0.

[0467] The subgroup bounding box origin (subgroup_bbox_origin) represents the location of the subgroup bounding box origin of the nth subgroup, indicated by subgroup_id, of the mth layer group, indicated by layer_group_id.

[0468] The subgroup bounding box size (subgroup_bbox_size) represents the size of the subgroup bounding box of the nth subgroup, indicated by subgroup_id, of the mth layer group, indicated by layer_group_id.

[0469] The point bounding box of a subgroup is described by subgroup_bbox_origin and subgroup_bbox_size. The area within the bounding box of the n-th subgroup does not overlap with the bounding box of the m-th subgroup when n and m are not the same.

[0470] The reference layer group ID (ref_layer_group_id) represents the layer group identifier for the context reference of the current dependent data unit. The range of ref_layer_group_id is from 0 to the layer_group_id of the current dependent data unit.

[0471] The reference subgroup ID (ref_subgroup_id) represents an indicator indicating the reference subgroup of the layer group denoted by ref_layer_group_id. The range of ref_subgroup_id is from 0 to the value of num_subgroup_id_ minus 1 of the layer group denoted by ref_layer_group_id.

[0472] If the context_reference_indication_flag is 1, it indicates that the context state of the current dependent data unit is inherited by one or more subsequent dependent data units. If context_reference_indication_flag is 0, it indicates that the context state of the current dependent slice is not inherited by subsequent dependent slices. If it does not exist, the context_reference_indication_flag of the data unit is inferred to be 1.

[0473] The decoder can manage the context buffer using context_reference_indication_flag. If context_reference_indication_flag is 1, the context state of the current dependent slice is stored in the context buffer when the decoding of the current data unit is finished. If context_reference_indication_flag is 0, the context state of the current dependent slice is not stored in the context buffer.

[0474] The number of subsequent subgroups (num_subsequent_subgroups) indicates the number of subsequent dependent data units that reference the current data unit or dependent data unit.

[0475] FIG. 31 shows geometry octree coding according to embodiments.

[0476] The method according to the embodiments samples and stores the index of a point based on a small LoD of a child point as a sampling index for each intermediate node in geometry octree coding.

[0477] Attribute Slicing:

[0478] Coding Process:

[0479] Encoder Process:

[0480] The attribute slicing encoder is based on partial LoD generation for each subgroup. This method uses less memory because it does not require generating LoDs for the entire point cloud. On the other hand, a process is required to assign corresponding attribute values ​​to the nodes of each intermediate layer group before encoding begins.

[0481] Geometry Encoding:

[0482] 1. Perform octree geometry encoding for each subgroup (data unit).

[0483] a. Encode the partial octree.

[0484] b. For the middle layer group, store the child index of each node.

[0485] 2. For nodes in the middle layer group, calculate a sampled index representing the points of the corresponding LoD by generating a small LoD using the stored child index.

[0486] Recoloring:

[0487] - Recolor the encoded points.

[0488] Attribute encoding:

[0489] Perform attribute encoding for each subgroup.

[0490] a. Quantization Parameter (QP) / Weight Adjustment:

[0491] i. For C1 / C2, quantization parameter delta adaptation

[0492] ii. For CY, weight adjustment

[0493] b. For the middle tier group:

[0494] i. Assign attribute values ​​derived from the recolored points marked by sampled indices to each intermediate node.

[0495] c. Generate a partial LoD for the current subgroup using the intermediate nodes and IDCM points encoded in the current subgroup.

[0496] i. Reverse the sampling direction of octree subsamples for odd and even levels, as in the case of extensible coding.

[0497] d. Encode attribute values ​​based on partial LoD.

[0498] i. Predict the attribute value of each point using the corresponding parent node at the top level of the LoD.

[0499] ii. At other levels, attribute values ​​are predicted using points from the LoD.

[0500] Decoder Process:

[0501] Geometry Decoding:

[0502] 1. Perform octree geometric decoding for each subgroup (data unit).

[0503] a. Decode the partial octree.

[0504] Attribute Decoding:

[0505] 1. Perform attribute decoding for each subgroup.

[0506] a. Generate a partial LoD for the current subgroup using the intermediate nodes and IDCM points encoded in the current subgroup.

[0507] i. Reverse the sample direction of octree subsamples for odd and even levels, as in the case of extensible coding.

