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

Abstract

A decoding method according to embodiments may comprise the steps of: decoding geometry data of point cloud data in a bitstream; and decoding attribute data of the point cloud data. An encoding 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] Embodiments relate to a method and apparatus for processing point cloud content.

[0002] Point cloud content is represented as a point cloud, a collection of points within a coordinate system representing 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 to hundreds of thousands of point data. Therefore, a method for efficiently processing massive amounts of point data is required.

[0003] Embodiments provide devices and methods for efficiently processing point cloud data. Embodiments provide methods and devices for processing point cloud data to address latency and encoding / decoding complexity.

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

[0005] A decoding method according to embodiments may include a step of decoding geometry data of point cloud data in a bitstream; and a step of decoding attribute data of the point cloud data. A encoding method according to embodiments may include a step of encoding geometry data of the point cloud data; and a step of encoding attribute data of the 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 services such as VR services and autonomous driving services.

[0009] The drawings are included to further understand the embodiments, and the drawings illustrate the embodiments together with the description related to the embodiments. For a better understanding of the various embodiments described below, reference should be made to the following description of the embodiments in conjunction with the following drawings, in which like reference numerals correspond to corresponding parts throughout the drawings.

[0010] Figure 1 illustrates 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 illustrates an example of a point cloud encoder according to embodiments.

[0013] Figure 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 illustrates an example of a point cloud decoder according to embodiments.

[0017] Figure 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] Figure 11 illustrates the encoding, transmission, and decoding processes of point cloud data according to embodiments.

[0021] Figure 12 illustrates a layer-based point cloud data configuration according to embodiments.

[0022] Figure 13 illustrates a geometry and attribute bitstream structure according to embodiments.

[0023] Figure 14 shows a bitstream configuration according to embodiments and a bitstream alignment method according to embodiments.

[0024] Figure 15 illustrates a bitstream alignment method according to embodiments.

[0025] Figure 16 illustrates a bitstream alignment method according to embodiments.

[0026] Figure 17 illustrates a method for selecting geometry data and attribute data according to embodiments.

[0027] Figure 18 shows a bitstream selection method according to embodiments.

[0028] FIG. 19 illustrates a method for constructing a slice including point cloud data according to embodiments.

[0029] Figure 20 shows a geometry coding layer structure according to embodiments.

[0030] Figure 21 shows the layer group and subgroup structure according to embodiments.

[0031] Figure 22 shows a multi-resolution, multi-size ROI according to embodiments.

[0032] Figure 23 illustrates a layer group slice according to embodiments.

[0033] Figure 24 illustrates an encoding process for layer group slicing according to embodiments.

[0034] Figure 25 illustrates a decoder process for layer group slicing according to embodiments.

[0035] Figure 26 shows a bitstream configuration according to embodiments.

[0036] Figures 27a and 27b illustrate sequence parameter sets (SPS) according to embodiments.

[0037] Figure 28 illustrates a dependent geometry data unit header according to embodiments.

[0038] Figure 29 shows a layer group structure inventory according to embodiments.

[0039] Figure 30 shows the transmission and reception operation of point cloud data according to layers according to embodiments.

[0040] Figure 31 shows the transmission and reception operation of point cloud data according to layers according to embodiments.

[0041] Figure 32 shows an encoding method according to embodiments.

[0042] Figure 33 shows a decryption method according to embodiments.

[0043] Preferred embodiments of the embodiments are described in detail, examples of which are illustrated in the accompanying drawings. The following detailed description, with reference to the accompanying drawings, is intended to illustrate preferred embodiments of the embodiments, rather than merely show embodiments that can be implemented according to the embodiments. The following detailed description includes details to provide a thorough understanding of the embodiments. However, it will be apparent to those skilled in the art that the embodiments may be practiced without these details.

[0044] While most of the terms used in the examples are commonly used in the field, some terms were arbitrarily selected by the applicant, and their meanings are described in detail in the following descriptions as needed. Therefore, the examples should be understood based on the intended meaning of the terms, not simply their names or meanings.

[0045] Figure 1 illustrates an example of a point cloud content provision system according to embodiments.

[0046] The point cloud content provision system illustrated in FIG. 1 may include a transmission device (10000) and a reception device (10004). The transmission device (10000) and the reception device (10004) are capable of wired and wireless communication to transmit and receive point cloud data.

[0047] A transmission device (10000) according to embodiments can secure, process, and transmit a point cloud video (or point cloud content). According to embodiments, the transmission device (10000) can include a fixed station, a base transceiver system (BTS), a network, an Artificial Intelligence (AI) device and / or system, a robot, an AR / VR / XR device and / or a server, etc. In addition, according to embodiments, the transmission device (10000) can include a device that performs communication with a base station and / or other wireless devices using a wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)), a robot, a vehicle, an AR / VR / XR device, a portable device, a home appliance, an IoT (Internet of Things) device, an AI device / server, etc.

[0048] A transmission device (10000) according to embodiments includes a point cloud video acquisition unit (Point Cloud Video Acquisition, 10001), a point cloud video encoder (Point Cloud Video Encoder, 10002), and / or a transmitter (or communication module, 10003).

[0049] A point cloud video acquisition unit (10001) according to embodiments acquires a point cloud video through a processing process such as capture, synthesis, or generation. The point cloud video is point cloud content expressed as a point cloud, which is a collection 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 embodiments may include one or more frames. One frame represents a still image / picture. Therefore, 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.

[0050] A point cloud video encoder (10002) according to embodiments encodes acquired point cloud video data. The point cloud video encoder (10002) may encode point cloud video data based on point cloud compression coding. The point cloud compression coding according to embodiments may include G-PCC (Geometry-based Point Cloud Compression) coding and / or V-PCC (Video-based Point Cloud Compression) coding or next-generation coding. In addition, the point cloud compression coding according to embodiments is not limited to the above-described embodiment. The point cloud video encoder (10002) may output a bitstream including encoded point cloud video data. The bitstream may include not only encoded point cloud video data but also signaling information related to encoding of the point cloud video data.

[0051] A transmitter (10003) according to embodiments transmits a bitstream including encoded point cloud video data. The bitstream according to embodiments is encapsulated into a file or segment (e.g., streaming segment) and transmitted through various networks such as a broadcast network and / or a broadband network. Although not shown in the drawing, the transmission device (10000) may include an encapsulation unit (or an encapsulation module) that performs an encapsulation operation. In addition, the encapsulation unit may be included in the transmitter (10003) according to embodiments. According to embodiments, the file or segment may be transmitted to a receiving device (10004) through a network or may be stored in a digital storage medium (e.g., USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.). The transmitter (10003) according to embodiments may communicate with the receiving device (10004) (or receiver (10005)) via a network such as 4G, 5G, or 6G via wired / wireless communication. Additionally, the transmitter (10003) can perform data processing operations required according to a network system (e.g., a communication network system such as 4G, 5G, or 6G). Additionally, the transmission device (10000) can transmit encapsulated data in an on-demand manner.

[0052] A receiving device (10004) according to embodiments includes a receiver (Receiver) 10005, a point cloud video decoder (Point Cloud Decoder) 10006, and / or a renderer (Renderer) 10007. According to embodiments, the receiving device (10004) may include a device, robot, vehicle, AR / VR / XR device, mobile device, home appliance, IoT (Internet of Things) device, AI device / server, etc. that performs communication with a base station and / or other wireless devices using a wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)).

[0053] A receiver (10005) according to embodiments receives a bitstream containing point cloud video data or a file / segment in which the bitstream is encapsulated, from a network or a storage medium. The receiver (10005) may perform data processing operations required according to a network system (e.g., a communication network system such as 4G, 5G, or 6G). The receiver (10005) according to embodiments may decapsulate the received file / segment and output a bitstream. In addition, the receiver (10005) according to embodiments may include a decapsulation unit (or decapsulation module) for performing the decapsulation operation. In addition, the decapsulation unit may be implemented as a separate element (or component) from the receiver (10005).

[0054] 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 how it is encoded (e.g., the reverse process of the operation of the point cloud video encoder (10002)). Accordingly, the point cloud video decoder (10006) can decode the point cloud video data by performing point cloud decompression coding, which is the reverse process of point cloud compression. The point cloud decompression coding includes G-PCC coding.

[0055] The renderer (10007) renders decoded point cloud video data. The renderer (10007) can output point cloud content by rendering not only point cloud video data but also audio data. According to embodiments, the renderer (10007) may include a display for displaying the point cloud content. According to embodiments, the display may not be included in the renderer (10007) but may be implemented as a separate device or component.

[0056] The dotted arrows in the drawing indicate the transmission path of feedback information acquired from the receiving device (10004). The feedback information is information for reflecting the interaction with the user consuming the point cloud content, and includes information about the user (e.g., head orientation information, viewport information, etc.). In particular, when the point cloud content is content for a service requiring interaction with the user (e.g., autonomous driving service, etc.), the feedback information may be transmitted to the content transmitter (e.g., the transmitting device (10000)) and / or the service provider. Depending on the embodiments, the feedback information may be used by the receiving device (10004) as well as the transmitting device (10000), or may not be provided.

[0057] Head orientation information according to embodiments is information about the position, direction, angle, movement, etc. of the user's head. The receiving device (10004) according to embodiments can calculate viewport information based on the head orientation information. The viewport information is information about the area of ​​the point cloud video that the user is looking at. The viewpoint is the point where the user is looking at the point cloud video, and may mean the exact center point of the viewport area. In other words, the viewport is an area centered on the viewpoint, and the size, shape, etc. of the area can be determined by the FOV (Field Of View). 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 area of ​​the point cloud video that 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). The feedback information according to embodiments may be acquired during a rendering and / or display process. The feedback information according to embodiments may be acquired by one or more sensors included in the receiving device (10004). Additionally, according to embodiments, the feedback information may be acquired by a renderer (10007) or a separate external element (or device, component, etc.). The dotted line in Fig. 1 represents a transmission process of feedback information acquired by the renderer (10007). The point cloud content providing system may process (encode / decode) point cloud data based on the feedback information. Therefore, 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 point cloud video data encoder (10002)) can perform an encoding operation based on the feedback information. Therefore, the point cloud content providing system can efficiently process necessary data (e.g., point cloud data corresponding to the user's head position) based on the feedback information without processing (encoding / decoding) all point cloud data, and provide point cloud content to the user.

[0058] According to embodiments, the transmitting device (10000) may be referred to as an encoder, a transmitting device, a transmitter, etc., and the receiving device (10004) may be referred to as a decoder, a receiving device, a receiver, etc.

[0059] Point cloud data processed (processed through a series of processes of acquisition / encoding / transmission / decoding / rendering) in the point cloud content providing system of FIG. 1 according to embodiments may be referred to as point cloud content data or point cloud video data. According to embodiments, point cloud content data may be used as a concept including metadata or signaling information related to point cloud data.

[0060] The elements of the point cloud content provision system illustrated in FIG. 1 may be implemented by hardware, software, a processor, and / or a combination thereof.

[0061] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0062] 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).

[0063] A point cloud content providing system according to 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 expressed as a point cloud belonging to a coordinate system representing a three-dimensional space. The point cloud video according to embodiments can 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 can include one or more Ply files. The Ply file includes point cloud data such as the geometry and / or attributes of points. The geometry includes the positions of points. The position of each point can be expressed as parameters (e.g., values ​​of each of the X-axis, Y-axis, and Z-axis) representing a three-dimensional coordinate system (e.g., a coordinate system composed of XYZ axes). Attributes include attributes of points (e.g., texture information of each point, color (YCbCr or RGB), reflectance (r), transparency, etc.). A point has one or more attributes (or properties). For example, a point may have one attribute, color, or two attributes, 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.Additionally, 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 a point cloud video (e.g., depth information, color information, etc.).

[0064] A point cloud content providing system according to embodiments (e.g., a transmission device (10000) or a point cloud video encoder (10002)) 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 can include geometry and attributes of points. Therefore, the point cloud content providing system can perform geometry encoding to encode geometry and output a geometry bitstream. The point cloud content providing system can perform attribute encoding to encode attributes and output an attribute bitstream. According to embodiments, the point cloud content providing system can perform attribute encoding based on geometry encoding. The geometry bitstream and the attribute bitstream according to embodiments can be multiplexed and output as a single bitstream. A bitstream according to embodiments may further include signaling information related to geometry encoding and attribute encoding.

[0065] 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 expressed as a geometry bitstream and an attribute bitstream. In addition, the encoded point cloud data can be transmitted in the form of a bitstream together with signaling information related to encoding of the point cloud data (e.g., signaling information related to geometry encoding and attribute encoding). In addition, the point cloud content providing system can encapsulate a bitstream that transmits the encoded point cloud data and transmit it in the form of a file or segment.

[0066] A point cloud content providing system according to embodiments (e.g., a receiving device (10004) or a receiver (10005)) can receive a bitstream including encoded point cloud data. In addition, the point cloud content providing system (e.g., a receiving device (10004) or a receiver (10005)) can demultiplex the bitstream.

[0067] 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. The point cloud content providing system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can decode the point cloud video data based on signaling information related to encoding of the point cloud video data included in the bitstream. The 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 positions (geometry) of points. The point cloud content providing system can decode the attribute bitstream based on the restored geometry to restore attributes of points. A point cloud content provision system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can reconstruct a point cloud video based on positions and decoded attributes according to the reconstructed geometry.

[0068] A point cloud content providing system (e.g., a receiving device (10004) or a renderer (10007)) according to embodiments 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 the decoded geometry and attributes 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 certain minimum size centered on the vertex position, or circles centered on the vertex position. All or a portion of the rendered point cloud content is provided to a user through a display (e.g., a VR / AR display, a general display, etc.).

[0069] A point cloud content provision system according to embodiments (e.g., a receiving device (10004)) can obtain feedback information (20005). The point cloud content provision system can encode and / or decode point cloud data based on the feedback information. The feedback information and the operation of the point cloud content provision system according to embodiments are identical to the feedback information and operation described in FIG. 1, and therefore, a detailed description thereof will be omitted.

[0070] FIG. 3 illustrates an example of a point cloud encoder according to embodiments.

[0071] FIG. 3 illustrates an example of a 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 an encoding operation to adjust the quality of point cloud content (e.g., lossless, lossy, near-lossless) depending on network conditions or applications. If the total size of the point cloud content is large (e.g., point cloud content of 60 Gbps at 30 fps), the point cloud content provision system may not be able to stream the content in real time. Therefore, the point cloud content provision system can reconstruct the point cloud content based on the maximum target bitrate in order to provide it according to the network environment, etc.

[0072] 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.

[0073] The point cloud encoder according to the embodiments includes 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), a LOD generation unit (Generated LOD, 30009), a lifting transformation unit (Lifting) (30010), and a coefficient quantization unit (Quantize Coefficients, 30011) and / or an arithmetic encoder (30012).

[0074] The coordinate system transformation unit (30000), the quantization unit (30001), the octree analysis unit (30002), the surface approximation analysis unit (30003), the arithmetic encoder (30004), and the geometry reconstruction unit (30005) can perform geometry encoding. The geometry encoding according to the embodiments can include octree geometry coding, direct coding, trisoup geometry encoding, and entropy encoding. Direct coding and trisoup geometry encoding are applied selectively or in combination. In addition, the geometry encoding is not limited to the above examples.

[0075] As illustrated in the drawing, a coordinate system conversion unit (30000) according to embodiments receives positions and converts them into coordinates. For example, the positions may be converted into location information of a three-dimensional space (e.g., a three-dimensional space expressed in an XYZ coordinate system, etc.). The location information of the three-dimensional space according to embodiments may be referred to as geometry information.

[0076] A quantization unit (30001) according to embodiments quantizes geometry. For example, the quantization unit (30001) may quantize points based on the minimum position value of all points (e.g., the minimum value on each axis for the X-axis, Y-axis, and Z-axis). The quantization unit (30001) performs a quantization operation of multiplying the difference between the minimum position value and the position value of each point by a preset quantization scale value, and then rounding down or up to find the closest integer value. Accordingly, one or more points may have the same quantized position (or position value). The quantization unit (30001) according to embodiments performs voxelization based on the quantized positions to reconstruct the quantized points. The minimum unit containing two-dimensional image / video information is a pixel, and points of point cloud content (or three-dimensional point cloud video) according to embodiments may be included in one or more voxels. A voxel is a combination of a volume and a pixel, and refers to a three-dimensional cubic space 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) may match groups of points in the three-dimensional space to voxels. According to embodiments, one voxel may include only one point. According to embodiments, one voxel may include one or more points. In addition, in order to express one voxel as one point, the position of the center of the voxel may be set based on the positions of one or more points included in one voxel. In this case, the attributes of all positions contained in one voxel can be combined and assigned to the voxel.

[0077] The octree analysis unit (30002) according to the embodiments performs octree geometry coding (or octree coding) to represent voxels in an octree structure. The octree structure represents points matched to voxels based on an octree structure.

[0078] 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 and voxelizing an area including a large number of points to efficiently provide an octree and voxelization.

[0079] An arithmetic encoder (30004) according to embodiments entropy encodes an octree and / or an approximated octree. For example, the encoding method includes an arithmetic encoding method. The encoding results in a geometry bitstream.

