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

Abstract

A decoding method according to embodiments may comprise the steps of: receiving a bitstream including point cloud data; and decoding the point cloud data. An encoding method according to embodiments may comprise the steps of: encoding point cloud data; and transmitting a bitstream including the point cloud data.

Description

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

[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 method for transmitting point cloud data according to embodiments may include a step of encoding point cloud data and a step of transmitting a bitstream including the point cloud data. A method for receiving point cloud data according to embodiments may include a step of receiving a bitstream including point cloud data and a step of decoding 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 illustrates 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 partial point cloud bitstream transmission and decoding according to embodiments.

[0022] Figure 13 illustrates scalable decoding of point cloud data according to embodiments.

[0023] Figure 14 illustrates scalable coding according to embodiments.

[0024] Figure 15 shows a layer group for scalable transmission according to embodiments.

[0025] Figure 16 shows partial geometry and partial attributes according to embodiments.

[0026] Figure 17 shows partial geometry and partial attributes according to embodiments.

[0027] Figure 18 shows partial geometry and partial attributes according to embodiments.

[0028] Figure 19 shows partial geometry and partial attributes according to embodiments.

[0029] Figure 20 illustrates a bitstream including point cloud data and parameters according to embodiments.

[0030] Figure 21 shows scalable lifting LoD generation information according to embodiments.

[0031] Figure 22 shows a set of attribute parameters according to embodiments.

[0032] Fig. 23 shows an encoder (transmitter) according to embodiments.

[0033] Fig. 24 shows a decoder (receiving device) according to embodiments.

[0034] Figure 25 illustrates scalable lifting according to embodiments.

[0035] Figure 26 illustrates subgroup LoD sampling considering the missing layer according to embodiments.

[0036] Figure 27 illustrates FGS-related encoder and decoder use cases according to embodiments.

[0037] Figure 28 illustrates FGS-related encoder and decoder use cases according to embodiments.

[0038] Figure 29 shows a sequence parameter set according to embodiments.

[0039] Figure 30 shows a sequence parameter set according to embodiments.

[0040] Figure 31 shows a sequence parameter set according to embodiments.

[0041] Figure 32 shows a decoding process according to embodiments.

[0042] Figure 33 illustrates a geometry data unit header (geometry_data_unit_header) according to embodiments.

[0043] Figure 34 shows a geometry data unit (geometry_data_unit) according to embodiments.

[0044] Figure 35 illustrates a geometry data unit header (geometry_data_unit_header) according to embodiments.

[0045] Figure 36 illustrates a dependent geometry data unit header (dependent_geometry_data_unit_header) according to embodiments.

[0046] Figure 37 shows an occupancy tree (occupancy_tree) according to embodiments.

[0047] Figure 38 illustrates a geometry data unit footer (geometry_data_unit_footer) according to embodiments.

[0048] Figure 39 shows an encoding method according to embodiments.

[0049] Figure 40 shows a decryption method according to embodiments.

[0050] 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.

[0051] 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.

[0052] Figure 1 illustrates an example of a point cloud content provision system according to embodiments.

[0053] 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.

[0054] 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.

[0055] 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).

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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)).

[0060] 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).

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0069] 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).

[0070] 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.).

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.).

[0076] 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.

[0077] FIG. 3 illustrates an example of a point cloud encoder according to embodiments.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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).

[0092] 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.

[0093] As shown in the drawing, the converted attributes are input to the RAHT conversion unit (30008) and / or the LOD generation unit (30009).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] A coefficient quantization unit (30011) according to embodiments quantizes attribute-coded attributes based on coefficients.

[0098] An arithmetic encoder (30012) according to embodiments encodes quantized attributes based on arithmetic coding.

[0099] 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).

[0100] Figure 4 illustrates examples of octree and occupancy codes according to embodiments.

[0101] 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.

[0102] 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.

[0103] d =Ceil(Log2(Max(x_n^int,y_n^int,z_n^int,n=1,…,N)+1))

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113]

[0114] Find the minimum of the added values, and perform the projection process 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.