[0508] b. Decode attribute values ​​based on partial LoD.

[0509] i. Predict the attribute value of each point using the corresponding parent node at the top level of the LoD.

[0510] ii. At other levels, attribute values ​​are predicted using points from the LoD.

[0511] FIG. 32 shows the APS (attribute parameter set) syntax in a bitstream according to embodiments.

[0512] FIG. 33 shows ASP syntax in a bitstream according to embodiments.

[0513] FIG. 34 shows the syntax of a dependent attribute data unit header within a bitstream according to embodiments.

[0514] If the layer_group_aligned_flag is 1, it indicates that the attribute bitstream contains at least one independent attribute data unit and one dependent data unit, and that each unit contains attribute values ​​corresponding to each decoded point of the independent / dependent geometry data unit. If layer_group_aligned_flag is 0, the attribute bitstream contains only independent attribute data units and attribute values ​​corresponding to all decoded points of the independent / dependent data units.

[0515] The attribute parameter set ID (dadu_attr_parameter_set_id) specifies the active APS as aps_attr_parameter_set_id.

[0516] The attribute index (dadu_sps_attr_idx) identifies the attribute coded as an index of the active SPS attribute list.

[0517] The slice ID (dadu_slice_id) specifies the slice to which the current dependent attribute data unit belongs.

[0518] The layer group ID (dadu_layer_group_id) specifies the identifier for the layer group. The range of dadu_layer_group_id is from 0 to num_layer_groups_minus.

[0519] The subgroup ID (dadu_subgroup_id) specifies the indicator for the subgroup. The range of dadu_subgroup_id is from 0 to num_subgroups_minus1[layer_group_id]. If not present, dadu_subgroup_id is inferred to be 0.

[0520] The predicted coefficient component (dadu_last_comp_pred_coeff_diff[ i ]) specifies the scale factor applied to the second coefficient component of the multi-component attribute to predict the last coefficient component. Each scale factor is applied to the coefficients of a single transformation level i, progressing from the first to the last.

[0521] The inter-pred coeff diff component (dadu_inter_comp_pred_coeff_diff[ i ][ c ]) is applied to the first coeff component of the multi-component attribute and is applied to predict the c-th coeff component. Each scale factor is applied to the coeff of a single transformation level i, proceeding from the first to the last.

[0522] The attribute quantization offset (dadu_attr_qp_offset) specifies whether the DADU header contains the QP offset per data unit, dadu_attr_qp_offset[ c ].

[0523] The attribute quantization layer present (dadu_attr_qp_layers_present) specifies whether DADU has QP offsets per transform layer (if 1) or not (if 0).

[0524] Adding 1 to the attribute quantization layer count (dadu_attr_qp_layer_cnt_minus1) specifies the number of levels in the LoD layer or RAHT tree where the QP offset is signaled (if any).

[0525] The attribute quantization layer offset (dadu_attr_qp_layer_offset[ dpth ][ qc ]) specifies the QP offset used for primary (qc = 0) and secondary (qc = 1) attribute components. Each offset is applied to the transform factor at depth dpth of the LoD layer or RAHT tree. If the number of levels in the LoD layer or RAHT tree is greater than dadu_attr_qp_layer_cnt_minus1 + 1, dadu_attr_qp_layer_offset[dadu_attr_qp_layer_cnt_minus1][qc] also specifies the QP offset for the transform factor at depths greater than dadu_attr_qp_layer_cnt_minus1.

[0526] Note: Dependent property data unit headers do not have an ID representing the parent data unit. It can be inherited from the corresponding dependent geometry data unit header.

[0527] If the attribute reference ID present flag (aps_attr_ref_id_present_flag) is 1, it indicates that the context reference of the attribute slice is indicated by attr_ref_layer_group_id and attr_ref_subgroup_id, and that the use of the attribute slice's context state is indicated by attr_context_reference_indication_flag in the dependent attribute data unit header. If aps_attr_ref_id_present_flag is 0, the context reference and the use of the context state are inherited from the geometry slice corresponding to the layer_group_id and subgroup_id of the current dependent attribute slice.

[0528] The attribute reference layer group ID (attr_ref_layer_group_id) represents the layer group identifier of the context reference of the current dependent attribute data unit. The range of attr_ref_layer_group_id is from 0 to the attr_layer_group_id of the current dependent attribute data unit.