[0080] The color conversion unit (30006), the attribute conversion unit (30007), the RAHT conversion unit (30008), the LOD generation unit (30009), the lifting conversion unit (30010), the coefficient quantization unit (30011) and / or the arithmetic encoder (30012) perform attribute encoding. As described above, one point may have one or more attributes. Attribute encoding according to embodiments is applied equally to the attributes of one point. However, when one attribute (e.g., color) includes one or more elements, independent attribute encoding is applied to each element. Attribute encoding according to embodiments may include color transform coding, attribute transform coding, RAHT (Region Adaptive Hierarchial Transform) coding, Interpolarization-based hierarchical nearest-neighbor prediction-Prediction Transform) coding, and lifting transform (interpolation-based hierarchical nearest-neighbor prediction with an update / lifting step (Lifting Transform)) coding. Depending on the point cloud content, the above-described RAHT coding, prediction transform coding, and lifting transform coding may be selectively used, or a combination of one or more codings may be used. In addition, attribute encoding according to embodiments is not limited to the above-described examples.

[0081] The color conversion unit (30006) according to the embodiments performs color conversion coding to convert color values ​​(or textures) included in attributes. For example, the color conversion unit (30006) may 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 optionally applied depending on the color values ​​included in the attributes.

[0082] 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 a reconstructed geometry (or restored geometry).

[0083] The attribute conversion unit (30007) according to the embodiments performs attribute conversion that converts attributes based on positions for which geometry encoding has not been performed and / or reconstructed geometry. As described above, since the attributes are dependent on the geometry, the attribute conversion unit (30007) can convert the attributes based on the reconstructed geometry information. For example, the attribute conversion unit (30007) can convert the attribute of a point at a position based on the position value of the point included in the voxel. As described above, when the position of the center point of a voxel is set based on the positions of one or more points included in the voxel, the attribute conversion unit (30007) converts the attributes of one or more points. When try-soup geometry encoding is performed, the attribute conversion unit (30007) can convert attributes based on the try-soup geometry encoding.

[0084] The attribute transformation unit (30007) can perform attribute transformation by calculating the average value of the attributes or attribute values ​​(e.g., the color or reflectance of each point) of neighboring points within a specific position / radius from the position (or position value) of the center point of each voxel. The attribute transformation unit (30007) can apply a weight according to the distance from the center point to each point when calculating the average value. Accordingly, each voxel has a position and a calculated attribute (or attribute value).

[0085] The attribute transformation unit (30007) can search for neighboring points within a specific position / radius from the position of the center point of each voxel based on the KD tree or the Moulton code. The KD tree is a binary search tree that supports a data structure that can manage points based on their positions to enable fast nearest neighbor search (NNS). The Moulton code represents the coordinate values ​​(e.g. (x, y, z)) indicating the 3D positions of all points as bit values ​​and is generated by mixing the bits. For example, if the coordinate values ​​indicating the position of a point are (5, 9, 1), the bit values ​​of the coordinate values ​​are (0101, 1001, 0001). If the bit values ​​are mixed in the order of z, y, and x according to the bit index, it is 010001000111. If this value is expressed in decimal, it becomes 1095. That is, the Moulton code value of the point with coordinate values ​​(5, 9, 1) is 1095. The attribute transformation unit (30007) can sort points based on the Moulton code value and perform nearest neighbor search (NNS) through a depth-first traversal process. After the attribute transformation operation, if nearest neighbor search (NNS) is also required in other transformation processes for attribute coding, a KD tree or Moulton code is utilized.

[0086] As shown in the drawing, the converted attributes are input to the RAHT conversion unit (30008) and / or the LOD generation unit (30009).

[0087] The RAHT transform unit (30008) according to the embodiments performs RAHT coding to predict attribute information based on reconstructed geometry information. For example, the RAHT transform unit (30008) can predict attribute information of a node at an upper level of an octree based on attribute information associated with a node at a lower level of the octree.

[0088] The LOD generation unit (30009) according to the embodiments generates a LOD (Level of Detail) to perform predictive transformation coding. The LOD according to the embodiments represents the level of detail of point cloud content. A smaller LOD value indicates lower detail of point cloud content, and a larger LOD value indicates higher detail of point cloud content. Points can be classified according to LOD.

[0089] The lifting transformation unit (30010) according to the embodiments performs lifting transformation coding that transforms attributes of a point cloud based on weights. As described above, lifting transformation coding may be applied selectively.

[0090] The coefficient quantization unit (30011) according to the embodiments quantizes attribute-coded attributes based on coefficients.

[0091] An arithmetic encoder (30012) according to embodiments encodes quantized attributes based on arithmetic coding.

[0092] The elements of the point cloud encoder of FIG. 3 may be implemented by hardware, software, firmware, or a combination thereof, including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing device, although not shown in the drawing. The one or more processors may perform at least one or more of the operations and / or functions of the elements of the point cloud encoder of FIG. 3 described above. Furthermore, the 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. The one or more memories according to embodiments may include high-speed random access memory, or 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).

[0093] Figure 4 illustrates examples of octree and occupancy codes according to embodiments.

[0094] As described in FIGS. 1 to 3, the point cloud content provision system (point cloud video encoder (10002)) or point cloud encoder (e.g., octree analysis unit (30002)) performs octree geometry coding (or octree coding) based on an octree structure to efficiently manage the area and / or position of a voxel.

[0095] The top of Fig. 4 shows the octree structure. The three-dimensional space of the point cloud content according to the embodiments is expressed 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 ) is generated by recursively subdividing the cubical axis-aligned bounding box defined by . 2d can be set to a value that constitutes the smallest bounding box that encloses all points of the point cloud content (or point cloud video). d represents the depth of the octree. The value of d is determined by 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.

[0096] d =Ceil(Log2(Max(x_n^int,y_n^int,z_n^int,n=1,…,N)+1))

[0097] As shown in the middle of the upper part of Fig. 4, the entire three-dimensional space can be divided into eight spaces according to the division. Each divided space is expressed as a cube with six faces. As shown in the upper right of Fig. 4, each of the eight spaces is again divided based on the axes of the coordinate system (e.g., X-axis, Y-axis, Z-axis). Therefore, each space is again divided into eight smaller spaces. The divided smaller spaces are also expressed as cubes with six faces. This division method is applied until the leaf nodes of the octree become voxels.

[0098] The bottom of Fig. 4 shows the occupancy code of the octree. The occupancy code of the octree is generated to indicate whether each of the eight partitioned spaces generated by partitioning one space contains at least one point. Therefore, one occupancy code is expressed by eight child nodes. Each child node represents the occupancy of the partitioned space, and each child node has a value of 1 bit. Therefore, the occupancy code is expressed 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 illustrated in Fig. 4 is 00100001, it indicates that the spaces corresponding to the third and eighth child nodes among the eight child nodes each contain at least one point. As shown in the drawing, the third child node and the eighth child node each have eight child nodes, and each child node is expressed by an 8-bit occupancy code. The drawing shows that the occupancy code of the third child node is 10000111, and the occupancy code of the eighth child node is 01001111. A point cloud encoder according to embodiments (e.g., an arithmetic encoder (30004)) can entropy encode the occupancy code. In addition, the point cloud encoder can intra / inter code the occupancy code to increase compression efficiency. A receiving device according to embodiments (e.g., a receiving device (10004) or a point cloud video decoder (10006)) reconstructs an octree based on the occupancy code.

[0099] A point cloud encoder according to 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 points. However, points within a 3D space are not always evenly distributed, and thus, there may be specific areas where there are not many points. Therefore, performing voxelization on the entire 3D space is inefficient. For example, if there are few points in a specific area, there is no need to perform voxelization up to that area.

[0100] Therefore, the point cloud encoder according to the embodiments can perform direct coding that directly codes the positions of points included in the specific region (or nodes excluding leaf nodes of the octree) without performing voxelization for the specific region described above. The coordinates of the direct coded points according to the embodiments are referred to as a direct coding mode (DCM). In addition, the point cloud encoder according to the embodiments can perform trisoup geometry encoding that reconstructs the positions of points within the specific region (or node) on a voxel basis based on a surface model. Trisoup geometry encoding is a geometry encoding that expresses the representation of an object as a series of triangle meshes. Therefore, the point cloud decoder can generate a point cloud from the mesh surface. Direct coding and trisoup geometry encoding according to the embodiments can be selectively performed. Additionally, direct coding and tri-subtractive geometry encoding according to embodiments may be performed in combination with octree geometry coding (or octree coding).

[0101] In order to perform direct coding, the option to use direct mode for applying direct coding must be activated, the node to which direct coding is to be applied must not be a leaf node, and there must be points below a threshold within a specific node. 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 arithmetic encoder (30004)) according to the embodiments can entropy code the positions (or position values) of the points.

[0102] A point cloud encoder according to embodiments (e.g., surface approximation analysis unit (30003)) can determine a specific level of an octree (when the level is smaller than the depth d of the octree) and, starting from that level, perform tri-subject geometry encoding to reconstruct the positions of points within a node region on a voxel basis using a surface model (tri-subject mode). A point cloud encoder according to embodiments can specify a level to which tri-subject geometry encoding is to be applied. For example, when the specified level is equal to the depth of the octree, the point cloud encoder does not operate in tri-subject mode. That is, a point cloud encoder according to embodiments can operate in tri-subject mode only when the specified level is smaller than the depth value of the octree. A three-dimensional cubic area of ​​nodes at a specified level according to embodiments is called a block. One block may include one or more voxels. A block or a voxel may correspond to a brick. Within each block, geometry is represented by a surface. According to embodiments, a surface may intersect each edge of the block at most once.

[0103] Since one block has 12 edges, there are at least 12 intersections within one block. Each intersection is called a vertex. A vertex existing along an edge is detected if there is at least one occupied voxel adjacent to the edge among all blocks sharing the edge. An occupied voxel according to 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 the edge among all blocks sharing the edge.

[0104] When a vertex is detected, the point cloud encoder according to the embodiments can entropy code the starting point (x, y, z) of the edge, the direction vector (Δx, Δy, Δz) of the edge, and the vertex position value (relative position value within the edge). When the tri-substructure geometry encoding is applied, the point cloud encoder according to the embodiments (e.g., the geometry reconstruction unit (30005)) can perform triangle reconstruction, up-sampling, and voxelization processes to generate restored geometry (reconstructed geometry).

[0105] The vertices located at the edge of a block determine the surface passing through the block. According to the embodiments, the surface is a non-planar polygon. The triangle reconstruction process reconstructs the surface represented by a triangle based on the starting point of the edge, the direction vector of the edge, and the position value of the vertex. The triangle reconstruction process is as follows. ① Calculate the centroid value of each vertex, ② Subtract the centroid value from each vertex value, and ③ Square the values, and then add up all the values ​​to obtain the value.

[0106]

[0107] The minimum of the added values ​​is found, and the projection process is performed according to the axis with the minimum value. For example, if the x element is minimum, each vertex is projected to the x-axis based on the center of the block, and then projected onto the (y, z) plane. If the value output when projected onto the (y, z) plane is (ai, bi), the θ value is found through atan2(bi, ai), and the vertices are sorted based on the θ value. The table below shows the combination of vertices to create a triangle depending on the number of vertices. The vertices are sorted in order from 1 to n. The table below shows that two triangles can be formed depending on the combination of vertices for four vertices. The first triangle can be formed by the 1st, 2nd, and 3rd vertices among the sorted vertices, and the second triangle can be formed by the 3rd, 4th, and 1st vertices among the sorted vertices.

[0108] Table 2-1. Triangles formed from vertices ordered 1,… ,n

[0109] n triangles

[0110] 3 (1,2,3)

[0111] 4 (1,2,3), (3,4,1)

[0112] 5 (1,2,3), (3,4,5), (5,1,3)

[0113] 6 (1,2,3), (3,4,5), (5,6,1), (1,3,5)

[0114] 7 (1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)

[0115] 8 (1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1)

[0116] 9 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7,9,3)

[0117] 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)

[0118] 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)

[0119] 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)

[0120] The upsampling process is performed to voxelize the triangle by adding points in the middle along the edges. Additional points are generated based on the upsampling factor and the width of the block. The additional points are called refined vertices. A point cloud encoder according to embodiments can voxelize the refined vertices. The point cloud encoder can also perform attribute encoding based on the voxelized positions (or position values).

[0121] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0122] 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 direct-coded points (e.g., placing the direct-coded points at the front of the point cloud data). When trysoup geometry encoding is applied, the geometry reconstruction process includes triangle reconstruction, upsampling, and voxelization. Since attributes depend on the geometry, attribute encoding is performed based on the reconstructed geometry.

[0123] A point cloud encoder (e.g., LOD generation unit (30009)) can reorganize points by LOD. The drawing shows point cloud content corresponding to LOD. The left side of the drawing shows the original point cloud content. The second figure from the left in the drawing shows the distribution of points of the lowest LOD, and the rightmost figure in the drawing shows the distribution of points of the highest LOD. That is, points of the lowest LOD are sparsely distributed, and points of the highest LOD are densely distributed. That is, as LOD increases in the direction of the arrow indicated at the bottom of the drawing, the interval (or distance) between points becomes shorter.

[0124] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0125] 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 a LOD generation unit (30009)) can generate a 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 not only in the point cloud encoder but also in the point cloud decoder.

[0126] The upper part of Fig. 6 shows examples of points (P0 to P9) of point cloud content distributed in 3D space. The original order in Fig. 6 represents the order of points P0 to P9 before LOD generation. The LOD-based order in Fig. 6 represents the order of points according to LOD generation. The points are rearranged by LOD. Additionally, a higher LOD includes points belonging to a lower LOD. As shown in Fig. 6, LOD0 includes P0, P5, P4, and P2. LOD1 includes points of LOD0 and P1, P6, and P3. LOD2 includes points of LOD0, points of LOD1, and P9, P8, and P7.

[0127] As described in FIG. 3, the point cloud encoder according to the embodiments can selectively or in combination perform predictive transform coding, lifting transform coding, and RAHT transform coding.

[0128] A point cloud encoder according to embodiments can perform predictive transformation coding to generate a predictor for points and set a predicted attribute (or predicted attribute value) for each point. That is, N predictors can be generated for N points. The predictor according to embodiments can calculate a weight (= 1 / distance) value based on the LOD value of each point, indexing information for neighboring points existing within a distance set for each LOD, and distance values ​​to the neighboring points.

[0129] According to the embodiments, the predicted attribute (or attribute value) is set as the average value of the product of the attributes (or attribute values, for example, color, reflectance, etc.) of neighboring points set in the predictor of each point and the weight (or weight value) calculated based on the distance to each neighboring point. The point cloud encoder according to the embodiments (for example, the 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.

[0130] graph. Attribute prediction residuals quantization pseudo code

[0131] int PCCQuantization(int value, int quantStep) {

[0132] if( value >=0) {

[0133] return floor(value / quantStep + 1.0 / 3.0);

[0134] } else {

[0135] return -floor(-value / quantStep + 1.0 / 3.0);

[0136] }

[0137] }

[0138] graph. Attribute prediction residuals inverse quantization pseudo code

[0139] int PCCInverseQuantization(int value, int quantStep) {

[0140] if( quantStep ==0) {

[0141] return value;

[0142] } else {

[0143] return value * quantStep;

[0144] }

[0145] }

[0146] A point cloud encoder according to embodiments (e.g., an arithmetic encoder (30012)) can entropy code the quantized and dequantized residuals as described above when there are neighboring points to the predictor of each point. A point cloud encoder according to embodiments (e.g., an arithmetic encoder (30012)) can entropy code the attributes of the point without performing the above-described process when there are no neighboring points to the predictor of each point.

[0147] A point cloud encoder according to embodiments (e.g., 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 according to the distance to the neighboring points. Lifting transformation coding according to embodiments is similar to the above-described predictive transformation coding, but differs in that weights are cumulatively applied to attribute values. The process of cumulatively applying weights to attribute values ​​according to embodiments is as follows.

[0148] 1) Create an array QW (QuantizationWieght) that stores the weight values ​​of each point. The initial value of all elements in QW is 1.0. Add the value obtained by multiplying the weight of the current point's predictor by the QW value of the predictor index of the neighboring node registered in the predictor.

[0149] 2) Lift prediction process: To calculate the predicted attribute value, the weighted value of the point's attribute value is multiplied and subtracted from the existing attribute value.

[0150] 3) Create temporary arrays called updateweight and update and initialize them to 0.

[0151] 4) For each predictor, the calculated weights are multiplied by the weights stored in the QW corresponding to the predictor index, and the resulting weights are cumulatively added to the update weight array as the index of the neighboring node. The update array accumulates the values ​​obtained by multiplying the calculated weights by the attribute values ​​of the indexes of the neighboring nodes.

[0152] 5) Lift update process: For each predictor, the attribute values ​​in the update array are divided by the weight values ​​in the update weight array of the predictor index, and the existing attribute values ​​are added to the divided value.

[0153] 6) For all predictors, the predicted attribute values ​​are calculated by additionally multiplying the updated attribute values ​​through the lift update process by the weights (stored in QW) updated through the lift prediction process. The point cloud encoder according to the embodiments (e.g., coefficient quantization unit (30011)) quantizes the predicted attribute values. In addition, the point cloud encoder (e.g., arithmetic encoder (30012)) entropy-codes the quantized attribute values.