[0115] Table 2-1. Triangles formed from vertices ordered 1,… ,n

[0116] n triangles

[0117] 3 (1,2,3)

[0118] 4 (1,2,3), (3,4,1)

[0119] 5 (1,2,3), (3,4,5), (5,1,3)

[0120] 6 (1,2,3), (3,4,5), (5,6,1), (1,3,5)

[0121] 7 (1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)

[0122] 8 (1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1)

[0123] 9 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7,9,3)

[0124] 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)

[0125] 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)

[0126] 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)

[0127] 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).

[0128] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0129] 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.

[0130] 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.

[0131] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] graph. Attribute prediction residuals quantization pseudo code

[0138] int PCCQuantization(int value, int quantStep) {

[0139] if( value >=0) {

[0140] return floor(value / quantStep + 1.0 / 3.0);

[0141] } else {

[0142] return -floor(-value / quantStep + 1.0 / 3.0);

[0143] }

[0144] }

[0145] graph. Attribute prediction residuals inverse quantization pseudo code

[0146] int PCCInverseQuantization(int value, int quantStep) {

[0147] if( quantStep ==0) {

[0148] return value;

[0149] } else {

[0150] return value * quantStep;

[0151] }

[0152] }

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 3) Create temporary arrays called updateweight and update and initialize them to 0.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163]

[0164] 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:

[0165]

[0166] The gDC values ​​are also quantized and entropy coded, like the high-pass coefficients.

[0167] Fig. 7 illustrates an example of a point cloud decoder according to embodiments.

[0168] 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.

[0169] As described in Figure 1, the point cloud decoder can perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

[0170] 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).

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] An arithmetic decoder (7005) according to embodiments decodes an attribute bitstream using arithmetic coding.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Figure 8 is an example of a transmission device according to embodiments.

[0184] 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).

[0185] 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).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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).

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] A slice is a series of syntax elements that represent all or part of a coded point cloud frame.

[0200] 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.

[0201] Fig. 9 is an example of a receiving device according to embodiments.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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).

[0208] 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).

[0209] The inverse quantization processing unit (9005) according to the embodiments can inverse quantize the decoded geometry.

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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).

[0214] 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.

[0215] Fig. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] <PCC+XR>

[0222] 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.

[0223] 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.

[0224] <PCC+XR+모바일폰>

[0225] The XR / PCC device (1030) can be implemented as a mobile phone (1040) or the like by applying PCC technology.

[0226] The mobile phone (1040) can decode and display point cloud content based on PCC technology.

[0227] <PCC+자율주행+XR>

[0228] Autonomous vehicles (1020) can be implemented as mobile robots, vehicles, unmanned aerial vehicles, etc. by applying PCC technology and XR technology.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] The PCC method / device according to the embodiments can be applied to a vehicle providing an autonomous driving service.

[0236] Vehicles providing autonomous driving services are connected to PCC devices to enable wired / wireless communication.

[0237] 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.

[0238] The encoding method / device according to the embodiments includes and performs 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. 11 to FIG. 13, the partial encoding of FIG. 14 to FIG. 19, the bitstream and parameter generation of FIG. 20 to FIG. 22 and FIG. 29 to FIG. 30, the parameter information generation of FIG. 31 to FIG. 38, and the encoding method of FIG. 39.

[0239] The decoding method / device according to the embodiments includes and performs 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 to FIG. 13, the partial decoding of FIG. 14 to FIG. 19, the bitstream and parameter parsing of FIG. 20 to FIG. 22 and FIG. 29 to FIG. 30, the decoding of FIG. 23 to FIG. 28, the parameter information parsing of FIG. 31 to FIG. 38, and the decoding method of FIG. 40.

[0240] Additionally, the encoding / decoding method / device according to the embodiments may be abbreviated as the method / device according to the embodiments.

[0241] 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.

[0242] Methods and devices according to embodiments include a scheme for signaling geometry depth information for multiple receiver support (Geometry occupancy tree depth signaling for multiple decoders with different capabilities).

[0243] 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. In this case, a scalable lifting method for scalable coding may be used when performing attribute coding, and additional considerations are required for use in a partial geometry coding environment.

[0244] The embodiments relate to a technique for constructing a data structure composed of a point cloud. The embodiments include a method for informing a receiver of a sampling location by taking into account a partial geometry coding environment when generating a scalable LoD for scalable attribute coding.