[0529] The attribute reference subgroup ID (attr_ref_subgroup_id) specifies the reference subgroup indicator of the layer group represented by attr_ref_layer_group_id. The range of attr_ref_subgroup_id is from 0 to 1, which is num_subgroup_id of the layer group represented by rattr_ef_layer_group_id minus 1.

[0530] If the attribute context reference indicator flag (attr_context_reference_indication_flag) is 1, it indicates that the context state of the current dependent property slice is inherited by one or more subsequent dependent property slices. If attr_context_reference_indication_flag is 0, it indicates that the context state of the current dependent slice is not inherited by subsequent dependent slices.

[0531] The decoder can manage the context buffer using attr_context_reference_indication_flag. If attr_context_reference_indication_flag is 1, the context state of the current dependent slice is stored in the context buffer at the end of decoding. If attr_context_reference_indication_flag is 0, the context state of the current dependent slice is not stored in the context buffer.

[0532] FIG. 35 shows the delivery sequence of FSG according to the embodiments.

[0533] The method according to the embodiments can deliver FGS units as follows.

[0534] Different data unit orders are possible depending on the target application of FGS. For example, transmitting in FGS units can be advantageous for partial decoding, progressive decoding, etc. (e.g., progressive_decoding_enabled = 1).

[0535] FIG. 36 shows the FGS delivery sequence according to the embodiments.

[0536] Geometry information priority transmission method according to embodiments:

[0537] Depending on the application, geometry information may be relatively important, while attribute information may be optional. In this case, geometry data units can be transmitted first to allow the receiver to quickly access the necessary information. As a method to transmit geometry data units first, the geometry data units can be moved to the front in the previously described FGS unit transmission method (example: data_unit_order_type = 1, progressive_decoding_enabled = 1).

[0538] FIG. 37 shows the FGS delivery sequence according to the embodiments.

[0539] According to the embodiments, FGS may be transmitted in slice geometry and slice attribute units. (Example: data_unit_order_type = 1, progressive_decoding_enabled = 0) In this case, a slice geometry boundary marker and a slice attribute boundary marker may be used to distinguish between slice geometry and slice attribute information.

[0540] FIG. 38 shows the FGS sequence according to the embodiments.

[0541] According to the embodiments, the FGS order can be generated in a manner that prioritizes major regions. When transmitting FGS data units, data units corresponding to major regions may be transmitted first. If the major information is a root layer group, FGS0 may be transmitted first, followed by slice geometry and slice attributes. (Example: data_unit_order_type = 2, progressive_decoding_enabled = 0)

[0542] FIG. 39 shows the FGS sequence according to the embodiments.

[0543] According to embodiments, when a receiver (or decoder) requests information regarding a specific region, the FGS for that region may be transmitted first as shown in FIG. 39, and the remaining regions may be transmitted in slice geometry and slice attribute units. (Example: data_unit_order_type = 2, progressive_decoding_enabled = 0)

[0544] FIG. 40 shows the FGS sequence according to the embodiments.

[0545] According to embodiments, when a receiver requests information about a specific region, the FGS for that region may be transmitted first as in FIG. 40, and the remaining regions may be transmitted in slice geometry and slice attribute units (e.g., data_unit_order_type = 2, progressive_decoding_enabled = 0).

[0546] FIGS. 41 through 43 illustrate the syntax contained in a bitstream. Hereinafter, the syntax of the bitstream parameters will be explained with reference to each figure.

[0547] Referring together to FIGS. 35 to 40, with respect to bitstreams and slices, in claim 1, the bitstream may include slice geometry containing geometry data and slice attribute containing attribute data.

[0548] The order of slice geometry and slice attributes within a bitstream according to the embodiments can be generated according to various types, as shown in FIGS. 35 to 40 described above. The encoding method according to the embodiments can transmit the order of slices by interleaving them in FGS units. The decoding method according to the embodiments can acquire the interleaved FGS unit slices and support partial decoding and spatial random access of point cloud data.

[0549] Referring together with FIG. 25, regarding layer groups, subgroups, FGS geometry, and FGS attributes, geometry data is decoded based on a geometry layer group for one or more subgroups related to the tree levels of the accusation tree, one subgroup regarding geometry data is mapped to FGS (Fine granularity slice) geometry, and attribute data is decoded based on an attribute layer group for one or more subgroups, one subgroup regarding attribute data is mapped to FGS (Fine granularity slice) attributes.