[0154] A point cloud encoder according to embodiments (e.g., RAHT transform unit (30008)) can perform RAHT transform coding that predicts attributes of upper-level nodes using attributes associated with nodes at lower levels of an octree. RAHT transform coding is an example of attribute intra coding through octree backward scan. A point cloud encoder according to embodiments scans from a voxel to the entire area, and repeats the merging process up to a root node while merging voxels into larger blocks at each step. The merging process according to embodiments is performed only for occupied nodes. The merging process is not performed for empty nodes, and the merging process is performed for the node immediately above the empty node.

[0155] The following equation represents the RAHT transformation matrix. g l x, y, z represents the average attribute value of 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.

[0156]

[0157] gl-1 x, y, z are low-pass values, which are 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 arithmetic encoder (400012)). The weights are computed 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:

[0158]

[0159] The gDC values ​​are also quantized and entropy coded, like the high-pass coefficients.

[0160] Fig. 7 illustrates an example of a point cloud decoder according to embodiments.

[0161] 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 FIGS. 1 to 6.

[0162] As described in Figure 1, the point cloud decoder can perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

[0163] A point cloud decoder according to embodiments includes an arithmetic decoder (7000), an octree synthesizer (7001), a surface approximation synthesizer (7002), a geometry reconstructor (7003), an inverse transform coordinates (7004), an arithmetic decoder (7005), an inverse quantize (7006), a RAHT transform (7007), a LOD generator (7008), an inverse lifting (7009), and / or an inverse transform colors (7010).

[0164] The arithmetic decoder (7000), the octree synthesis unit (7001), the surface oproximation synthesis unit (7002), the geometry reconstruction unit (7003), and the coordinate system inversion unit (7004) can perform geometry decoding. Geometry decoding according to embodiments can include direct coding and trisoup geometry decoding. Direct coding and trisoup geometry decoding are applied selectively. In addition, geometry decoding is not limited to the above examples, and is performed by the reverse process of the geometry encoding described in FIGS. 1 to 6.

[0165] An arithmetic decoder (7000) according to embodiments decodes a received geometry bitstream based on arithmetic coding. The operation of the arithmetic decoder (7000) corresponds to the reverse process of the arithmetic encoder (30004).

[0166] 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 about the geometry obtained as a result of decoding). A specific description of the occupancy code is as described in FIGS. 1 to 6.

[0167] The surface off-axis synthesis unit (7002) according to the embodiments can synthesize a surface based on the decoded geometry and / or the generated octree when the tri-sub geometry encoding is applied.

[0168] The geometry reconstruction unit (7003) according to the embodiments can regenerate geometry based on the surface and / or decoded geometry. As described in FIGS. 1 to 6, direct coding and try-soup geometry encoding are selectively applied. Therefore, the geometry reconstruction unit (7003) directly retrieves and adds position information of points to which direct coding is applied. In addition, when try-soup geometry encoding is applied, the geometry reconstruction unit (7003) can restore geometry by performing a reconstruction operation of the geometry reconstruction unit (30005), such as triangle reconstruction, up-sampling, and voxelization operations. The specific details are the same as described in FIG. 4 and are therefore omitted. The restored geometry may include a point cloud picture or frame that does not include attributes.

[0169] The coordinate system inverse transformation unit (7004) according to the embodiments can obtain the positions of points by transforming the coordinate system based on the restored geometry.

[0170] The arithmetic decoder (7005), the inverse quantization unit (7006), the RAHT transform unit (7007), the LOD generation unit (7008), the inverse lifting unit (7009), and / or the color inverse transform unit (7010) can perform the attribute decoding described in FIG. 10. The attribute decoding according to embodiments can include RAHT (Region Adaptive Hierarchial Transform) decoding, Interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform) decoding, and lifting transform (interpolation-based hierarchical nearest-neighbor prediction with an update / lifting step (Lifting Transform)) decoding. The three decodings described above can be used selectively, or a combination of one or more decodings can be used. Additionally, attribute decoding according to embodiments is not limited to the examples described above.

[0171] An arithmetic decoder (7005) according to embodiments decodes an attribute bitstream using arithmetic coding.

[0172] The inverse quantization unit (7006) according to the embodiments inverse quantizes information about the decoded attribute bitstream or the attributes obtained as a result of the decoding and outputs the inverse quantized attributes (or attribute values). The inverse quantization may be selectively applied based on the attribute encoding of the point cloud encoder.

[0173] According to embodiments, the RAHT transform unit (7007), the LOD generator (7008), and / or the inverse lifting unit (7009) may process the reconstructed geometry and the inverse quantized attributes. As described above, the RAHT transform unit (7007), the LOD generator (7008), and / or the inverse lifting unit (7009) may selectively perform a corresponding decoding operation according to the encoding of the point cloud encoder.

[0174] The color inverse transform unit (7010) according to the embodiments performs inverse transform coding to inversely transform the color values ​​(or textures) included in the decoded attributes. The operation of the color inverse transform unit (7010) may be selectively performed based on the operation of the color transform unit (30006) of the point cloud encoder.

[0175] The elements of the point cloud decoder of FIG. 7 may be implemented by hardware, software, firmware, or a combination thereof, including one or more processors or integrated circuits configured to communicate with one or more memories included in a point cloud providing device, although not shown in the drawing. The one or more processors may perform at least one or more of the operations and / or functions of the elements of the point cloud decoder of FIG. 7 described above. Furthermore, the 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.

[0176] Figure 8 is an example of a transmission device according to embodiments.

[0177] 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 or more of the same or similar operations and encoding methods as the operations and encoding methods of the point cloud encoder described in FIGS. 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 a property conversion processing unit) (8009), a prediction / lifting / RAHT conversion processing unit (8010), an arithmetic coder (8011), and / or a transmission processing unit (8012).

[0178] The data input unit (8000) according to the embodiments receives or acquires point cloud data. The data input unit (8000) may perform operations and / or acquisition methods identical or similar to those of the point cloud video acquisition unit (10001) (or the acquisition process (20000) described in FIG. 2).

[0179] 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 the same or similar to the geometry encoding described in FIGS. 1 to 6, a detailed description thereof will be omitted.

[0180] The quantization processing unit (8001) according to the embodiments quantizes geometry (e.g., position values ​​of points or position values). The operation and / or quantization of the quantization processing unit (8001) is identical to 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 FIGS. 1 to 6.

[0181] The voxelization processing unit (8002) according to the embodiments voxels the position values ​​of quantized points. The voxelization processing unit (80002) may perform operations and / or processes identical or similar to the operations and / or voxelization processes of the quantization unit (30001) described in FIG. 3. Specific descriptions are identical to those described in FIGS. 1 to 6.

[0182] The octree occupancy code generation unit (8003) according to the embodiments performs octree coding on the positions of voxelized points based on the 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 those 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 to 6.

[0183] The surface model processing unit (8004) according to the embodiments can perform tri-subject geometry encoding to reconstruct the positions of points within a specific area (or node) on a voxel basis based on the surface model. The surface model processing unit (8004) can perform operations and / or methods identical or similar to those 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 with reference to FIGS. 1 to 6.

[0184] The intra / inter coding processing unit (8005) according to embodiments can intra / inter code point cloud data. The intra / inter coding processing unit (8005) can perform coding identical to or similar to the intra / inter coding described in FIG. 7. The specific description is identical to that described in FIG. 7. According to embodiments, the intra / inter coding processing unit (8005) can be included in an arithmetic coder (8006).

[0185] An arithmetic coder (8006) according to embodiments entropy encodes an octree and / or an approximated octree of point cloud data. For example, the encoding method includes an arithmetic encoding method. The arithmetic coder (8006) performs operations and / or methods identical or similar to those of the arithmetic encoder (30004).

[0186] The metadata processing unit (8007) according to the embodiments processes metadata regarding point cloud data, such as setting values, and provides the metadata to a necessary processing step, such as geometry encoding and / or attribute encoding. In addition, the 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 and processed separately from geometry encoding and / or attribute encoding. In addition, the signaling information according to the embodiments may be interleaved.

[0187] The color conversion processing unit (8008), the attribute conversion processing unit (8009), the prediction / lifting / RAHT conversion processing unit (8010), and the arithmetic coder (8011) perform attribute encoding. Since the attribute encoding according to the embodiments is the same as or similar to the attribute encoding described in FIGS. 1 to 6, a detailed description thereof will be omitted.

[0188] The color conversion processing unit (8008) according to the embodiments performs color conversion coding to convert the color values ​​included in the attributes. The color conversion processing unit (8008) can perform color conversion coding based on the reconstructed geometry. The description of the reconstructed geometry is the same as that described with reference to FIGS. 1 to 6. In addition, the color conversion processing unit (8008) performs the same or similar operation and / or method as that of the color conversion unit (30006) described with reference to FIG. 3. A detailed description thereof will be omitted.

[0189] The attribute transformation processing unit (8009) according to embodiments performs attribute transformation to transform attributes based on positions for which geometry encoding has not been performed and / or reconstructed geometry. The attribute transformation processing unit (8009) performs operations and / or methods that are the same as or similar to those of the attribute transformation unit (30007) described in FIG. 3. A detailed description thereof will be omitted. The prediction / lifting / RAHT transformation processing unit (8010) according to embodiments can code transformed attributes by using any one or a combination of RAHT coding, prediction transformation coding, and lifting transformation coding. The prediction / lifting / RAHT transformation processing unit (8010) performs at least one or more of operations that are the same as or similar to those of the RAHT transformation unit (30008), LOD generation unit (30009), and lifting transformation unit (30010) described in FIG. 3. In addition, the description of the prediction transformation coding, lifting transformation coding, and RAHT transformation coding is the same as that described in FIGS. 1 to 6, so a detailed description is omitted.

[0190] An arithmetic coder (8011) according to embodiments can encode coded attributes based on arithmetic coding. The arithmetic coder (8011) performs operations and / or methods identical or similar to those of the arithmetic encoder (300012).

[0191] The transmission processing unit (8012) according to embodiments may transmit each bitstream including encoded geometry and / or encoded attribute, metadata information, or may transmit the encoded geometry and / or encoded attribute, and metadata information as one bitstream. When the encoded geometry and / or encoded attribute, and metadata information according to embodiments are configured as one bitstream, the bitstream may include one or more sub-bitstreams. The bitstream according to embodiments may include signaling information including a Sequence Parameter Set (SPS) for sequence-level signaling, a Geometry Parameter Set (GPS) for signaling geometry information coding, an Attribute Parameter Set (APS) for signaling attribute information coding, and a Tile Parameter Set (TPS) for tile-level signaling, and slice data. The slice data may include information about one or more slices. One slice according to embodiments may include one geometry bitstream (Geom0). 0 ) and one or more attribute bitstreams (Attr0 0 , Attr1 0 ) may be included.

[0192] A slice is a series of syntax elements that represent all or part of a coded point cloud frame.

[0193] A TPS according to embodiments may include information about each tile (e.g., coordinate value information of a bounding box and height / size information, etc.) for one or more tiles. A geometry bitstream may include a header and a payload. The header of a geometry bitstream according to embodiments may include identification information of a parameter set included in GPS (geom_ parameter_set_id), a tile identifier (geom_tile_id), a slice identifier (geom_slice_id), and information about data included in the payload. As described above, a metadata processing unit (8007) according to embodiments may generate and / or process signaling information and transmit it to a transmission processing unit (8012). According to embodiments, elements that perform geometry encoding and elements that perform attribute encoding may share data / information with each other as indicated by a dotted line. The transmission processing unit (8012) according to the embodiments may perform operations and / or transmission methods identical or similar to those of the transmitter (10003). A detailed description thereof is omitted as it is the same as that described in FIGS. 1 and 2.

[0194] Fig. 9 is an example of a receiving device according to embodiments.

[0195] The receiving device illustrated in FIG. 9 is an example of the receiving device (10004) of FIG. 1 (or the point cloud decoder of FIGS. 10 and 11). The receiving device illustrated in FIG. 9 can perform at least one or more of the same or similar operations and decoding methods as the operations and decoding methods of the point cloud decoder described in FIGS. 1 to 11.

[0196] A receiving device according to 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 transform processing unit (9009), a color inverse transform processing unit (9010), and / or a renderer (9011). Each component of the decoding according to embodiments may perform the reverse process of the component of the encoding according to embodiments.

[0197] The receiving unit (9000) according to the embodiments receives point cloud data. The receiving unit (9000) may perform operations and / or receiving methods identical or similar to those of the receiver (10005) of FIG. 1. A detailed description thereof will be omitted.

[0198] The 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) can be included in the receiving unit (9000).

[0199] The arithmetic 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 the same or similar to the geometry decoding described in FIGS. 1 to 10, a detailed description thereof will be omitted.

[0200] An arithmetic decoder (9002) according to embodiments can decode a geometry bitstream based on arithmetic coding. The arithmetic decoder (9002) performs operations and / or coding identical to or similar to those of the arithmetic decoder (7000).

[0201] The occupancy code-based octree reconstruction processing unit (9003) according to embodiments can reconstruct an octree by obtaining an occupancy code from a decoded geometry bitstream (or information about the 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 those of the octree synthesis unit (7001) and / or the octree generation method. The surface model processing unit (9004) according to embodiments can perform tri-sub geometry decoding and related geometry reconstructing (e.g., triangle reconstruction, up-sampling, voxelization) based on the surface model method when tri-sub geometry encoding is applied. The surface model processing unit (9004) performs the same or similar operations as those of the surface off-ratio synthesis unit (7002) and / or the geometry reconstructing unit (7003).

[0202] The inverse quantization processing unit (9005) according to the embodiments can inverse quantize the decoded geometry.

[0203] The metadata parser (9006) according to the embodiments can parse metadata, such as setting values, contained in the received point cloud data. The metadata parser (9006) can pass the metadata to geometry decoding and / or attribute decoding. A detailed description of the metadata is omitted as it is the same as described in FIG. 8.

[0204] The arithmetic decoder (9007), the inverse quantization processing unit (9008), the prediction / lifting / RAHT inverse transform processing unit (9009), and the color inverse transform processing unit (9010) perform attribute decoding. Since attribute decoding is the same or similar to the attribute decoding described in FIGS. 1 to 10, a detailed description thereof will be omitted.

[0205] An arithmetic decoder (9007) according to embodiments can decode an attribute bitstream using arithmetic coding. The arithmetic decoder (9007) can decode the attribute bitstream based on the reconstructed geometry. The arithmetic decoder (9007) performs operations and / or coding identical or similar to those of the arithmetic decoder (7005).

[0206] The inverse quantization processing unit (9008) according to the embodiments can inverse quantize the decoded attribute bitstream. The inverse quantization processing unit (9008) performs operations and / or methods identical or similar to the operations and / or inverse quantization methods of the inverse quantization unit (7006).

[0207] The prediction / lifting / RAHT inverse transform processing unit (9009) according to embodiments can process reconstructed geometry and inverse quantized attributes. The prediction / lifting / RAHT inverse transform processing unit (9009) performs at least one or more of operations and / or decodings that are identical or similar to the operations and / or decodings of the RAHT transform unit (7007), the LOD generation unit (7008), and / or the inverse lifting unit (7009). The color inverse transform processing unit (9010) according to embodiments performs inverse transform coding for inverse transforming the color value (or texture) included in the decoded attributes. The color inverse transform processing unit (9010) performs operations and / or inverse transform coding that are identical or similar to the operations and / or inverse transform coding of the color inverse transform unit (7010). A renderer (9011) according to embodiments can render point cloud data.

[0208] Fig. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0209] 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. In addition, the XR device (1030) may correspond to or be linked with a point cloud data (PCC) device according to embodiments.

[0210] A cloud network (1000) may refer to a network that constitutes part of a 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.

[0211] 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) through a cloud network (1000), and can assist in at least part of the processing of the connected devices (1010 to 1070).

[0212] The HMD (Head-Mount Display) (1070) represents one of the types in which the XR device and / or the PCC device according to the embodiments can be implemented. The HMD type device according to the embodiments includes a communication unit, a control unit, a memory unit, an I / O unit, a sensor unit, and a power supply unit.

[0213] Below, various embodiments of devices (1010 to 1050) to which the above-described technology is applied are described. Here, the devices (1010 to 1050) illustrated in FIG. 10 can be linked / combined with point cloud data transmission / reception devices according to the above-described embodiments.

[0214] <PCC+XR>

[0215] The XR / PCC device (1030) may be implemented as a HMD (Head-Mount Display), a HUD (Head-Up Display) equipped in a vehicle, a television, a mobile phone, a smart phone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a fixed robot, a mobile robot, etc., by applying PCC and / or XR (AR+VR) technology.

[0216] The XR / PCC device (1030) can obtain information about surrounding space or real objects by analyzing 3D point cloud data or image data acquired through various sensors or from external devices to generate location 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 including additional information about a recognized object in correspondence with the recognized object.

[0217] <PCC+XR+모바일폰>

[0218] The XR / PCC device (1030) can be implemented as a mobile phone (1040) or the like by applying PCC technology.

[0219] The mobile phone (1040) can decode and display point cloud content based on PCC technology.

[0220] <PCC+자율주행+XR>

[0221] Autonomous vehicles (1020) can be implemented as mobile robots, vehicles, unmanned aerial vehicles, etc. by applying PCC technology and XR technology.

[0222] An autonomous vehicle (1020) to which XR / PCC technology is applied may refer to an autonomous vehicle equipped with a means for providing XR images, or an autonomous vehicle that is the subject of control / interaction within an XR image. In particular, an autonomous vehicle (1020) that is the subject of control / interaction within an XR image is distinct from an XR device (1030) and can be linked with each other.