[0245] Referring to FIGS. 3 and 7, point cloud data is composed of the location (geometry: eg, XYZ coordinates) and attributes (eg, color, reflectance, intensity, grayscale, opacity, etc.) of each data. In 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.

[0246] The encoding / decoding method according to the embodiments assumes full octree depth geometry when generating LoD used in scalable lifting, and applies sampling positions alternately according to the octree depth, thereby increasing compression efficiency when compressing using attributes located near the node center. When scalable transmission is supported, partial geometry is supported, and in this case, a mismatch in the sampling positions of scalable lifting LoD generation may occur between the encoder and decoder, which may cause a problem of inaccurate representation of the partial point cloud. By changing or clearly signaling the sampling reference position according to the method according to the embodiments, the problem of mismatch between geometry and attributes during partial decoding can be solved.

[0247] The method and device according to the embodiments further include a method for signaling a geometry occupancy tree depth considering a single bitstream multiple decoder. For example, information about an occupancy tree subset related to each geometry data unit can be transmitted. A depth, an octree coded axis (occtree_coded_axis), and an octree level point count (occtree_lvl_point_cnt_minus1) can be generated and transmitted / received as signaling information. An octree according to the embodiments can be interpreted as a term identical to an occupancy tree.

[0248] Figure 11 illustrates the encoding, transmission, and decoding processes of point cloud data according to embodiments.

[0249] Point cloud data is compressed and transmitted by dividing the location information of data points and feature information such as color / brightness / reflectivity into geometry and attribute information. At this time, PC data can be organized according to the level of detail in an octree structure with layers or LoD (Level of Detail), and scalable point cloud data coding and representation are possible based on this. 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, but there is currently no method to remove unnecessary data in advance. In other words, when only a portion of the scalable PCC bitstream needs to be transmitted (when only some layers are decoded during scalable decoding), it is not possible to selectively transmit only the required portion. Therefore, 1) the necessary portion is re-encoded after decoding, or 2) the entire data is transmitted and then selectively applied at the receiver. However, in case 1), there may be a delay due to the time required for decoding and re-encoding, and in case 2), there is a disadvantage in that bandwidth efficiency is reduced due to the transmission of unnecessary data, and when a fixed bandwidth is used, data quality must be lowered for transmission.

[0250] Figure 12 illustrates partial point cloud bitstream transmission and decoding according to embodiments.

[0251] If compressed data is divided and transmitted by layer, it has the advantage of selectively transmitting only the necessary portions of pre-compressed data at the bitstream level without a separate conversion process. This is efficient in terms of storage space, as only one storage space is required per stream. Furthermore, because only the necessary layers are selectively transmitted before transmission (bitstream selector), transmission is also efficient in terms of bandwidth.

[0252] Figure 13 illustrates scalable decoding of point cloud data according to embodiments.

[0253] From the receiver (decoder)'s perspective, when transmitting information that can restore the entire PCC data regardless of the receiver's performance, the receiver needs to restore the point cloud data through decoding and then select only the data corresponding to the required layers (data selection or sub-sampling). In this case, since the transmitted bitstream has already been decoded, it may cause delay in receivers targeting low latency or may not be able to decode depending on the receiver's performance. However, when the bitstream is divided into slices and transmitted, the receiver can selectively transmit the bitstream to the decoder according to the decoder performance or the density of the point cloud data to be expressed in the application. In this case, since the selection is performed before decoding, the decoder efficiency is increased and there is the advantage of supporting decoders with various performances.

[0254] Figure 14 illustrates scalable coding according to embodiments.

[0255] The encoding / decoding method according to the embodiments can scalably encode and decode point cloud data.

[0256] Scalable coding:

[0257] The encoding method according to the embodiments can compress and transmit point cloud data to enable scalable transmission. First, a tool that can support scalability can be used when using position and attribute compression. In addition, when transmitting compressed data, slices can be subdivided to enable partial decoding, and a basis for selecting each slice can be signaled before decoding. Information for selecting the slices required by the receiver can be transmitted. Scalable transmission may mean supporting a case where 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.

[0258] When applying scalable transmission to an octree (same as an occupancy tree)-based geometry bitstream, point cloud data can be constructed using only information up to a specific octree layer for the bitstream of each octree layer from the root node to the leaf node. To achieve this, there is no dependency on the information of the lower octree layers for the target octree layer (layer of the occupancy tree). The dependency condition can be a constraint commonly applied to geometry and attribute coding.