[0550] Referring together with FIG. 42, regarding the FGS-related parameter progressive_decoding_enabled, the method of FIG. 49 further comprises: a step of obtaining information regarding FGS geometry and FGS attributes within a bitstream; and the information regarding FGS geometry and FGS attributes may include a flag (progressive_decoding_enabled) indicating the order of FGS geometry and FGS attributes within the bitstream.

[0551] Referring together with FIG. 35, with respect to progressive_decoding_enabled, based on the case where the flag has a first value (0), FGS geometries in the bitstream are included in priority over FGS attributes.

[0552] Referring together with FIG. 35, regarding progressive_decoding_enabled, based on the case where the flag has a first value (0), the bitstream includes: a first FGS geometry for the first subgroup, a second FGS geometry for the second subgroup, a first FGS attribute for the first subgroup, and a second FGS attribute for the second subgroup.

[0553] Referring to FIG. 35, in relation to progressive_decoding_enabled, FGS geometry and FGS attributes belonging to the same subgroup within the bitstream are combined based on the case where the flag has a second value (1).

[0554] Referring together with FIG. 35, regarding progressive_decoding_enabled, based on the case where the flag has a second value (1), a first FGS geometry for the first subgroup, a first FGS attribute for the first subgroup, a second FGS geometry for the second subgroup, and a second FGS attribute for the second subgroup are included.

[0555] Referring together with FIG. 35, progressive_decoding_enabled can be referred to as fgs_interleaving_enabled, and based on the case where the flag has a second value (1), FGS geometries and FGS attributes within the bitstream are interleaved and included.

[0556] The method of Fig. 49 can correspond to the reverse process of the method of Fig. 48. The decoding method of Fig. 49 can be performed by a decoding device.

[0557] The decoding device includes a memory; and at least one processor connected to the memory; and the at least one processor is configured to: decode geometry data of point cloud data within a bitstream; and decode attribute data of point cloud data.

[0558] The method and apparatus according to the embodiments provide the following technical effects.

[0559] FGS data units (FGS geometry and FGS attributes) can be efficiently transmitted. For example, as a data unit order type, the order can be specified as FGS unit, slice geometry / attribute unit, geometry priority, ROI priority, etc., and additional signaling can be provided in the case of ROI priority. As a result, spatial random access and scalable coding capabilities can be provided while maintaining the compression rate of point cloud data.

[0560] A encoding method according to embodiments (transmitting device (10000) of FIG. 1, point cloud video encoder (10002), transmitter (10003), acquire-encode-transmit (20000-20001-20002) of FIG. 2, encoding of FIG. 11, bitstream segment-based encoding of FIG. 12 to 16, syntax generation of FIG. 17 to 19, NAL unit-based bitstream encoding of FIG. 20, bitstream segment (FGS, Fine Granularity Slice) of FIG. 22, alignment of FGS order of FIG. 23, layer group and subgroup-based encoding of FIG. 24 to 27, syntax generation of FIG. 29a and 29b to 30, octree-based subgroup encoding of FIG. 31, syntax generation of FIG. 32 to 34, FGS alignment of FIG. 35 to 40, syntax generation of FIG. 41 to 43, partial encoding of FIG. 44 to 47, encoding method of FIG. 48) is FIG. 41 Parameters such as those in Fig. 43 can be generated, and a bitstream containing the parameters can be generated.

[0561] Decoding method according to embodiments (receiving device (10004) of FIG. 1, receiver (10005), point cloud video decoder (10006), transmission-decoding-rendering (20002-20003-20004) of FIG. 2, decoder of FIG. 7, receiving device of FIG. 9, device of FIG. 10, decoding of FIG. 11, bitstream segment-based decoding of FIG. 12 to 16, syntax acquisition of FIG. 17 to 19, NAL unit-based bitstream parsing of FIG. 21, bitstream segment (FGS, Fine Granularity Slice)-based decoding of FIG. 22, FGS-based sorting order-based decoding of FIG. 23, layer group and subgroup-based decoding of FIG. 24 to 27, layer group-based decoding of FIG. 28, syntax acquisition of FIG. 29a and 29b to 30, syntax acquisition of FIG. 32 to 34, and FIG. 35 to Fig. 40 FGS alignment order-based decoding, Figs. 41 to 43 syntax acquisition, Figs. 44 to 47 partial coding, Fig. 49 decoding method) can acquire a bitstream containing parameters such as those in Figs. 41 to 43 and acquire parameters.