[0223] An autonomous vehicle (1020) equipped with a means for providing XR / PCC images can obtain sensor information from sensors including cameras and output XR / PCC images generated based on the obtained sensor information. For example, the autonomous vehicle (1020) can be equipped with a HUD to output XR / PCC images, thereby providing passengers with XR / PCC objects corresponding to real objects or objects on a screen.

[0224] At this time, when the XR / PCC object is output to the HUD, at least a part of the XR / PCC object may be output so as to overlap with an actual object toward which the passenger's gaze is directed. On the other hand, when the XR / PCC object is output to a display provided inside the autonomous vehicle, at least a part of the XR / PCC object may be output so as to overlap with an object on the screen. For example, the autonomous vehicle (1220) may output XR / PCC objects corresponding to objects such as a lane, another vehicle, a traffic light, a traffic sign, a two-wheeled vehicle, a pedestrian, a building, etc.

[0225] 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.

[0226] In other words, VR technology is a display technology that provides only CG images of objects or backgrounds in the real world. On the other hand, AR technology refers to a technology that shows a virtually created CG image on top of an image of an actual object. Furthermore, MR technology is similar to the aforementioned AR technology in that it mixes and combines virtual objects in the real world. However, in AR technology, the distinction between real objects and virtual objects created with CG images is clear, and virtual objects are used in a form that complements real objects, whereas in MR technology, virtual objects are considered to have the same characteristics as real objects. A more specific example is the hologram service, which is an application of the aforementioned MR technology.

[0227] However, recently, rather than clearly distinguishing between VR, AR, and MR technologies, they are often referred to as XR (extended reality) technologies. Therefore, embodiments of the present invention are applicable to all VR, AR, MR, and XR technologies. These technologies can be applied to encoding / decoding based on PCC, V-PCC, and G-PCC technologies.

[0228] The PCC method / device according to the embodiments can be applied to a vehicle providing an autonomous driving service.

[0229] Vehicles providing autonomous driving services are connected to PCC devices to enable wired / wireless communication.

[0230] A point cloud data (PCC) transmission and reception device according to embodiments, when connected to a vehicle to enable wired / wireless communication, can receive / process content data related to AR / VR / PCC services that can be provided together with autonomous driving services and transmit the same to the vehicle. In addition, when the point cloud data transmission and reception device is mounted on a vehicle, the point cloud transmission and reception device can receive / process content data related to AR / VR / PCC services and provide the same to a user according to a user input signal input through a user interface device. A vehicle or a user interface device according to embodiments can receive a user input signal. The user input signal according to embodiments can include a signal instructing an autonomous driving service.

[0231] The encoding method / device according to the embodiments is interpreted as a term referring to the transmitting device (10000) of FIG. 1, the point cloud video encoder (10002), the transmitter (10003), the acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, the encoder of FIG. 3, the transmitting device of FIG. 8, the device of FIG. 10, the encoder of FIG. 11, FIG. 23, the encoding of FIG. 24, the bitstream of FIG. 26 and the parameter generation of FIGs. 27 to 30, the encoder of FIGs. 30-31, the encoding method of FIG. 32, etc.

[0232] The decoding method / device according to the embodiments is interpreted as a term referring to the 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, decoder of FIG. 11, FIG. 23, decoding of FIG. 25, bitstream of FIG. 26 and parameter parsing of FIG. 27 to FIG. 30, decoder of FIG. 30-31, decoding method of FIG. 32, etc.

[0233] In addition, the point cloud data transmission / reception method / device (encoding method and device) according to the embodiments may be abbreviated as the method / device according to the embodiments.

[0234] According to embodiments, geometry data, geometry information, location information, etc., which constitute point cloud data, are interpreted as having the same meaning. Attribute data, attribute information, property information, etc., which constitute point cloud data, are interpreted as having the same meaning.

[0235] Methods and devices according to embodiments include methods and devices for encoding and decoding geometry data based on planar context continuation of geometry layer-group slicing.

[0236] The embodiments include a method for efficiently supporting selective decoding of a portion of data when transmitting and receiving point cloud data due to receiver performance or transmission speed. The embodiments propose a method for selecting necessary information or removing unnecessary information from a bitstream unit by dividing geometry and attribute data, which are conventionally transmitted as data units, into semantic units such as geometry octree and LoD (Level of Detail).

[0237] Embodiments include operations for constructing a data structure composed of a point cloud. Specifically, a packing and signaling method for effectively transmitting PCC data constructed based on layers is described, and an operation for applying the same to a scalable PCC-based service is included. In particular, a method for constructing and transmitting / receiving slice segments to be more suitable for a scalable PCC service when a direct compression mode is used for position compression is included. In particular, a compression structure for efficient storage and transmission of large-capacity point cloud data with a wide distribution and a high point density is included.

[0238] Referring to FIGS. 3 and 7, referring to point cloud data transmission and reception devices (or encoders / decoders for short) according to embodiments, point cloud data is composed of the location (geometry: e.g., XYZ coordinates) and attributes (attributes: e.g., color, reflectance, intensity, grayscale, opacity, etc.) of each data. In point cloud compression (Point Cloud Compression: PCC), octree-based compression is performed to efficiently compress distribution characteristics that are unevenly distributed in a three-dimensional space, and attribute information is compressed based on this. The PCC transmission and reception devices illustrated in FIGS. 3 and 7 can process operation(s) according to embodiments through each component device.

[0239] Figure 11 illustrates the encoding, transmission, and decoding processes of point cloud data according to embodiments.

[0240] A point cloud encoder (15000) is a transmission device according to embodiments that performs a transmission method according to embodiments, and can scalably encode and transmit point cloud data.

[0241] A point cloud decoder (15010) is a receiving device according to embodiments that performs a receiving method according to embodiments and can scalably decode point cloud data.

[0242] The source data received by the encoder (15000) may include geometry data and / or attribute data.

[0243] The encoder (15000) does not scalably encode point cloud data to directly generate a partial PCC bitstream, but receives full geometry data and full attribute data, stores the data in storage connected to the encoder, and then transcodes the data for partial encoding to generate and transmit a partial PCC bitstream. The decoder (15010) receives and decodes the partial PCC bitstream to restore the partial geometry and / or partial attributes.

[0244] An encoder (15000) can receive full geometry and full attributes, store the data in storage connected to the encoder, and transcode the point cloud data with low QP (quantization parameter) to generate and transmit a full PCC bitstream. A decoder (15010) can receive and decode the full PCC bitstream to restore the full geometry and / or full attributes. The decoder (15010) can select partial geometry and / or partial attributes from the full PCC bitstream through data selection.

[0245] The method / device according to the embodiments divides the location information and feature information such as color / brightness / reflectivity of data points, which are point cloud data, into geometry and attribute information, and compresses and transmits them respectively. At this time, the PCC data can be configured according to an octree structure with layers or LoD (Level of Detail) depending on the level of detail. Based on this, scalable point cloud data coding and representation are possible. At this time, it is possible to decode or represent only a portion of the point cloud data depending on the performance or transmission speed of the receiver.

[0246] The method / device according to the embodiments can remove unnecessary data in advance during this process. That is, when only a part of a scalable PCC bitstream needs to be transmitted (for example, when only some layers are decoded during scalable decoding), since only the necessary part cannot be selected and transmitted, 1) the necessary part must be re-encoded after decoding (15020) or 2) the entirety must be transmitted and then selectively applied at the receiving end (15030). However, in case 1), a delay may occur due to the time required for decoding and re-encoding (15020), and in case 2), bandwidth efficiency may decrease because unnecessary data is transmitted, and when a fixed bandwidth is used, data quality may have to be lowered during transmission (15030).

[0247] At this time, in the case of octree-based positional compression, entropy-based compression method and direct coding can be used together, in which case slice configuration is required to efficiently utilize scalability.

[0248] Additionally, for large point clouds with wide distribution and high point density, delay issues may occur due to the large number of bitstreams that must be processed to access the region of interest.

[0249] Accordingly, the method / device according to the embodiments can solve such technical problems through the following technical means: 1) a method for continuity between parent-child subgroups when applying layer-group slicing (hereinafter, FGS (Fine granularity slice)) to geometry data, 2) a method for directly transferring an occupancy map from an encoder to a decoder (2-1) or inferring an occupancy map based on a parent subgroup node position (2-2), etc.

[0250] A unit according to embodiments may be referred to as LOD, layer, slice, etc. LOD is a term similar to LOD of attribute data coding, but in another sense, it can mean a data unit for a layer structure of a bitstream. It can be a concept that corresponds to one depth or combines two or more depths based on the depth (level) of a hierarchical structure of point cloud data, for example, an octree or multiple trees. Similarly, a layer is a concept that corresponds to one depth or combines two or more depths as a unit for generating a sub-bitstream, and can correspond to one LOD or correspond to two or more LODs. In addition, a slice is a unit for configuring a unit of a sub-bitstream, and can correspond to one depth, a part of one depth, or correspond to two or more depths. In addition, it can correspond to one LOD, a part of one LOD, or correspond to two or more LODs. According to embodiments, LODs, layers, and slices may correspond to or be inclusive of each other. In addition, units according to embodiments may include LODs, layers, slices, layer groups, subgroups, etc., and may be referred to as complementary to each other. Segmented slices may be referred to as FGS fine-granularity slices.

[0251] Figure 12 illustrates a layer-based point cloud data configuration according to embodiments.

[0252] The transmission method / device according to the embodiments can encode and decode point cloud data by configuring layer-based point cloud data as shown in FIG. 12.

[0253] Layering of point cloud data can have a layer structure in various perspectives such as SNR, spatial resolution, color, temporal frequency, bitdepth, etc. depending on the application field, and layers can be formed in the direction of increasing data density based on an octree structure or LoD structure.

[0254] Figure 13 illustrates a geometry and attribute bitstream structure according to embodiments.

[0255] The method / device according to the embodiments can construct, encode, and decode a geometry bitstream and an attribute bitstream as in FIG. 13 based on layering as in FIG. 12.

[0256] A bitstream obtained through point cloud compression of a transmitting device / encoder according to embodiments can be divided into a geometry data bitstream and an attribute data bitstream and transmitted according to the type of data.

[0257] Each bitstream according to the embodiments may be transmitted as a slice. Regardless of layer information or LoD information, a geometry data bitstream and an attribute data bitstream may each be transmitted as a slice. In this case, if only a part of a layer or LoD is to be used, the following steps must be taken: 1) a process of decoding the bitstream, 2) a process of selecting only the part to be used and removing unnecessary parts, and 3) a process of re-encoding based only on the necessary information.

[0258] Figure 14 shows a bitstream configuration according to embodiments and a bitstream alignment method according to embodiments.

[0259] The transmission method / device according to the embodiments can generate a bitstream as in FIG. 14, and the reception method / device according to the embodiments can decode point cloud data included in the bitstream as in FIG. 14.

[0260] Bitstream composition according to embodiments

[0261] Embodiments may apply a method of dividing the bitstream into layers (or LoDs) and transmitting them to avoid unnecessary intermediate processes.

[0262] For example, considering the LoD-based PCC technology, it has a structure where lower LoDs are included in higher LoDs. Information included in the current LoD but not in the previous LoD, i.e., newly included information for each LoD, can be referred to as R (Residual, Rest). As shown in Figure 13 and below, the initial LoD information and the newly included information R in each LoD can be divided into independent units and transmitted.

[0263] The transmission method / device according to the embodiments can encode geometry data and generate a geometry bitstream. The geometry bitstream can be configured by LOD or layer, and the geometry bitstream can include a header (geometry header) by LOD or layer configuration unit. The header can include reference information for the next LOD or the next layer. The current LOD (layer) can further include R information (geometry data) that is not included in the previous LOD (layer).

[0264] The receiving method / device according to the embodiments can encode attribute data and generate an attribute bitstream. The attribute bitstream can be configured by LOD or layer, and the attribute bitstream can include a header (attribute header) by LOD or layer. The header can include reference information for the next LOD or the next layer. The current LOD (layer) can further include R information (attribute data) that is not included in the previous LOD (layer).

[0265] The receiving method / device according to the embodiments can receive a bitstream composed of LODs or layers and efficiently decode only the data to be used without a complex intermediate process.

[0266] Figure 15 illustrates a bitstream alignment method according to embodiments.

[0267] The method / device according to the embodiments can align the bitstream of FIG. 14 as in FIG. 15.

[0268] Bitstream alignment method according to embodiments

[0269] The transmission method / device according to the embodiments can transmit geometry and attributes serially, as illustrated in FIG. 15, when transmitting a bitstream. At this time, depending on the type of data, the entire geometry information (geometry data) can be transmitted first, and then attribute information (attribute data) can be transmitted. In this case, there is an advantage in that the geometry information can be quickly restored based on the information in the transmitted bitstream.

[0270] For example, layers (LODs) containing geometry data may be positioned first in the bitstream, and layers (LODs) containing attribute data may be positioned after the geometry layer. Since attribute data is dependent on geometry data, the geometry layer may be positioned first. Furthermore, the position may vary depending on the embodiments. References between geometry headers are possible, and references between attribute headers and geometry headers are also possible.

[0271] Figure 16 illustrates a bitstream alignment method according to embodiments.

[0272] Figure 16 is an example of bitstream alignment according to embodiments.

[0273] Bitstreams that comprise the same layer, including geometry and attribute data, can also be aggregated and transmitted. In this case, using a compression technique that enables parallel decoding of geometry and attributes can reduce decoding time. In this case, information that needs to be processed first (small LoD, geometry before attributes) can be placed first.

[0274] The first layer (2000) includes geometry data and attribute data corresponding to the smallest LOD 0 (layer 0) along with each header, and the second layer (2010) includes LOD 0 (layer 0) and includes geometry data and attribute data of points for a new and more detailed layer 1 (LOD 1) not present in LOD 0 (layer 0) as R1 information. Similarly, a third layer (2020) may exist subsequently.

[0275] The transmission / reception method / device according to the embodiments can efficiently select a desired layer (or LoD) in an application field at the bitstream level when transmitting and receiving a bitstream. In the case of collecting and transmitting geometry information among the bitstream alignment methods according to the embodiments (Fig. 15), an empty part may appear in the middle after selecting the bitstream level, in which case the bitstream may need to be rearranged. In the case of transmitting geometry and attributes by bundling them according to the layer (Fig. 16), unnecessary information can be selectively removed as follows depending on the application field.

[0276] Figure 17 illustrates a method for selecting geometry data and attribute data according to embodiments.

[0277] Bitstream selection according to embodiments

[0278] As above, when a bitstream needs to be selected, the method / device according to the embodiments can select data at the bitstream level as shown in FIG. 17: 1) symmetric geometry and attribute selection, 2) asymmetric geometry and attribute selection, 3) or a combination of both methods.

[0279] 1) Selecting symmetric geometry attributes

[0280] Referring to Figure 17, this shows a case where only LoD1 is selected (LOD 0 +R1, 21000) and transmitted or decoded. Information corresponding to R2 (a new part of LOD 2) corresponding to the upper layer is removed (21010) and transmitted and decoded.

[0281] Figure 18 shows a bitstream selection method according to embodiments.

[0282] 2) Selecting asymmetric geometry and attributes

[0283] The method / device according to the embodiments can transmit geometry and attributes asymmetrically. Only the attribute of the upper layer can be removed (Attribute R2, 22000) and the entire geometry (from level 0 (root level) to level 7 (leaf level) of the triangular octree structure) can be selected and transmitted / decoded (22010).

[0284] Referring to Figure 12, when point cloud data is expressed in an octree structure and hierarchically divided by LOD (or lair), scalable encoding / decoding (scalability) can be supported.

[0285] Scalability features according to embodiments may include slice level scalability and / or octree level scalability.

[0286] A LoD (level of detail) according to embodiments may be used as a unit to represent a set of one or more octree layers. It may also have the meaning of a bundle of octree layers for configuring them into slice units.

[0287] LOD according to the embodiments is a unit that divides data into details by extending the meaning of LOD when encoding / decoding attributes, and can be used in a broad sense.

[0288] That is, spatial scalability by an actual octree layer (or scalable attribute layer) can be provided for each octree layer, but when configuring scalability at the slice level prior to bitstream parsing, it can be selected at the LoD level according to embodiments.

[0289] In an octree structure, LOD 0 can be from the root level to level 4, LOD 1 can be from the root level to level 5, and LOD 2 can be from the root level to level 7 of the leaf.

[0290] That is, as in Fig. 12, when utilizing scalability in slice units such as scalable transmission, the scalable stages provided are three stages of LoD0, LoD1, and LoD2, and the scalable stages that can be provided in the decoding stage by the octree structure are eight stages from the root to the leaf.

[0291] According to embodiments, for example, in FIG. 12, when LoD0 to LoD2 are each composed of slices, a transcoder (FIG. 11 15040) of a receiver or transmitter can select 1) only LoD0, 2) LoD0 and LoD1, or 3) LoD0, LoD1, and LoD2 for scalable processing.

[0292] Example 1) If only LoD0 is selected, the maximum octree level is 4, and one scalable layer among octree layers 0 to 4 can be selected during the decoding process. At this time, the receiver can consider the node size that can be obtained through the maximum octree depth as a leaf node, and the node size at this time can be transmitted as signaling information.

[0293] Example 2) When LoD0 and LoD1 are selected, layer 5 is added, so the maximum octree level becomes 5, and one scalable layer among octree layers 0 to 5 can be selected during the decoding process. At this time, the receiver can consider the node size that can be obtained through the maximum octree depth as a leaf node, and the node size at this time can be transmitted as signaling information.

[0294] According to embodiments, octree depth, octree layer, octree level, etc. refer to units for dividing data in detail.

[0295] Example 3) When selecting LoD0, LoD1, and LoD2, layers 6 and 7 are added, so that the maximum octree level becomes 7, and one scalable layer among octree layers 0 to 7 can be selected during the decoding process. At this time, the receiver can consider the node size that can be obtained through the maximum octree depth as a leaf node, and the node size at this time can be transmitted as signaling information.

[0296] FIG. 19 illustrates a method for constructing a slice including point cloud data according to embodiments.

[0297] Slice configuration according to embodiments

[0298] The transmission method / device / encoder according to the embodiments may be configured by dividing a G-PCC bit stream into a slice structure. A slice may be a data unit for detailed data representation.

[0299] For example, one or more octree layers can be matched to a single slice.

[0300] A transmission method / device according to embodiments, for example, an encoder, may scan nodes (points) included in an octree in the direction of a scan order (2300) to construct a slice (2301)-based bitstream.

[0301] Figure 19(a): Some nodes of the octree layer may be included in one slice.

[0302] An octree layer (e.g., level 0 to level 4) can constitute one slice (2302).

[0303] In an octree layer, for example, some data from level 5 can constitute each slice (2303, 2304, 2305).

[0304] In an octree layer, for example, some data from level 6 can constitute each slice.

[0305] Figure 19(b)(c): When multiple octree layers are matched to a single slice, only a portion of the nodes in each layer may be included. In this way, when multiple slices form a single geometry / attribute frame, the information necessary to construct the layers for the receiver can be conveyed. This may include layer information contained in each slice and node information contained in each layer.

[0306] Figure 19(b): An octree layer, for example, can be configured to contain data from level 0 to level 3 and some data from level 4 as one slice.

[0307] An octree layer, for example, can be configured to contain some data from level 4 and some data from level 5 in one slice.

[0308] An octree layer, for example, can be configured to contain some data from level 5 and some data from level 6 as one slice.

[0309] An octree layer, for example, can organize some data at level 6 into one slice.

[0310] Figure 19(c): Octree layer, for example, data from level 0 to level 4 can be organized into one slice.

[0311] Some data from each of the octree layers levels 5, 6, and 7 can be configured as a single slice.

[0312] An encoder and a device corresponding to the encoder according to the embodiments can encode point cloud data and generate and transmit a bitstream further including parameter information about the encoded data and the point cloud data.

[0313] Furthermore, when generating a bitstream, the bitstream can be generated based on a bitstream structure according to embodiments (e.g., see FIG. 26, etc.). Accordingly, a receiving device, a decoder, a corresponding device, etc. according to embodiments can receive and parse a bitstream configured to suit a selective partial data decoding structure, thereby partially decoding point cloud data and efficiently providing it (see FIG. 11).

[0314] Scalable transmission according to embodiments

[0315] The point cloud data transmission method / device according to the embodiments can scalably transmit a bitstream including point cloud data, and the point cloud data reception method / device according to the embodiments can scalably receive and decode the bitstream.

[0316] When a bitstream according to embodiments such as Fig. 26 is used for scalable transmission, information for selecting a slice required by the receiver can be transmitted to the receiver. Scalable transmission may mean that only a portion of the bitstream is transmitted or decoded rather than decoding the entire bitstream, and the result may be low resolution point cloud data.

[0317] When applying scalable transmission to an octree-based geometry bitstream, it is necessary to be able to construct point cloud data using only information up to a specific octree layer for the bitstream of each octree layer (Fig. 12) from the root node to the leaf node.

[0318] To achieve this, the target octree layer must not have any dependency on information in lower octree layers. This can be a common constraint for geometry / attribute coding.

[0319] Also, when performing scalable transmission, it is necessary to transmit a scalable structure to select a scalable layer at the transmitter / receiver. Considering the octree structure according to the implementation, all octree layers may support scalable transmission, but scalable transmission can be enabled only for a specific octree layer or lower. If some octree layers are included, the necessity or unnecessaryness of the slice can be determined at the bitstream level by indicating which scalable layer the slice is included in. In the example of Fig. 19(a), the yellow highlighted part starting from the root node does not support scalable transmission and configures a single scalable layer, and the octree layers below it can be configured to have a one-to-one match with the scalable layer. Generally, scalability can be supported for the part corresponding to the leaf node, but as in Fig. 19(c), if multiple octree layers are included in a slice, the layers can be defined to configure a single scalable layer.

[0320] At this time, scalable transmission and scalable decoding can be distinguished and used depending on the purpose. Scalable transmission can be used for the purpose of selecting information up to a specific layer without going through a decoder at the transmitting and receiving ends. Scalable decoding is for the purpose of selecting a specific layer during coding. In other words, scalable transmission can support the selection of required information without going through a decoder in a compressed state (at the bitstream stage), so that it can be distinguished at the transmitting or receiving end. On the other hand, scalable decoding can be used in cases such as scalable representation by supporting cases where encoding / decoding is performed only up to the required part during the encoding / decoding process.

[0321] In this case, the layer configuration for scalable transmission and the layer configuration for scalable decoding may differ. For example, the three lower octree layers including leaf nodes may constitute one layer from the perspective of scalable transmission, but from the perspective of scalable decoding, if all layer information is included, scalable decoding may be possible for each of the leaf node layer, leaf node layer-1, and leaf node layer-2.

[0322] The method / device according to the embodiments can perform fine granularity slicing. Fine granularity slicing means fine granularity slicing.

[0323] When fine-grained slicing is enabled, the G-PCC bitstream (see, e.g., Figure 26) can be segmented into multiple sub-bitstreams during the encoding step of the point cloud transmission method according to embodiments. To effectively utilize the layering structure of G-PCC, each slice can include a partial coding layer or coded data for a partial region. Slice segmentation paired with the coding layer structure can efficiently support scalable transmission or spatial random access use cases.

[0324] Figure 20 shows a geometry coding layer structure according to embodiments.

[0325] The method / device according to the embodiments (the transmitting device (10000) of FIG. 1, the point cloud video encoder (10002), the transmitter (10003), the acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, the encoder of FIG. 3, the transmitting device of FIG. 8, the device of FIG. 10, the encoder of FIG. 11, FIG. 23, the encoding of FIG. 24, the bitstream of FIG. 26 and the parameter generation of FIGS. 27 to 30, the encoder of FIGS. 30-31, the transmitting method of FIG. 32, etc.) can generate a layer-based bitstream by encoding a point cloud in a layer structure as in FIG. 20.

[0326] The method / device according to the 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, decoder of FIG. 11, FIG. 23, decoding of FIG. 25, bitstream of FIG. 26 and parameter parsing of FIGS. 27 to 30, decoder of FIGS. 30-31, receiving method of FIG. 33, etc.) can receive layer-based point cloud data and bitstream as in FIG. 20 and selectively decode the data.

[0327] Bitstream and point cloud data according to embodiments can be generated based on coding layer-based slice segmentation. By slicing the bitstream at the end of the coding layer of the encoding process, the method / device according to embodiments can select relevant slices, thereby supporting scalable transmission or partial decoding.

[0328] Fig. 20(a) illustrates a geometry coding layer structure with eight layers, each slice corresponding to a layer group. Layer group 1 includes coding layers 0 to 4. Layer group 2 (3901) includes coding layer 5. Layer group 3 is a group for coding layers 6 and 7. When a geometry (or attribute) has a tree structure with eight levels (depths), data corresponding to one or more levels (depths) can be grouped to hierarchically structure a bitstream. Each group can be included in one slice.

[0329] Figure 20(b) shows the decoded output when two slices are selected from three groups. When the decoder selects groups 1 and 2, it can select partial layers from levels 0 to 5 of the tree. In other words, using slices in the layer group structure, it is possible to support partial decoding of a coding layer without accessing the entire bitstream.

[0330] For the partial decoding process according to the embodiments, the encoder may generate three slices based on the layer group structure. The decoder may select two slices from the three slices to perform partial decoding.

[0331] A bitstream according to embodiments (Fig. 26) may include layer-based groups / slices. Each slice may include a header containing signaling information about point cloud data (geometry data and / or attribute data) included in the slice. A receiving method / device according to embodiments may select partial slices and decode point cloud data included in the payload of the slice based on the header included in the slice.

[0332] In addition to the hierarchical group structure, the method / device according to the embodiments can further divide the hierarchical group into multiple subgroups (subgroups) to consider spatial random access use cases. The subgroups according to the embodiments are mutually exclusive, and the set of subgroups can be identical to the hierarchical group. Since the points of each subgroup form a boundary in a spatial area, the subgroups can be represented using subgroup bounding box information. Using spatial information, the layer group and subgroup structures can support spatial access. By efficiently comparing the region of interest (ROI) and the boundary box information of each slice, spatial random access within a frame or tile can be supported.

[0333] The method / device according to the embodiments may divide layer groups 1 to 3 into one or more subgroups.

[0334] Figure 20 shows an example of a geometry coding layer structure, but an attribute coding layer structure can also be created similarly.

[0335] The method / device according to the embodiments can perform layer-group based slice segmentation. In fine-grained slicing, each slice segment can include coded data of a layer group defined as follows.

[0336] A layer group is defined as a contiguous tree layer group whose start and end depths can be any number within the tree depth, and whose start is less than the end. The order of coded data in a slice segment can be the same as the order of coded data in a single slice.

[0337] For example, consider a geometric coding layer structure with eight layers as shown in Fig. 20(a). In this example, there are three layer groups, and each layer group corresponds to a different slice. When the first two slices are transmitted or selected: layer group 1 for coding layers 0 through 4, layer group 2 for coding layer 5, and layer group 3 for coding layers 0 through 4, the decoded output becomes partial layers 0 through 5 as shown in Fig. 20(b). Using slices in the layer group structure allows for partial decoding of coding layers without accessing the entire bitstream.

[0338] A subgroup is a subset of a layer group whose points are adjacent to each other. Subgroups of a layer group are mutually exclusive, and the set of points in a subgroup of a layer group can be identical to the set of points in the layer group itself. Since the points in each subgroup are bounded in the spatial domain, the boundaries of the subgroup can be described using subgroup boundary box information. Using spatial information, the layer group and subgroup structures can efficiently support access to a region of interest (ROI) by selecting a slice that covers the ROI.

[0339] Figure 21 shows the layer group and subgroup structure according to embodiments.

[0340] The point cloud data and bitstream based on the layer structure illustrated in Fig. 20 can represent a bounding box as in Fig. 21.

[0341] The subgroup structure and the bounding boxes corresponding to the subgroups are illustrated. Layer groups 2 and 3 are divided into two subgroups (group2-1, group2-2) and four subgroups (group3-1, group3-2, group3-3, group3-4), which are included in different slices. Given slices of layer groups and subgroups with bounding box information, 1) the bounding box of each slice is compared with the ROI, 2) the slice whose subgroup bounding box correlates with the ROI is selected, and spatial access can be performed. Then, 3) the selected slices are decoded. Considering the ROI in region 3-3, slices 1, 3, and 6 are selected as the subgroup bounding boxes of layer group 1, subgroups 2-2, and 3-3, covering the ROI area. For efficient spatial access, it is assumed that there is no dependency between subgroups of the same layer group. For live streaming or low-latency use cases, selection and decoding of each slice segment can be performed as it is received, improving time efficiency.

[0342] The method / device according to the embodiments can represent data as a hierarchical tree by layers (which may be referred to as depths, levels, etc.) when encoding geometry and / or attributes. Point cloud data corresponding to each layer (depth / level) can likewise be grouped into layer groups (or groups). Each layer group can be further divided (segmented) into subgroups. Each subgroup can be configured as a slice to generate a bitstream. The receiving device according to the embodiments can receive the bitstream, select a specific slice, decode a subgroup included in the slice, and decode a bounding box corresponding to the subgroup. For example, selecting slice 1 can decode a bounding box corresponding to group 1. Group 1 may be data corresponding to the largest area. If the user wants to additionally view the detailed area for group 1, the method / device according to the embodiments can partially hierarchically access the bounding boxes (point cloud data) of group 2-2 and / or group 3-3 for the detailed area included in the area of ​​group 1 by selecting slice 3 and / or slice 6.

[0343] Figure 22 shows a multi-resolution, multi-size ROI according to embodiments.

[0344] Based on scalability and spatial accessibility, layer group slicing can provide efficient access to large-scale or dense point cloud data. Due to the large number of points and data size, rendering or displaying such content can take significant time. Another approach is to adjust the level of detail based on the viewer's interest. For example, when the viewer is far away from a scene or object, structural or global region information is more important than local details. Conversely, when the viewer is close to a specific area or object, detailed information about the region of interest is needed. Using an adaptive approach, the renderer can efficiently present sufficiently high-quality data to the viewer. Figure 22 shows examples of incremental detail changes for three viewing distances, where the viewing distance changes based on the ROI. For example, this could be 1) a high-level view (coarse detail), 2) a medium-level view (medium-level detail), or 3) a low-level view (fine-grained detail).

[0345] Figure 23 illustrates a layer group slice according to embodiments.

[0346] The method / device according to the embodiments (the transmitting device (10000) of FIG. 1, the point cloud video encoder (10002), the transmitter (10003), the acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, the encoder of FIG. 3, the transmitting device of FIG. 8, the device of FIG. 10, the encoder of FIG. 11, FIG. 23, the encoding of FIG. 24, the bitstream of FIG. 26 and the parameter generation of FIGS. 27 to 30, the encoder of FIGS. 30-31, the encoding method of FIG. 32, etc.) can encode layer-based point cloud data as in FIG. 23.

[0347] The method / device according to the 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, decoder of FIG. 11, FIG. 23, decoding of FIG. 25, bitstream of FIG. 26 and parameter parsing of FIGs. 27 to 30, decoder of FIGs. 30-31, decoding method of FIG. 33, etc.) can receive and decode sliced ​​data based on a layer group as in FIG. 23.

[0348] The method / device according to the embodiments can support high-resolution ROI based on the scalability and spatial accessibility of hierarchical slicing.

[0349] Referring to FIG. 20, the encoder can generate bitstream slices of an octree layer group or spatial subgroups of each layer group. Upon request, the encoder can select and transmit slices that match the ROI of each resolution. Since the bitstream does not contain details other than the requested ROI, the overall bitstream size can be smaller than the tile-based approach. The decoder at the receiver can combine the slices to generate three outputs. For example, 1) a high-level view output can be generated from layer group slice 1, 2) a mid-level view output can be generated from layer group slice 1 and a selected subgroup of layer group 2, and 3) a low-level view with fine detail output can be generated from selected subgroups of layer group 1 and layer groups 2 and 3. Since the outputs can be generated incrementally, the receiver can provide a zoom-like viewing experience, where the resolution can gradually increase from the high-level view to the low-level view.

[0350] The encoder is a point cloud encoder according to embodiments, and may correspond to a geometry encoder and an attribute encoder. The encoder may slice point cloud data based on a layer group (or groups). A layer may be referred to as a tree depth, a layer level, etc. The depth of a geometry octree and / or the level of an attribute layer may be divided into layer groups (or subgroups).

[0351] The slice selector can work in conjunction with the encoder to select a split slice (or sub-slice) to selectively transmit partially, such as layer group 1 to layer group 3.

[0352] The decoder can selectively and partially decode transmitted point cloud data. For example, a high-level view can be decoded based on layer group 1 (high depth / layer / level, or index 0, closer to the root). Then, a mid-level view can be decoded based on layer group 1 and layer group 2, with a slightly higher depth / level index than layer group 1 alone. A low-level view can be decoded based on layer groups 1 to 3.

[0353] Figure 24 illustrates an encoding process for layer group slicing according to embodiments.

[0354] The method / device according to the embodiments (the transmitting device (10000) of FIG. 1, the point cloud video encoder (10002), the transmitter (10003), the acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, the encoder of FIG. 3, the transmitting device of FIG. 8, the device of FIG. 10, the encoder of FIG. 11, FIG. 23, the encoding of FIG. 24, the bitstream of FIG. 26 and the parameter generation of FIGS. 27 to 30, the encoder of FIGS. 30-31, the encoding method of FIG. 32, etc.) can region-adaptive encode point cloud data in FGS by generating layer groups and / or subgroups as (segmented) slices as in FIG. 24.

[0355] The inputs to the layer group slicing encoder are point cloud data and parameter information describing the structure of the layer group slicing. At the beginning of each tree depth, the layer group structure parameters are used to determine the layer group of the target tree depth. The layer group index determines the subgroup index of each node using the subgroup boundary box. When the subgroup for a node changes, the context state and buffer used by the previous subgroup encoder are saved and the context state and buffer of the current subgroup encoder are loaded. Using separate encoders for each subgroup allows for the persistence of context state within the subgroup. Additionally, the extent of the geometric occupancy atlas is updated by considering the subgroup boundaries above the atlas boundaries to restrict neighboring nodes to belong to the same subgroup as the current node. Both methods allow each coded bitstream to be decoded independently without the need for node information in neighboring subgroups. This process is repeated for all nodes across all tree depths. When the end of a node of the target tree depth is reached, a Fine-Granularity Slice (FGS) is generated that matches one-to-one with the subgroups of each layer group.