[0562] FIG. 41 shows the syntax of a sequence parameter set (SPS) of a bitstream according to embodiments.

[0563] FIG. 42 shows the syntax of the FGS parameters of the bitstream according to the embodiments.

[0564] FIG. 43 shows slice boundary markers of a bitstream according to embodiments.

[0565] progressive_decoding_enabled: If 1, it indicates that progressive decoding is supported, and to do this, geometry data units and attribute data units for a specific FGS can be passed as a bundle.

[0566] Data unit order type (data_unit_order_type): If 0, it indicates a case based on coding order. If necessary, detailed types such as depth first order or breadth first order can be distinguished by assigning them separately or defining additional signals. If 1, it indicates a case where the geometry data unit has priority. If 2, it indicates a case where a specific area has priority. In this case, the priority is determined by a request from the receiver, or the data start point can be defined by the data creator or sender; if necessary, a type can be assigned separately for each case or distinguished through additional signaling.

[0567] ROI Bounding Box Origin and ROI Bounding Box Size (roi_bbox_origin, roi_bbox_size): When the data unit order is determined based on the priority of a specific area, location and size information of the determined area can be transmitted. If different coordinate systems are used depending on the timing of data compression, transmission, and output, information regarding the coordinate system can be added and transmitted.

[0568] The slice boundary marker data unit (DU) can explicitly mark the end of a slice.

[0569] Slice ID (slice_id): An identifier for the target slice representing the boundary.

[0570] Data unit type (data_unit_type): If this value is 0, it represents a geometry data unit; if 1, the first attribute data unit; and if 2, the second attribute data unit. n represents the nth attribute data unit.

[0571] end_of_slice: If 1, it indicates the end of the entire slice. If 0, it indicates that one or more of the slice geometry and slice attributes are missing.

[0572] FIG. 44 illustrates partial encoding and partial decoding processes according to embodiments.

[0573] Due to the aforementioned embodiments, compressed data can be divided and transmitted (encoded) and received (decoded) according to any standard for point cloud data. For example, when using layered coding, compressed data can be divided and sent according to the layer, in which case the storage and transmission efficiency of the transmitting end (encoder) is increased.

[0574] FIG. 44 illustrates an example of a service where geometry and attributes of point cloud data are compressed. In PCC-based services, the compression rate or the number of data can be adjusted and sent depending on receiver performance or the transmission environment. However, if point cloud data is bundled into a single slice unit as in the conventional method, when receiver performance or the transmission environment changes, it is necessary to either 1) convert a bitstream suitable for each environment in advance, store it separately, and select it during transmission, or 2) perform a conversion process (transcoding) prior to transmission. In this case, if the number of receiver environments to be supported increases or the transmission environment changes frequently, storage space issues or delays caused by conversion may become problematic.

[0575] Referring to FIG. 11, before transmitting the point cloud data, the point cloud data may be subsampled to suit various decoder performances, and then each subsampled and stored.

[0576] FIG. 45 illustrates partial encoding and partial decoding processes according to embodiments.

[0577] When compressed data is divided and transmitted in NAL unit units according to the layers in accordance with the embodiments, there is an advantage in that only the necessary parts can be selectively transmitted at the bitstream stage using the information in the NAL unit header for the pre-compressed data without a separate conversion process. This is efficient in terms of storage space as only one storage space is required per stream, and efficient transmission is also possible in terms of bandwidth because only the necessary layers are selectively transmitted before transmission (bitstream selector).

[0578] FIG. 46 illustrates scalable encoding and scalable decoding according to embodiments.

[0579] The embodiments divide and transmit (encode) and receive (decode) compressed data according to a standard for point cloud data. When using layered coding according to the embodiments, the compressed data can be divided and sent according to the layer, in which case the efficiency of the receiving end (decoder) increases.

[0580] FIG. 46 illustrates the operation of the transmitting and receiving ends (e.g., encoder and decoder) when transmitting point cloud data composed of layers. In this case, when information capable of reconstructing the entire PCC data is transmitted regardless of the receiver's performance, the receiver requires a process (data selection or sub-sampling) to select only the data corresponding to the required layer after reconstructing the point cloud data through decoding. In this case, since the transmitted bitstream is already decoded, a delay may occur in a receiver aiming for low latency, or decoding may not be possible depending on the receiver's performance.