[0356] An encoder according to embodiments generates parameter information such as SPS, GPS, LGSI, etc., and determines a subgroup. Whenever a subgroup is changed, information used to encode point cloud data within the subgroup is stored, and necessary information is loaded to efficiently encode. For example, context information can be stored and loaded for use during the next encoding. A geometry occupancy atlas is updated according to the encoding. Nodes of a subgroup are encoded. When all nodes in the occupancy tree are encoded, a geometry data unit header is generated, and a geometry bitstream including parameters, a geometry data unit header, a geometry data unit, etc. is generated.

[0357] In the following, the technique used for independent decryption is explained through pseudo code.

[0358] Encoder Save and Load (Layer Group and Subgroup Determination Step) Steps:

[0359] As mentioned in the encoder process, saving and loading the encoder state is necessary to ensure independent decoding of each subgroup. To provide flexible subgroup partitioning, this process is performed for each node in the tree hierarchy.

[0360] For each tree layer, the layer group of the tree layer is determined and fixed for all tree depths within the layer group. Since a layer group is a set of contiguous tree layers, the layer group index changes at the beginning of the layer group. Subgroups are determined using the determined layer group. Since a subgroup is a group of nodes bounded by a subgroup bounding box, the subgroup of a node is found by comparing the node position with the subgroup bounding box. Whenever a subgroup or layer group changes, the encoder state of the previous subgroup is saved for later use, and the encoder state of the current subgroup is loaded for continuous encoding.

[0361] pseudo code:

[0362] for (depth = 0; depth < maxDepth; depth++) {

[0363] / determine layer-group index

[0364] if (depth == 0) {

[0365] curLayerGroupId = 0;

[0366] curSubgroupId = 0;

[0367] load current context state;

[0368] sum_layers = numLayersPerLayerGroup[curLayerGroupId];

[0369] }

[0370] else if (depth == sum_layers) {

[0371] prevLayerGroupId = curLayerGroupId++;

[0372] prevSubgroupId = curSubgroupId;

[0373] curSubgroupId = 0;

[0374] save previous encoder state;

[0375] load current encoder state;

[0376] sum_layers += numLayersPerLayerGroup[curLayerGroupId];

[0377] }

[0378] else if (numSubgroupsMinus1[curLayerGroupId] > 0) {

[0379] prevSubgroupId = curSubgroupId;

[0380] curSubgroupId = 0;

[0381] save previous encoder state;

[0382] reload current encoder state;

[0383] }

[0384] for (all nodes in curLayerGroupId) {

[0385] / determine subgroup index

[0386] if (!(nodePos >= bbox_min && nodePos < bbox_max)) {

[0387] for (i = 0; i<=numSubgroupsMinus1[curLayerGroupId]; i++) {

[0388] if (nodePos >= bbox_min[i] && nodePos < bbox_max[i]){

[0389] prevSubgroupId = curSubgroupId;

[0390] curSubgroupId = i;

[0391] / save and load encoder

[0392] save previous encoder state;

[0393] if (first node of the current subgroup)

[0394] load reference encoder state;

[0395] else

[0396] reload current encoder state;

[0397] break;

[0398] }}}}

[0399] }

[0400] Geometry Accuracy Atlas Update Steps:

[0401] To account for the subgroup boundaries of the geometry occupancy atlas, _maxRange and _minRange are defined in the MortonMap3D class. When the atlas is in a subgroup, the minimum and maximum ranges are set to 0, and the edge lengths of the cubes are set respectively. If the minimum boundary of the subgroup is greater than the atlas minimum, _minRange is set to the minimum of the subgroup boundary. If the maximum boundary of the subgroup is less than the atlas maximum, _maxRange is set to the maximum of the subgroup boundary. When ranges are used, the portion of the atlas that overlaps the subgroup boundary box is considered active, and the nodes in the active area are used as neighbors. This ensures that FGS is decoded without nodes in neighboring subgroups.

[0402] pseudo code:

[0403] class MortonMap3D {

[0404] setRange( ) {

[0405] for (m = 0; m < 3; m++) {

[0406] / _maxRange

[0407] if (bboxMax < atlasOrigin + _cubeSize)

[0408] _maxRange[m] = bboxMax - atlasOrigin;

[0409] else

[0410] _maxRange[m] = _cubeSize;

[0411] / _minRange

[0412] if (bboxMin > atlasOrigin)

[0413] _minRange[m] = bboxMin - atlasOrigin;

[0414] else

[0415] _minRange[m] = 0;

[0416] }}

[0417] }

[0418] This operation updates the atlas used for geometry coding, taking into account subgroup boundaries. The atlas is a lookup table (LUT) that stores a certain range of peripheral nodes to speed up tasks like geometry neighbor searches. If the current node's location falls outside the atlas range, it must be updated.

[0419] Looking at the encoder and decoder behavior: If a subgroup boundary exists within an atlas boundary, the atlas boundary (_maxRange, _minRange) is updated to the subgroup boundary. This allows for the atlas to be updated without having to update the entire atlas, even when a subgroup within the atlas changes.

[0420] Here: _maxRange, _minRange represent the minimum and maximum values ​​of the actual usable range within the atlas. atlasOrigin, cubeSize represent the starting position and size of the atlas. bboxMin, _bboxMax represent the minimum and maximum values ​​of the bounding box position of a specific layer group or subgroup.

[0421] Figure 25 illustrates a decoder process for layer group slicing according to embodiments.

[0422] Figure 25 can follow the reverse process of Figure 24.

[0423] The method / device according to the embodiments (the receiving device (10004), the receiver (10005), the point cloud video decoder (10006) of FIG. 1, the transmission-decoding-rendering (20002-20003-20004) of FIG. 2, the decoder of FIG. 7, the receiving device of FIG. 9, the device of FIG. 10, the decoder of FIG. 11, FIG. 23, the decoding of FIG. 25, the bitstream of FIG. 26 and the parameter parsing of FIGs. 27 to 30, the decoder of FIGs. 30-31, the decoding method of FIG. 33, etc.) can receive a (segmented) slice including a layer group and / or a subgroup as in FIG. 25 and decode point cloud data in an area-adaptive manner.

[0424] Decoder process:

[0425] The decoding process of the layer group slicing reference SW for the first FGS (Fine Granularity Slice) is the same as the conventional geometry slice decoding (parameter set parsing, data unit header parsing, data unit decoding). When layer group slicing is enabled, if the next dependent geometry data unit has the same Slice_id, it is considered as the FGS for the first slice. Considering the context reference and node inheritance between the upper and child subgroups, the order of the FGS is assumed to be sorted in ascending order by layer_group_id and subgroup_id.

[0426] Before decoding a dependent data unit, the context state, output node, and layer group parameters of the previous slice are stored in the buffer for the next slice. After parsing the dependent data unit header, the parent group of the current subgroup is retrieved by finding the subgroup whose subgroup boundary box is a superset of the current subgroup boundary box. Once the parent subgroup is determined, the parent node of the current dependent data unit is selected. By using the selected node as the initial node of the decoding process, the current dependent data unit is decoded up to the tree layer covered by the current layer group. The decoding process of the dependent data unit is performed repeatedly until the geometry bitstream ends.

[0427] The following describes in more detail the additional steps involved in finding parent subgroups and parent nodes.

[0428] A decoder according to embodiments parses parameter information such as SPS, GPS, and LGSI included in a bitstream. Parses information included in a data unit header included in a slice such as FGS. Parses point cloud data included in a data unit based on the header information. When layer group slicing is enabled, updates buffer and layer group parameters, and parses data unit header information of a dependent data unit dependent on an upper data unit. Detects a parent subgroup and selects a parent node. Parses a dependent data unit.

[0429] Parent subgroup detection step:

[0430] In the decoding of slices in FGS, nodes in a parent subgroup are used as inputs to child subgroups to provide continuous decoding at layer group boundaries. This can be derived using parent-child spatial relationships due to the hierarchical structure of layer group slicing. A child subgroup is a subset of its parent subgroup whose boundary boxes are spatially exclusive from the bounding boxes of other child subgroups within the same layer group.

[0431] Based on these relationships, we can find the spatial superset of the current fragment and retrieve fragments with parent subgroups. Using the subgroup_bbox_origin and subgroup_bbox_size signaled in the data unit header, we can compare the boundary box information of the subgroups at the previous layer-group level to find the parent subgroup.

[0432] pseudo code:

[0433] parentLayerGroup = curLayerGroup - 1;

[0434] for (i = 0; i < numSubgroups[parentLayerGroup]; i++) {

[0435] if (_bboxMin[parentLayerGroup][i] <= curBboxMin

[0436] && _bboxMax[parentLayerGroup][i] > curBboxMin) {

[0437] parentSubgroup = i;

[0438] break;

[0439] }

[0440] }

[0441] This behavior allows us to infer parent subgroups without additional signaling based on the positional relationship between parent subgroups and child subgroups.

[0442] From the encoder's perspective: When defining a child subgroup, you can define a partitioning of the parent subgroup's bounding box. The child subgroup is a subregion of the parent subgroup, and the encoder can infer the parent subgroup's index using the method described above.

[0443] From the decoder's perspective: If the bounding box of a parent subgroup is a superset of the bounding boxes of its child subgroups, then the subgroup index is determined as the parent subgroup index.

[0444] parentLayerGroup is the index of the parent layer-group related to the current layer-group.

[0445] parentSubgroup is the subgroup index of the subgroup that has a parent-child relationship with the current subgroup.

[0446] curLayerGroup is the current layer-group.

[0447] numSubgroups is the number of subgroups belonging to the layer-group.

[0448] _bboxMin, _bboxMax are the minimum and maximum values ​​of the bounding box of a specific layer-group or subgroup.

[0449] curBboxMin is the minimum value of the bounding box position of the subgroup currently being coded.

[0450] Steps to select input parent nodes:

[0451] In decoding a dependent slice, the output node of the parent subgroup is used as input for decoding 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. On the other hand, 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 in the parent subgroup is compared with the boundary box of the child subgroup.

[0452] pseudo code:

[0453] for (node ​​= inNodes.begin(); node != inNodes.end(); node++) {

[0454] if (node.Pos >= bbox_min && node.Pos < bbox_max)

[0455] fifo.emplace_back(node);

[0456] else

[0457] continue;

[0458] }

[0459] Below, we describe the slice structure for the layer configuration described above and the signaling method for scalable transmission.

[0460] Figure 26 shows a bitstream configuration according to embodiments.

[0461] The method / device according to the embodiments can generate a bitstream as in FIG. 26. The bitstream includes encoded geometry data and attribute data, and may include parameter information.

[0462] The syntax and semantics for parameter information are as follows.

[0463] According to embodiments, information about a separated slice can be defined in the parameter set and SEI message of the bitstream as follows.

[0464] A bitstream can include a sequence parameter set, a geometry parameter set, an attribute parameter set, a geometry slice header, and an attribute slice header. Depending on the application or system, they can be defined in corresponding locations or in separate locations, and their application scopes and application methods can be used differently. In other words, a signal can have different meanings depending on where it is transmitted. If it is defined in SPS, it can be applied equally to the entire sequence. If it is defined in GPS, it can indicate that it is used for position recovery. If it is defined in APS, it can indicate that it is applied to attribute recovery. If it is defined in TPS, it can indicate that the signaling is applied only to points within a tile. If it is transmitted in slice units, it can indicate that the signal is applied only to the corresponding slice. In addition, depending on the application or system, they can be defined in corresponding locations or in separate locations, and their application scopes and application methods can be used differently. Additionally, 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 can be conveyed through a parameter set of a higher concept, etc.

[0465] Each abbreviation means: SPS: Sequence Parameter Set, GPS: Geometry Parameter Set, APS: Attribute Parameter Set, TPS: Tile Parameter Set, Geom: Geometry bitstream = geometry slice header+ geometry slice data, Attr: Attrobite bitstream = attribute slice header + attribute slice data.

[0466] The embodiments define the information independently of the coding technique, but can be defined in conjunction with the coding method, and can be defined in the tile parameter set of the bitstream to support regionally different 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 can be conveyed through a parameter set of a higher concept, etc.

[0467] Alternatively, a bitstream can be selected at the system level by defining a NAL (Network abstract layer) unit for the bitstream and passing relevant information for selecting a layer, such as a layer_id.

[0468] Below, parameters (which may be called various names such as metadata, signaling information, etc.) according to the embodiments may be generated in the process of the transmitter according to the embodiments, and may be transmitted to the receiver according to the embodiments and used in the reconstruction process.

[0469] For example, parameters according to embodiments may be generated in a metadata processing unit (or metadata generator) of a transmitting device according to embodiments described below, and may be obtained in a metadata parser of a receiving device according to embodiments.

[0470] Parameters describing the layer group structure are signaled at various levels. In SPS, common structure information is described in the SPS, and details for each layer group or subgroup are signaled in the slice header. Additionally, a layer group structure inventory and dependent slice headers are provided to describe the overall layer group structure. The definition of G-PCC slices and the signaling method for fine-granularity slicing (FGS) are as follows.

[0471] 1) Definition of G-PCC slice.

[0472] A. Slice: A set of points coded as one independent fine-granularity slice and zero or more dependent fine-granularity slices. For example, a slice may include multiple fine-granularity slices (FGS), and the FGS may be referred to as a segmented slice, a subdivided slice, etc.

[0473] B. Dependent Fine-granularity Slice: A data unit in a slice that depends on the previous data unit within the same slice.

[0474] C. Independent Fine-granularity Slice: The first [geometry] data unit of the slice.

[0475] 2) Fine-granularity slicing is enabled in SPS.

[0476] 3) The essential information required to decode dependent fine-granularity slices is conveyed by the dependent data unit header, including context inheritance and slice-specific bounding boxes.

[0477] 4) Define a layer group structure inventory to describe the relationships between fine-granularity slices.

[0478] Figures 27a and 27b illustrate sequence parameter sets (SPS) according to embodiments.

[0479] simple_profile_compatibility_flag: Indicates whether the bitstream conforms to the simple profile (if 1) or not (if 0).

[0480] dense_profile_compatibility_flag: Indicates whether the bitstream conforms to the Dense profile (if 1) or not (if 0).

[0481] predictive_profile_compatibility_flag: Indicates whether the bitstream complies with the prediction profile (if 1) or not (if 0).

[0482] main_profile_compatibility_flag: Indicates whether the bitstream complies with the main profile (if 1) or not (if 0).

[0483] slice_reordering_constraint_flag: Indicates whether the bitstream is sensitive to the reordering or removal of slices within the coded point cloud frame (if 1) or not (if 0). If slices are reordered or removed when slice_reordering_constraint is 1, the resulting bitstream may not be fully decoded.

[0484] unique_point_positions_constraint_flag: Equal to 1 indicates that every point in each coded point cloud frame must have a unique position. Unique_point_positions_constraint equal to 0 indicates that more than one point in a coded point cloud frame can have the same position.

[0485] sps_seq_parameter_set_id: Identifies the SPS so that other DUs (data units) can reference it.

[0486] seq_origin_bits: The length in bits of each seq_origin_xyz syntax element, excluding the sign bit.

[0487] seq_origin_xyz[ k ] and seq_origin_log2_scale: Together, these represent the XYZ origin of the sequence and the coding coordinate system in sequence coordinate units at the application-specific origin. If seq_origin_bits is 0, seq_origin_xyz[ k ] and seq_origin_log2_scale are inferred to be 0. The th XYZ component of the origin is specified by the expression SeqOrigin[ k ].

[0488] seq_bounding_box_size_bits: The length of each seq_bbox_size_minus1_xyz syntax element in bits.

[0489] seq_bounding_box_size_minus1_xyz[ k ]: plus 1 represents the kth XYZ component of the coded volume dimensions in sequence coordinates.

[0490] seq_unit_numerator_minus1, seq_unit_denominator_minus1, and seq_unit_is_metres: Together, they represent lengths expressed as unit vectors in the sequence coordinate system.

[0491] sps_num_attribute_sets: Indicates the number of attributes listed in the SPS attribute list.

[0492] attribute_instance_id[ attrId ]: Indicates the instance identifier for the identified attribute.

[0493] attribute_bitdepth_minus1[ attrId ]: Adding 1 specifies the bit depth for all components of the identified attribute.

[0494] layer_group_enabled_flag equal to 1 indicates that the geometry bitstream of the slice is contained in multiple slices that match the coding layer group or its subgroups. layer_group_enabled_flag equal to 0 indicates that the geometry bitstream is contained in a single slice.

[0495] num_layer_groups_minus1 + 1 represents the number of layer groups representing contiguous tree layer groups that are part of the geometry coding tree structure. num_layer_groups_minus1 ranges from 0 to the number of coding tree layers.

[0496] layer_group_id represents the layer group ID of the slice. The range of layer_group_id is from 0 to num_layer_groups_minus1.

[0497] num_layers_minus1 + 1 represents the number of coding layers included in the ith layer group. The total number of layer groups can be derived by adding all (num_layers_minus1[i] + 1) to num_layer_groups_minus1 when i is 0.

[0498] subgroup_enabled_flag is equal to 1. Indicates that the ith layer group is divided into two or more subgroups where the point sets in the subgroups of the layer group are equal to the point sets of the layer group. When subgroup_enabled_flag of the ith layer group is equal to 1, subgroup_enabled_flag of the jth layer group is equal to 1 when j is greater than or equal to i. If subgroup_enabled_flag is equal to 0, it indicates that the current layer group is not divided into multiple subgroups but is contained in a single slice.