[0581] FIG. 47 illustrates partial encoding and partial decoding according to embodiments.

[0582] According to the embodiments, when a bitstream is divided into slice units and transmitted, the receiver (decoder) may selectively transmit the bitstream to the decoder based on the density of the point cloud data to be represented, depending on the decoder performance or application field. At this time, the information of the layer to be selected can be selected at the NAL unit level through the nal_spatial_id and nal_data_type of the NAL unit header. In this case, since the selection is made before decoding, the decoder efficiency is increased, and there is an advantage that a single bitstream can support decoders of various performance levels.

[0583] FIG. 48 illustrates a encoding method according to embodiments.

[0584] The method according to the embodiments may include the step of encoding geometry data of point cloud data (S4800); the step of encoding attribute data of point cloud data (S4810), etc.

[0585] The method of FIG. 48 can be performed by an encoding device. The encoding device includes a memory; and at least one processor connected to the memory; and the at least one processor is configured to: encode geometry data of point cloud data; and encode attribute data of point cloud data.

[0586] The embodiments further include a computer-readable storage medium that stores a bitstream generated by the method according to FIG. 48.

[0587] The embodiments further include a method comprising the steps of: acquiring a bitstream for point cloud data; generating the bitstream based on the steps of encoding geometry data of the point cloud data and encoding attribute data of the point cloud data; and transmitting data including the bitstream.

[0588] FIG. 49 illustrates a decoding method according to embodiments.

[0589] The method according to the embodiments may include the step of decoding geometry data of point cloud data in a bitstream (S4900), the step of decoding attribute data of point cloud data (S4910), etc.

[0590] Referring together with FIG. 35, with respect to the bitstream and slice, in claim 1, the bitstream may include a slice geometry containing geometry data and a slice attribute containing attribute data.

[0591] Referring together with FIG. 25, regarding layer groups, subgroups, FGS geometry, and FGS attributes, geometry data is decoded based on a geometry layer group for one or more subgroups related to the tree levels of the accusation tree, one subgroup regarding geometry data is mapped to FGS (Fine granularity slice) geometry, and attribute data is decoded based on an attribute layer group for one or more subgroups, one subgroup regarding attribute data is mapped to FGS (Fine granularity slice) attributes.

[0592] Referring together with FIG. 42, regarding the FGS-related parameter progressive_decoding_enabled, the method of FIG. 49 further comprises: a step of obtaining information regarding FGS geometry and FGS attributes within a bitstream; and the information regarding FGS geometry and FGS attributes may include a flag (progressive_decoding_enabled) indicating the order of FGS geometry and FGS attributes within a bitstream.

[0593] Referring together with FIG. 35, with respect to progressive_decoding_enabled, based on the case where the flag has a first value (0), FGS geometries in the bitstream are included in priority over FGS attributes.

[0594] Referring together with FIG. 35, regarding progressive_decoding_enabled, based on the case where the flag has a first value (0), the bitstream includes: a first FGS geometry for the first subgroup, a second FGS geometry for the second subgroup, a first FGS attribute for the first subgroup, and a second FGS attribute for the second subgroup.

[0595] Referring to FIG. 35, in relation to progressive_decoding_enabled, FGS geometry and FGS attributes belonging to the same subgroup within the bitstream are combined based on the case where the flag has a second value (1).

[0596] Referring together with FIG. 35, regarding progressive_decoding_enabled, based on the case where the flag has a second value (1), a first FGS geometry for the first subgroup, a first FGS attribute for the first subgroup, a second FGS geometry for the second subgroup, and a second FGS attribute for the second subgroup are included.

[0597] Referring together with FIG. 35, progressive_decoding_enabled can be referred to as fgs_interleaving_enabled, and based on the case where the flag has a second value (1), FGS geometries and FGS attributes within the bitstream are interleaved and included.

[0598] The method of Fig. 49 can correspond to the reverse process of the method of Fig. 48. The decoding method of Fig. 49 can be performed by a decoding device.

[0599] The decoding device includes a memory; and at least one processor connected to the memory; and the at least one processor is configured to: decode geometry data of point cloud data within a bitstream; and decode attribute data of point cloud data.

[0600] The method and apparatus according to the embodiments provide the following technical effects.

[0601] FGS data units (FGS geometry and FGS attributes) can be efficiently transmitted. For example, as a data unit order type, the order can be specified as FGS unit, slice geometry / attribute unit, geometry priority, ROI priority, etc., and additional signaling can be provided in the case of ROI priority. As a result, spatial random access and scalable coding capabilities can be provided while maintaining the compression rate of point cloud data.

[0602] The embodiments have been described in terms of methods and / or devices, and the description of the methods and the description of the devices may be applied complementarily.

[0603] Although the drawings have been described separately for the convenience of explanation, it is also possible to design a new embodiment by combining the embodiments described in each drawing. Furthermore, designing a computer-readable recording medium containing a program for executing the previously described embodiments, as required by a person skilled in the art, falls within the scope of the claims of the embodiments. The apparatus and method according to the embodiments are not limited to the configuration and method of the embodiments described above; rather, the embodiments may be configured by selectively combining all or part of each embodiment to allow for various modifications. Although preferred embodiments have been illustrated and described, the embodiments are not limited to the specific embodiments described above. It is not only possible for a person skilled in the art to make various modifications without departing from the essence of the embodiments claimed in the claims, but such modifications should not be understood individually from the technical concept or perspective of the embodiments.

[0604] Various components of the device of the embodiments may be implemented by hardware, software, firmware, or a combination thereof. Various components of the embodiments may be implemented as a single chip, for example, a single hardware circuit. Depending on the embodiments, the components according to the embodiments may each be implemented as separate chips. Depending on the embodiments, at least one of the components of the device according to the embodiments may be composed of one or more processors capable of executing one or more programs, and one or more programs may include instructions for performing or executing any one or more of the operations / methods according to the embodiments. Executable instructions for performing the methods / operations of the device according to the embodiments may be stored in non-transient CRMs or other computer program products configured to be executed by one or more processors, or may be stored in transient CRMs or other computer program products configured to be executed by one or more processors. Additionally, memory according to the embodiments may be used as a concept that includes not only volatile memory (e.g., RAM, etc.) but also non-volatile memory, flash memory, PROM, etc. In addition, it may also include implementation in the form of carrier waves, such as transmission over the Internet. Furthermore, processor-readable recording media are distributed across networked computer systems, allowing processor-readable code to be stored and executed in a distributed manner.

[0605] In this document, “ / ” and “,” are interpreted as “and / or.” For example, “A / B” is interpreted as “A and / or B,” and “A, B” is interpreted as “A and / or B.” Additionally, “A / B / C” means “at least one of A, B and / or C.” Also, “A, B, C” means “at least one of A, B and / or C.” Additionally, in this document, “or” is interpreted as “and / or.” For example, “A or B” may mean 1) “A” alone, 2) “B” alone, or 3) “A and B.” In other words, “or” in this document may mean “additionally or alternatively.”

[0606] Terms such as "first," "second," etc., may be used to describe various components of the embodiments. However, the interpretation of the various components according to the embodiments should not be limited by these terms. These terms are merely used to distinguish one component from another. For example, the first user input signal may be referred to as the second user input signal. Similarly, the second user input signal may be referred to as the first user input signal. The use of these terms should be interpreted as not departing from the scope of the various embodiments. Although the first user input signal and the second user input signal are both user input signals, they do not imply the same user input signals unless clearly indicated in the context.

[0607] The terms used to describe the embodiments are intended for the purpose of describing specific embodiments and are not intended to limit the embodiments. As used in the description of the embodiments and in the claims, the singular is intended to include the plural unless explicitly indicated in the context. Expressions of and / or are used to mean including all possible combinations between the terms. Expressions of include describe the presence of features, numbers, steps, elements, and / or components and do not imply the exclusion of additional features, numbers, steps, elements, and / or components. Conditional expressions such as "if" or "when" used to describe the embodiments are not limited to being optional. It is intended to be interpreted as "when a specific condition is satisfied," "when a related action is performed in response to a specific condition," or "when a related definition is interpreted."