[0499] subgroup_bbox_origin_bits_minus1 + 1 is the bit length of the syntax element subgroup_bbox_origin.

[0500] subgroup_bbox_size_bits_minus1 + 1 is the bit length of the syntax element subgroup_bbox_size.

[0501] Figure 28 illustrates a dependent geometry data unit header according to embodiments.

[0502] The geometry parameter set ID (dgdu_geometry_parameter_set_id) indicates the active GPS indicated by gps_geom_parameter_set_id. The dgdu_geometry_parameter_set_id value is equal to the gdu_geometry_parameter_set_id value of the corresponding slice.

[0503] dgdu_slice_id indicates the slice to which the current dependent geometry data unit belongs.

[0504] layer_group_id represents the layer group ID of the slice. layer_group_id ranges from 0 to num_layer_groups_minus1. If not present, layer_group_id is inferred to be 0.

[0505] subgroup_id indicates a subgroup identifier of the layer group referenced by layer_group_id. The subgroup_id must be in the range 0 to num_subgroups_minus1[layer_group_id], where subgroup_id indicates the order of slices within the same layer_group_id. If not present, subgroup_id is inferred to be 0.

[0506] subgroup_bbox_origin indicates the origin position of the subgroup bounding box of the ith subgroup indicated by subgroup_id in the jth layer group indicated by layer_group_id.

[0507] subgroup_bbox_size represents the subgroup bounding box size of the ith subgroup indicated by subgroup_id in the jth layer group indicated by layer_group_id.

[0508] The bounding box of a point in a subgroup is described by subgroup_bbox_origin and subgroup_bbox_size. The area in the bounding box of the ith subgroup does not overlap with the bounding box of the jth subgroup when i and j are not equal.

[0509] ref_layer_group_id represents the layer group identifier of the context reference of the current dependent data unit. ref_layer_group_id ranges from 0 to the layer_group_id of the current dependent data unit.

[0510] ref_subgroup_id indicates an indicator for a reference subgroup of the layer group pointed to by ref_layer_group_id. The range of ref_subgroup_id is from 0 to num_subgroup_id_minus1 for the layer group pointed to by ref_layer_group_id.

[0511] A bitstream according to embodiments may include at least one slice or subdivided slices, and may include a geometry data unit and an attribute data unit. Each data unit includes a header. In addition, the data unit includes an independent data unit and a dependent data unit. The dependent data unit may indicate a containment relationship according to a dependency relationship between upper and lower nodes. In addition, Fig. 28 shows the syntax of a dependent geometry data unit header, and similarly, the syntax of a geometry data unit header may also be the same as Fig. 28.

[0512] Figure 29 shows a layer group structure inventory according to embodiments.

[0513] The sequence parameter set ID (lgsi_seq_parameter_set_id) represents the sps_seq_parameter_set_id value.

[0514] lgsi_frame_ctr_lsb_bits represents the length of the lgsi_frame_ctr_lsb syntax element in bits.

[0515] lgsi_frame_ctr_lsb represents the lgsi_frame_ctr_lsb_bits least significant bits of the FrameCtr for which the group structure inventory is valid. A layer group structure inventory remains valid until it is replaced by another layer group structure inventory.

[0516] lgsi_num_slice_ids_minus1 + 1 represents the number of slices in the layer group structure inventory.

[0517] lgsi_slice_id represents the slice ID of the sid-th slice within the layer group structure inventory.

[0518] lgsi_num_layer_groups_minus1 + 1 represents the number of layer groups.

[0519] lasi_subgroup_bbox_origin_bits_minus1 + 1 is the bit length of the syntax element lgsi_subgroup_bbox_origin.

[0520] lgsi_subgroup_bbox_size_bits_minus1 + 1 is the bit length of the syntax element lgsi_subgroup_bbox_size.

[0521] lgsi_layer_group_id represents the indicator of the layer group.

[0522] lgsi_num_layers_minus1 + 1 represents the number of coded layers in the slice of the i-th layer group of the sid-th slice. The total number of coded layers required to decode the n-th layer group is equal to the sum of lgsi_num_layers_minus1[sid][i] + 1, where i is from 0 to n.

[0523] lgsi_num_subgroups_minus1 + 1 represents the number of subgroups in the i-th layer group of the sid-th slice.

[0524] lgsi_subgroup_id represents the ID of the layer group.

[0525] lgsi_parent_subgroup_id indicates the indicator of a subgroup within the layer group pointed to by lgsi_subgroup_id.

[0526] lgsi_subgroup_bbox_origin indicates the origin of the subgroup bounding box of the subgroup pointed to by lgsi_subgroup_id among the layer groups pointed to by lgsi_layer_group_id.

[0527] lgsi_subgroup_bbox_size indicates the size of the subgroup bounding box of the subgroup indicated by lgsi_subgroup_id among the layer groups indicated by lgsi_layer_group_id.

[0528] lgsi_origin_bits_minus1 + 1 represents the length of the lgsi_origin_xyz syntax element in bits.

[0529] lgsi_origin_xyz represents the origin of all partitions. The value of lgsi_origin_xyz[ k ] is equal to sps_bounding_box_offset[ k ].

[0530] lgsi_origin_log2_scale represents a scaling factor for scaling the components of lgsi_origin_xyz. The value of lgsi_origin_log2_scale is equal to sps_bounding_box_offset_log2_scale.

[0531] Meanwhile, variations and combinations between the embodiments of this document are possible. The terms used in this document can be understood based on their intended meaning, within the scope of their widespread use in the relevant field.

[0532] The method according to the embodiments can encode and decode point cloud data based on a plane occupancy coding scheme.

[0533] If occtree_planar_enabled is 1, planar occupancy coding can be performed.

[0534] Planar occupancy coding decomposes the node occupancy bitmap into axis-aligned planes. Each coded axis has two perpendicular planes that child nodes can occupy. For each coded axis appropriate to the plane, plane occupancy coding specifies whether one of the two planes is unoccupied. Plane occupancy is used to constrain and infer the bit coding of the node occupancy bitmap in bit-wise occupancy coding.

[0535] Each occupied plane has at least one child node.

[0536] The definition of an occupied tree node requires that at least one plane along each coded axis be occupied.

[0537] For example, if a node has three coded axes that are suitable for planes, there are six axis-aligned planes in total. Information about the occupancy status of the two TV planes is coded along the S-axis ( = 0).

[0538] Planar position:

[0539] When the method and device according to the embodiments perform layer-group slicing, a break in the depth direction of the occupancy tree occurs, in which case compression efficiency may be reduced due to artificial separation of continuity between nodes. As a method for minimizing this problem, information of a parent subgroup may be used for child subgroup coding. The method according to the embodiments may further include a method for context continuity of a plane position that determines whether to perform planar coding of a node among the parent subgroup information.

[0540] 1) How to pass OccupancyMapP directly to each node; and / or

[0541] 2) How to infer OccupancyMapP based on parent subgroup node position information

[0542] OccupancyMapP may refer to an occupancy map. The method and device according to the embodiments may continuously process an occupancy map (OccupancyMapP) on an FGS structure to improve compression and restoration performance.

[0543] The method according to the embodiments uses the context for the planar position information of the previous node in coding the planar position information of each node for the planar mode, and can select the context table as follows.

[0544] Determine CtxIdxPlanePos for occ_plane_pos[ k ] (for angle-ineligible axes):

[0545] Contextualization of occ_plane_pos[ k ] for nodes that are not eligible for angular contextualization (nodes with AngularEligible equal to 0) is specified by the CtxIdxPlanePos expression.

[0546] CtxIdxPlanePos := occtree_planar_buffer_disabled || ￢PrevOccSinglePlane[k][PlanarNodeAxisLoc[k]] ? adjPlaneCtxInc : 12 Х k + 4 Х adjPlaneCtxInc + 2 Х zoneCtxInc + prevPlanePosCtxInc + 3

[0547] The adjPlaneCtxInc expression determines whether a node has an adjacent neighbor on one side along the th axis, and if so, on which of the two faces it is on. The adjacent neighbors are:

[0548] Nodes along the -th axis identified by the corresponding bit of the occupied neighbor pattern (adjNeighHL); and if the node is in the lower -th axis plane of its parent node, sibling nodes in that higher plane (identified by OccPlaneMask

[0001] [k]).

[0549] adjPlaneCtxInc := (adjNeighHL | sibPlaneH << 1) % 3

[0550] where

[0551] adjNeighHL := (OccNeighPat >> 2 Х k) & 3

[0552] sibPlaneH := (Nloc[k] & 1) && (OccupancyMapP & OccPlaneMask[1][k]) ≠? 0

[0553] Whenever occtree_coded_axis[Depth-1][k] is 0, sibPlaneH is always 0.

[0554] If occtree_planar_buffer_disabled is 0, contextualization uses information about the previous planar eligible node in the plane identified by the coded node position.

[0555] The expression zoneCtxInc determines whether the coded node is within ±1 zone from the identified previous node.

[0556] zoneCtxInc := Abs(a - b) > 1

[0557] where

[0558] a := PrevPlanarNodeZone[k][PlanarNodeAxisLoc[k]]

[0559] b := PlanarNodeZone[k]

[0560] The expression prevPlanePosCtxInc identifies the occupied plane position of the identified previous node.

[0561] prevPlanePosCtxInc := PrevOccPlanePos[k][PlanarNodeAxisLoc[k]]

[0562] occtree_planar_buffer_disabled indicates whether contextualization of per-node occupied planar positions using previously coded node planar positions is disabled (if 1) or disabled (if 0). If occtree_planar_buffer_disabled is absent, it is inferred to be 0.

[0563] for (k = 0; k < 3; k++)

[0564] if (PlanarEligible[k]) {

[0565] PrevPlanarNodeZone[k][PlanarNodeAxisLoc[k]] = PlanarNodeZone[k]

[0566] PrevOccSinglePlane[k][PlanarNodeAxisLoc[k]] = occ_single_plane[k]

[0567]

[0568] if (occ_single_plane[k])

[0569] PrevOccPlanePos[k][PlanarNodeAxisLoc[k]] = occ_plane_pos[k]

[0570] }

[0571] State variables:

[0572] Information about the previous plane-eligible coded node is specified via the following state variables: index and axisLoc, which identify the plane's position along the th axis.

[0573] Array PrevPlanarNodeZone; PrevPlanarNodeZone[K][axisLoc] is the planar zone of the previous planar eligible node in the identified plane.

[0574] Array PrevOccSinglePlane; PrevOccSinglePlane[K][axisLoc] is the occ_single_plane[ K ] value of the previous plane eligible node in the identified plane.

[0575] Array PrevOccPlanePos; PrevOccPlanePos[ K ][axisLoc] is the value of occ_plane_pos[k] for the previous plane eligible node of the identified plane.

[0576] After each occupancy_tree_node syntax structure, the status for each plane eligibility axis is updated.

[0577] for (k = 0; k < 3; k++)

[0578] if (PlanarEligible[k]) {

[0579] PrevPlanarNodeZone[k][PlanarNodeAxisLoc[k]] = PlanarNodeZone[k]

[0580] PrevOccSinglePlane[k][PlanarNodeAxisLoc[k]] = occ_single_plane[k]

[0581]

[0582] if (occ_single_plane[k])

[0583] PrevOccPlanePos[k][PlanarNodeAxisLoc[k]] = occ_plane_pos[k]

[0584] }

[0585] occ_single_plane[ k ] indicates in the node occupancy bitmap whether the child node's position occupies a single plane (if 1) or two planes perpendicular to the th axis (if 0). If 1, the position in the single plane is specified by occ_plane_pos[ k ].

[0586] occ_plane_pos[ k ] represents the node relative position along the kth axis with respect to the occupied plane specified in occ_single_plane[ k ] as 1.

[0587] Identification of the plane:

[0588] A plane perpendicular to the axis of a coded node is identified by a position located modulo 2^14 along the axis.

[0589] PlanarNodeAxisLoc[k] := Nloc[k] & 0x3FFF

[0590] 1) How to directly pass the OccupancyMap to each node

[0591] In the decoding process described above, the occupancy map (OccupancyMapP) of the parent's child nodes is required to obtain the index CtxIdxPlanePos of the context table of occ_plane_pos. Since the information about the parent in the startDepth position of the subgroup of layer-group slicing belongs to the parent subgroup, it can be inherited from the parent subgroup.

[0592] For example, at the end of coding a parent subgroup or at the endDepth of the current subgroup, you can store OccNeighPatEq0 (a variable that identifies whether there are no nodes in the neighbor pattern occupied by the identified node) and OccNodeChildCnt (a variable that indicates the number of child nodes of the identified node) for each node.

[0593] For example, you can save the OccupancyMap of each node at the end of the parent subgroup coding or at the endDepth of the current subgroup.

[0594] An OccupancyMap can be defined as the occupancy information of a parent node's children. For a subgroup specified by the layer group index (LayerGroupIdx) and subgroup index (SubgroupIdx), the OccupancyMap of the nodeCount node belonging to endDepth can be stored as follows.

[0595] SubgroupOccupancyMap[LayerGroupIdx][SubgroupIdx][nodeCount++] = OccupancyMap

[0596] At this time, since nodes belonging to the same parent have the same OccupancyMap, we can remove duplicate information to reduce storage capacity and store OccupancyMap as a representative for only one child per parent.

[0597] You can restore the OccupancyMap of each node at the start of coding the child subgroup or at startDepth.

[0598] When coding a subgroup, if Dpth = startDepth, the OccupancyMap can be restored based on the SubgroupOccupancyMap for the parent subgroup (ParentLayerGroupIdx, ParentSubgroupIdx) as follows.

[0599] OccupancyMapP = SubgroupOccupancyMap[ParentLayerGroupIdx][ParentSubgroupIdx][nodeCount++]

[0600] 2) How to infer OccupancyMapP based on parent subgroup node position information

[0601] The OccupancyMapP for the parent node can be inferred as follows:

[0602] Node occupancy bitmap of parent:

[0603] Below, we use the expression OccupancyMapP to represent the node occupancy bitmap.

[0604] OccupancyMapP = 0

[0605] ns = ((Ns >> 1) << 1)

[0606] nt = ((Nt >> 1) << 1)

[0607] nv = ((Nv >> 1) << 1)

[0608] for (s = 0; s <= occtree_coded_axis[Dpth][0]; s++)

[0609] for (t 0; t <= occtree_coded_axis[Dpth][1]; t++)

[0610] for (v 0; v <= occtree_coded_axis[Dpth][2]; v++)

[0611] OccupancyMapP |= OccNodePresent[Dpth][ns + s][nt + t][nv + v]

[0612] If there is at least one node in the current subgroup at tree position (ns, nt, nv), OccNodePresent[Dpth][ns][nt][nv] is equal to 1.

[0613] ns OccNodeLoc [NodeIdx][0]

[0614] nt = OccNodeLoc [NodeIdx][1]

[0615] nv = OccNodeLoc [NodeIdx][2]

[0616] Consideration of direct node:

[0617] Direct nodes can be stored in separate memory for output. In this case, the direct nodes stored in separate memory can be additionally considered when determining whether an occupied node is present for the current node or its parent node.

[0618] If the direct node is at (ns, nt, nv), OccNodePresent[Dpth][ns][nt][nv] is equal to 1.

[0619] ns = PointPos [PointCnt][0] >> NodeSizeLog2[0]

[0620] nt = PointPos [PointCnt][1] >> NodeSizeLog2[1]

[0621] nv = PointPos [PointCnt][2] >> NodeSizeLog2[2]

[0622]

[0623] OccNodePresent[Dpth-1][ns][nt][nv] equal to 1 when direct nodes are present at (ns, nt, nv)

[0624] ns = PointPos [PointCnt][0] >> ParentNodeSizeLog2[0]

[0625] nt = PointPos [PointCnt][1] >> ParentNodeSizeLog2[1]

[0626] nv = PointPos [PointCnt][2] >> ParentNodeSizeLog2[2]

[0627] Definition of Node size:

[0628] The expressions NodeSizeLog2[ k ] and ChildNodeSizeLog2[ k ] represent the log2 dimensions of the coded node and its child nodes, respectively.

[0629] NodeSizeLog2[k] := OccLvlNodeSizeLog2[Dpth][k]

[0630] ChildNodeSizeLog2[k] := OccLvlNodeSizeLog2[Dpth + 1][k]

[0631] ParentNodeSizeLog2[k] := OccLvlNodeSizeLog2[Dpth - 1][k]

[0632] If geom_scaling_enabled is 0, QuantizedNodeSizeLog2[ k ] is equal to NodeSizeLog2[ k ].

[0633] Output of a direct node:

[0634] At the end of the direct node, the coded points are scaled and added to the output point list.

[0635] if (occ_direct_node) {

[0636] for (dnPt = 0; dnPt <= direct_point_cnt_eq2; dnPt++, PointCnt++, DirectNodePointCnt++)

[0637] for (k =0; k < 3; k++)

[0638] PointPos[PointCnt][k] = OccPosScaleK(k, DnPtPos[dnPt][k])

[0639]

[0640] for (i = 0; i < direct_dup_point_cnt; i++, PointCnt++, DirectNodePointCnt++)

[0641] for (k = 0; k < 3; k++)

[0642] PointPos[PointCnt][k] = PointPos[PointCnt - 1][k]

[0643] }

[0644] Due to the embodiments, the transmitter and receiver devices provide the following effects.