[0608] Additionally, operations according to the embodiments described herein may be performed by a transmitting and receiving device including memory and / or a processor, depending on the embodiments. The memory may store programs for processing / controlling operations according to the embodiments, and the processor may control various operations described in this document. The processor may be referred to as a controller, etc. Operations in the embodiments may be performed by firmware, software, and / or a combination thereof, and the firmware, software, and / or a combination thereof may be stored in the processor or in memory.

[0609] Meanwhile, the operation according to the embodiments described above may be performed by a transmitting device and / or a receiving device according to the embodiments. The transmitting and receiving device may include a transmitting and receiving unit for transmitting and receiving media data, a memory for storing instructions (program code, algorithm, flowchart and / or data) for a process according to the embodiments, and a processor for controlling the operations of the transmitting and receiving devices.

[0610] The processor may be referred to as a controller, etc., and may correspond, for example, to hardware, software, and / or a combination thereof. The operation according to the embodiments described above may be performed by the processor. Additionally, the processor may be implemented as an encoder / decoder, etc., for the operation of the embodiments described above.

[0611] As described above, the relevant details have been explained in the best mode for carrying out the embodiments.

[0612] As described above, the embodiments may be applied wholly or partially to point cloud data transmission and reception devices and systems.

[0613] Those skilled in the art may make various changes or modifications to the embodiments within the scope of the embodiments.

[0614] The embodiments may include modifications / variations, and such modifications / variations do not exceed the scope of the claims and their equivalents.

Claims

1. A step of decoding geometry data of point cloud data within a bitstream; and A step of decoding attribute data of the above point cloud data; comprising Decryption method.

2. In Paragraph 1, The bitstream comprises a slice including a slice geometry including the geometry data and a slice attribute including the attribute data. Decryption method.

3. In Paragraph 1, The above geometry data is decoded based on a group of geometry layers for one or more subgroups related to the tree levels of the accusation tree, and one subgroup related to the geometry data is mapped to FGS (Fine granularity slice) geometry, and The above attribute data is decoded based on an attribute layer group for one or more subgroups, and one subgroup of the attribute data is mapped to an FGS (Fine granularity slice) attribute. Decryption method.

4. In paragraph 3, the above method is: The method further includes the step of obtaining information regarding the FGS geometry and the FGS attributes within the bitstream; Information regarding the above FGS geometry and above FGS attributes is: including a flag indicating the order of the FGS geometry and FGS attributes within the bittree, Decryption method.

5. In Paragraph 4, Based on the case where the above flag has a first value, FGS geometries within the above bitstream are included prior to FGS attributes, Decryption method.

6. In Paragraph 4, Based on the case where the above flag has a first value, within the bitstream: 1st FGS geometry for 1st subgroup, Second FGS geometry for the second subgroup, The first FGS attribute for the above first subgroup, A second FGS attribute for the second subgroup above is included, Decryption method.

7. In Paragraph 4, Based on the case where the above flag has a second value, within the bitstream FGS geometry and FGS attributes belonging to the same subgroup are combined, Decryption method.

8. In Paragraph 7, Based on the case where the above flag has a second value, 1st FGS geometry for 1st subgroup, The first FGS attribute for the above first subgroup, Second FGS geometry for the second subgroup, A second FGS attribute for the second subgroup above is included, Decryption method.

9. In Paragraph 7, Based on the case where the above flag has a second value, FGS geometries and FGS attributes within the above bitstream are interleaved and included, Decryption method.

10. Memory; and At least one processor connected to the memory; comprising, wherein the at least one processor: Decoding geometry data of point cloud data within a bitstream; and Decoding attribute data of the above point cloud data; configured to do so, Decoding device.

11. A step of encoding the geometry data of the point cloud data; and A step of encoding attribute data of the above point cloud data; comprising Encoding method.

12. In Paragraph 11, The geometry data and the attribute data are included in the bitstream, and The bitstream comprises a slice including a slice geometry including the geometry data and a slice attribute including the attribute data. Encoding method.

13. Memory; and At least one processor connected to the memory; comprising, wherein the at least one processor: Encoding the geometry data of the point cloud data; and Configured to encode the attribute data of the above point cloud data; Decoding device.

14. A computer-readable storage medium for storing a bitstream generated by the method according to paragraph 11.

15. Step for acquiring a bitstream for point cloud data, The bitstream is generated based on the step of encoding geometry data of the point cloud data; and the step of encoding attribute data of the point cloud data; and A method comprising the step of transmitting data including the bitstream above.

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