[0645] Referring to Fig. 11, embodiments transmit compressed data divided according to any standard for point cloud data. When layered coding is used, compressed data can be divided and transmitted according to layers, which increases the storage and transmission efficiency of the transmitter. Fig. 11 illustrates an embodiment in which the geometry and attributes of point cloud data are compressed and provided as a service. In a PCC-based service, the compression ratio or the number of data can be adjusted and transmitted according to the receiver performance or transmission environment. In the case where point cloud data is bundled in single slice units as in the past, when the receiver performance or transmission environment changes, 1) a bitstream suitable for each environment is converted in advance and stored separately and selected at the time of transmission, or 2) a process of conversion prior to transmission is required. In this case, if the number of receiver environments to be supported increases or the transmission environment changes frequently, storage space issues or delays due to conversion may become a problem.

[0646] Figure 30 shows the transmission and reception operation of point cloud data according to layers according to embodiments.

[0647] Referring to FIG. 30, the effects of the embodiments will be described. When compressed data is divided and transmitted according to layers, only the necessary portions of pre-compressed data can be selectively transmitted at the bitstream stage 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, as only the necessary layers are selectively transmitted before transmission (bitstream selector).

[0648] Figure 31 shows the transmission and reception operation of point cloud data according to layers according to embodiments.

[0649] Referring to FIG. 31, the effects of the embodiments will be further explained. The embodiments include a method for dividing and transmitting compressed data according to any standard for point cloud data. When layered coding is used, compressed data can be divided and transmitted according to layers, in which case the efficiency of the receiving end increases. FIG. 32 shows the operation of the transmitting and receiving ends when transmitting point cloud data composed of layers. In this case, when transmitting information that can restore the entire PCC data regardless of the performance of the receiver, the receiver requires a process (data selection or sub-sampling) to select only the data corresponding to the required layer after restoring the point cloud data through decoding. In this case, since the transmitted bitstream is already decoded, a delay may occur in a receiver that aims for low delay, or decoding may not be performed depending on the performance of the receiver.

[0650] In embodiments where the bitstream is divided into slices and transmitted, the receiver can selectively transmit the bitstream to the decoder based on the decoder performance or the density of point cloud data to be represented based on the application. In this case, since the selection is made before decoding, decoder efficiency is improved and there is the advantage of being able to support decoders with various performances.

[0651] Various elements of the embodiments may be implemented by hardware, software, firmware, or a combination thereof. Various elements of the embodiments may be implemented on a single chip, such as a hardware circuit. In some embodiments, the embodiments may optionally be implemented on individual chips. In some embodiments, at least one of the elements of the embodiments may be implemented within one or more processors that include instructions for performing operations according to the embodiments.

[0652] The operations according to the embodiments described above may be performed by a transmitting device and / or a receiving device according to the embodiments. The transmitting / receiving device may include a transmitting / 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 / receiving device.

[0653] The processor may be referred to as a controller or the like, and may correspond to, for example, hardware, software, and / or a combination thereof. The operations according to the above-described embodiments may be performed by the processor. Furthermore, the processor may be implemented as an encoder / decoder or the like for the operations of the above-described embodiments.

[0654] Figure 32 shows an encoding method according to embodiments.

[0655] The encoding method according to the embodiments may include a step of encoding geometry data of point cloud data (S3200); and / or a step of encoding attribute data of point cloud data (S3210).

[0656] The step of encoding geometry data (S3200) and the step of encoding attribute data (S3210) may include operations described in the transmitting device (10000), point cloud video encoder (10002), transmitter (10003) of FIG. 1, acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, encoder of FIG. 3, transmitting device of FIG. 8, device of FIG. 10, encoder of FIG. 11, FIG. 23, encoding of FIG. 24, bitstream of FIG. 26 and parameter generation of FIGs. 27 to 30, encoder of FIGs. 30-31, etc.

[0657] Referring to FIG. 24, the step of encoding geometry data (S3200) includes: encoding geometry data of a subgroup within a layer group, and the step of encoding attribute data (S3210) includes: encoding attribute data of a subgroup within a layer group, and the layer group includes at least one layer of an accumunity tree of geometry data, and at least one layer may include at least one subgroup.

[0658] The step of encoding geometry data (S3200) includes: encoding geometry data based on a plane occupancy coding scheme for an occupancy tree, wherein a plane position of a node with respect to a plane can be encoded based on a context regarding a plane position of a previous node of the node.

[0659] As a method for utilizing continuity between parent-child subgroups when applying layer group slicing (FGS) to geometry data, in relation to a method of directly transferring an occupancy map (occupancy information) between subgroups or nodes and a method of inferring an occupancy map (occupancy information) based on a parent subgroup node position (geometry), planar occupancy coding is described, wherein the step of encoding geometry data (S3200) includes: encoding geometry data based on a planar occupancy coding method for an occupancy tree, and a planar position of a node for a plane can be encoded based on a context regarding a planar position for a previous node of the node.

[0660] With respect to a method of directly transferring an occupancy map (occupancy information) between subgroups or nodes, geometry data for a parent subgroup within a layer group may be encoded based on a plane occupancy coding method, and geometry data for a child subgroup of the parent subgroup may be encoded based on the occupancy information of the parent subgroup.

[0661] In relation to a method of storing an occupancy map (occupancy information) of a parent subgroup, an occupancy map of each node of an occupancy tree at an end depth of the parent subgroup is stored, and the occupancy map can be stored as subgroup occupancy map information identified based on at least one of an index of a layer group, an index of a subgroup, or the number of nodes.

[0662] In relation to a method for restoring a saved occupancy map, geometry data for a child subgroup of a parent subgroup can be encoded based on a restored occupancy map based on subgroup occupancy map information for a parent subgroup identified based on at least one of an index of the parent layer group or an index of the parent subgroup.

[0663] With respect to a method for inferring (deriving) an occupancy map based on a parent subgroup node position, geometry data for a child subgroup of a parent subgroup is decoded based on occupancy information of the parent subgroup within a layer group, and the occupancy information of the parent subgroup can be derived based on whether at least one node currently exists within the subgroup.

[0664] With respect to direct nodes, occupancy information of a parent subgroup can be derived based on whether a direct node exists in the occupancy tree.

[0665] The encoding method of FIG. 32 can be performed by an encoding device as in FIG. 1. The encoding device includes a memory; and at least one processor connected to the memory; and the at least one processor can be configured to: encode geometry data of point cloud data; and encode attribute data of point cloud data.

[0666] The embodiments further include a computer-readable storage medium storing a bitstream generated by the method according to FIG. 32.

[0667] Embodiments further include a method comprising: obtaining a bitstream for point cloud data, the bitstream being generated based on a step of encoding geometry data of the point cloud data; and a step of encoding attribute data of the point cloud data; and transmitting data including the bitstream.

[0668] Figure 33 shows a decryption method according to embodiments.

[0669] A decoding method according to embodiments may include a step of decoding geometry data of point cloud data in a bitstream (S3300); and / or a step of decoding attribute data of point cloud data (S3310).

[0670] The step of decoding geometry data (S3300) and the step of decoding attribute data (S3310) may include operations described in the receiving device (10004), receiver (10005), point cloud video decoder (10006) of the above-described FIG. 1, 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, FIG. 23, decoding of FIG. 25, bitstream of FIG. 26 and parameter parsing of FIG. 27 to FIG. 30, decoder of FIG. 30-31, etc.

[0671] The decryption method of Fig. 33 and the encoding method of Fig. 32 can correspond to each other in reverse processes.

[0672] Referring to FIG. 25, FGS-based geometry and / or attribute decoding; and with respect to the FIG. 20 FGS structure, the step of decoding geometry data (S3300) includes: decoding geometry data of a sub-group within a layer group, and the step of decoding attribute data (S3310) includes: decoding attribute data of a sub-group within a layer group, wherein the layer group includes at least one layer of an accumulator tree of geometry data, and at least one layer may include at least one sub-group.

[0673] As a method for utilizing continuity between parent-child subgroups when applying layer group slicing (FGS) to geometry data, in relation to a method of directly transferring an occupancy map (occupancy information) between subgroups or nodes and a method of inferring an occupancy map (occupancy information) based on a parent subgroup node position (geometry), planar occupancy coding is described, wherein the step of decoding geometry data (S3300) includes: decoding geometry data based on a planar occupancy coding method for an occupancy tree, and a planar position of a node for a plane can be decoded based on a context regarding a planar position for a previous node of the node.

[0674] With respect to a method of directly transmitting an occupancy map (occupancy information) between subgroups or nodes, geometry data for a parent subgroup within a layer group may be decoded based on a plane occupancy coding method, and geometry data for a child subgroup of the parent subgroup may be decoded based on the occupancy information of the parent subgroup.

[0675] In relation to a method of storing an occupancy map (occupancy information) of a parent subgroup, an occupancy map of each node of an occupancy tree at an end depth of the parent subgroup is stored, and the occupancy map can be stored as subgroup occupancy map information identified based on at least one of an index of a layer group, an index of a subgroup, or the number of nodes.

[0676] In relation to a method for restoring a saved occupancy map, geometry data for a child subgroup of a parent subgroup can be decoded based on a restored occupancy map based on subgroup occupancy map information for a parent subgroup identified based on at least one of an index of the parent layer group or an index of the parent subgroup.

[0677] With respect to a method for inferring (deriving) an occupancy map based on a parent subgroup node position, geometry data for a child subgroup of a parent subgroup is decoded based on occupancy information of the parent subgroup within a layer group, and the occupancy information of the parent subgroup can be derived based on whether at least one node currently exists within the subgroup.

[0678] With respect to direct nodes, occupancy information of a parent subgroup can be derived based on whether a direct node exists in the occupancy tree.

[0679] The decoding method of FIG. 33 can be performed by a decoding device, as in FIG. 1. The decoding device includes at least one processor connected to a memory; and the at least one processor can be configured to: decode geometry data of point cloud data in a bitstream; and decode attribute data of the point cloud data.

[0680] The method and device according to the embodiments provide the following technical effects: spatial random access and scalable coding effects can be provided while maintaining compression and restoration performance of point cloud data.

[0681] The embodiments have been described in terms of methods and / or devices, and the descriptions of methods and devices may be applied complementarily.

[0682] For the convenience of explanation, each drawing has been described separately, but it is also possible to design a new embodiment by combining the embodiments described in each drawing. In addition, designing a computer-readable recording medium having a program recorded thereon for executing the previously described embodiments, as needed by a person skilled in the art, also falls within the scope of the embodiments. The devices and methods according to the embodiments are not limited to the configurations and methods of the embodiments described above, but the embodiments may be configured by selectively combining all or part of the embodiments so that various modifications can be made. Although preferred embodiments of the embodiments have been illustrated and described, the embodiments are not limited to the specific embodiments described above, and various modifications can be made by a person skilled in the art to which the present invention pertains without departing from the gist of the embodiments claimed in the claims, and such modifications should not be understood individually from the technical idea or prospect of the embodiments.

[0683] The various components of the devices of the embodiments may be implemented by hardware, software, firmware, or a combination thereof. The various components of the embodiments may be implemented by a single chip, for example, a single hardware circuit. According to embodiments, the components according to the embodiments may be implemented by separate chips. According to embodiments, at least one of the components of the devices of the embodiments may be configured with one or more processors capable of executing one or more programs, and the one or more programs may perform, or include instructions for performing, one or more of the operations / methods according to the embodiments. The executable instructions for performing the methods / operations of the devices of the embodiments may be stored in non-transitory CRMs or other computer program products configured to be executed by one or more processors, or may be stored in temporary CRMs or other computer program products configured to be executed by one or more processors. In addition, the memory according to the embodiments may be used as a concept including not only volatile memory (e.g., RAM, etc.), but also non-volatile memory, flash memory, PROM, etc. Additionally, it may include implementations in the form of carrier waves, such as transmissions via the Internet. Furthermore, processor-readable recording media may be distributed across network-connected computer systems, allowing processor-readable code to be stored and executed in a distributed manner.

[0684] 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, “or” in this document is interpreted as “and / or”. For example, “A or B” can mean 1) “A” only, 2) “B” only, or 3) “A and B”. In other words, “or” in this document can mean “additionally or alternatively”.

[0685] Terms such as "first," "second," etc. may be used to describe various components of the embodiments. However, the various components according to the embodiments should not be interpreted as limited by these terms. These terms are merely used to distinguish one component from another. For example, a first user input signal may be referred to as a "second user input signal." Similarly, a second user input signal may be referred to as a "first user input signal." The use of these terms should be interpreted as not departing from the scope of the various embodiments. Although "first user input signal" and "second user input signal" are both user input signals, they do not mean the same user input signals unless the context clearly indicates otherwise.

[0686] The terminology used to describe the embodiments is for the purpose of describing particular embodiments and is not intended to be limiting of the embodiments. As used in the description of the embodiments and in the claims, the singular is intended to include the plural unless the context clearly dictates otherwise. The expressions “and / or” are used to mean all possible combinations of terms. The expression “includes” describes the presence of features, numbers, steps, elements, and / or components, but does not mean that additional features, numbers, steps, elements, and / or components are not included. Conditional expressions such as “if” or “when” used to describe the embodiments are not intended to be limited to only optional cases. When a specific condition is satisfied, a related action is performed in response to a specific condition, or a related definition is intended to be interpreted.

[0687] Additionally, the operations according to the embodiments described in this document may be performed by a transceiver device including a memory and / or a processor according to the embodiments. The memory may store programs for processing / controlling the 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. The operations according to 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 the memory.

[0688] Meanwhile, the operations according to the embodiments described above may be performed by a transmitting device and / or a receiving device according to the embodiments. The transmitting / receiving device may include a transmitting / 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 / receiving device.

[0689] The processor may be referred to as a controller or the like, and may correspond to, for example, hardware, software, and / or a combination thereof. The operations according to the above-described embodiments may be performed by the processor. Furthermore, the processor may be implemented as an encoder / decoder or the like for the operations of the above-described embodiments.

[0690] As described above, the relevant contents have been described in the best form for carrying out the embodiments.

[0691] As described above, the embodiments may be applied in whole or in part to a point cloud data transmission and reception device and system.

[0692] Those skilled in the art may make various changes or modifications to the embodiments within the scope of the embodiments.

[0693] Embodiments may include modifications / changes, which do not depart from the scope of the claims and their equivalents.

Claims

1. A step of decoding geometry data of point cloud data in a bitstream; and A step of decoding attribute data of the above point cloud data; comprising; How to decrypt.

2. In paragraph 1, The steps for decoding the above geometry data are: Includes decoding geometry data of a subgroup within a layer group, The steps to decode the above attribute data are: Including decoding attribute data of the subgroup within the layer group, The layer group includes at least one layer of the occupancy tree of the geometry data, wherein at least one layer comprises at least one subgroup, How to decrypt.

3. In paragraph 2, The steps for decoding the above geometry data are: Decoding the above geometry data based on a plane occupancy coding scheme for the occupancy tree, The plane position of a node with respect to a plane is decoded based on the context regarding the plane position of the node with respect to the previous node. How to decrypt.

4. In paragraph 3, Geometry data for a parent subgroup within the above layer group is decoded based on the above plane occupancy coding method, Geometry data for a child subgroup of the parent subgroup is decoded based on the occupancy information of the parent subgroup. How to decrypt.

5. In paragraph 4, At the end depth of the parent subgroup, the occupancy map of each node of the occupancy tree is stored, The above occupancy map is stored as subgroup occupancy map information identified based on at least one of the index of the layer group, the index of the subgroup, or the number of nodes. How to decrypt.

6. In paragraph 5, Geometry data for the child subgroups of the above parent subgroups Based on the restored occupancy map information for the parent subgroup identified based on the index of the parent layer group, or at least one of the indices of the parent subgroup, decoded, How to decrypt.

7. In paragraph 3, Geometry data for a child subgroup of the parent subgroup is decoded based on the occupancy information of the parent subgroup within the layer group, The above occupancy information of the above parent subgroup is derived based on whether at least one node exists in the current subgroup. How to decrypt.

8. In paragraph 7, Based on whether a direct node exists in the above occupancy tree, the occupancy information of the parent subgroup is derived. How to decrypt.

9. Memory; and At least one processor connected to the memory; wherein the at least one processor comprises: Decoding geometry data of point cloud data in the bitstream; and Decoding attribute data of the above point cloud data; configured to do so, Decryption device.

10. 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.

11. In paragraph 10, The steps for encoding the above geometry data are: Including encoding the geometry data of a subgroup within a layer group, The steps for encoding the above attribute data are: Including encoding attribute data of the subgroup within the layer group, The layer group includes at least one layer of the occupancy tree of the geometry data, wherein at least one layer comprises at least one subgroup, Encoding method.

12. In paragraph 11, The steps for encoding the above geometry data are: Encoding the above geometry data based on a plane occupancy coding scheme for the occupancy tree, The plane position of a node with respect to a plane is encoded based on the context regarding the plane position of the node with respect to the previous node. Encoding method.

13. Memory; and At least one processor connected to the memory; wherein the at least one processor comprises: Encoding the geometry data of point cloud data; and Encoding attribute data of the above point cloud data; configured to do so; Decryption device.

14. A computer-readable storage medium storing a bitstream generated by the method according to Article 10.

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

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