[0259] In the case of attribute coding, partial decoding may be possible according to the octree depth when using scalable lifting, similar to octree-based geometry coding. Each octree depth may belong to an LoD, and one or more octree depths may match an LoD. Fig. 14 illustrates a method for generating LoD when using scalable lifting, and shows a method for selecting an attribute of a parent node as one of the attributes of a child node based on the geometry octree structure. As shown in Fig. 14, when the octree depth is 4, the attribute of a node belonging to octree depth 3 can select one of the attributes of the child nodes belonging to octree depth 4. Similarly, the attribute of a node belonging to octree depth 2 can select one of the attributes of the child nodes belonging to octree depth 3. In scalable lifting, attributes can be assigned to nodes at all octree levels by recursively selecting representative attributes based on the attributes of child nodes in the direction from the leaf node to the root node.

[0260] Figure 14 illustrates a case where the position of a node selected among child nodes varies depending on the octree level. At the leaf node level (octree depth 4), the first node can be used as an attribute of the parent node when sampling, and when sampling at octree depth 3, the last node can be used as an attribute of the parent node. The node (1400) identified by shade indicates the position selected as an attribute of the parent node.

[0261] The encoding method according to the embodiments can encode the entire attribute data based on the level of detail related to the octree (accumulation tree). Among the nodes belonging to each depth (or layer) of the octree (accumulation tree), if the index of the depth (or layer) is even, the attribute of the first node in the sorted order among the child nodes belonging to a parent node can be set (sampled) as the attribute of the parent node. If the index of the depth (or layer) is odd, the attribute of the last node in the sorted order among the child nodes belonging to a parent node can be set (sampled) as the attribute of the parent node.

[0262] A decryption method according to embodiments can decode entire attribute data based on a level of detail related to an octree (accumulation tree). Among the nodes belonging to each depth (or layer) of the octree (accumulation tree), if the index of the depth (or layer) is even, the attribute of the first node in the sorted order among the child nodes belonging to a parent node can be set (sampled) as the attribute of the parent node. If the index of the depth (or layer) is odd, the attribute of the last node in the sorted order among the child nodes belonging to a parent node can be set (sampled) as the attribute of the parent node.

[0263] Figure 15 shows a layer group for scalable transmission according to embodiments.

[0264] The encoding method according to the embodiments can generate a layer group for scalable transmission.

[0265] The geometry and attribute coding scheme supports layer-based scalability, and by configuring slices according to the layer structure, scalability can be supported before decoding, such as in scalable transmission applications. Figure 15 illustrates a method for configuring layer groups according to an octree structure.

[0266] Layer group: Represents a group of layer structure units that occur in G-PCC coding, such as octree layers and LoD layers.

[0267] Subgroup: Represents a set of adjacent nodes based on location information for a single layer group. Alternatively, the group can be formed based on the lowest layer within the layer group (which may refer to the layer closest to the root, e.g., layer 6 for group 3). The group can be formed by grouping adjacent nodes based on the Moulton code order, by grouping adjacent nodes based on distance, or by grouping adjacent nodes based on coding order. Additionally, nodes in a parent-child relationship can be defined to exist within a single subgroup.

[0268] When defining a subgroup, the boundary occurs in the middle of the layer, and whether to have continuity at the boundary, whether to use entropy continuously, such as the entropy continuity enable flag (sps_entropy_continuation_enabled_flag), the entropy continuity flag (gsh_entropy_continuation_flag), etc., and by indicating the reference slice ID (ref_slice_id), continuity with the previous slice can be maintained.

[0269] When using a layer group and subgroup structure, it is necessary to convey a scalable structure for selecting a scalable layer at the transmitter / receiver (encoder / decoder). Considering the octree structure, all octree layers may support scalable transmission, but scalable transmission may only be possible for certain octree layers and below. When including some of the octree layers, it is possible to determine whether the slice is necessary or not at the bitstream level by indicating which scalable layer the slice belongs to. For example, starting from the root node, a single scalable layer can be configured without supporting scalable transmission up to a certain depth, and the octree layers below 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, and as shown in Fig. 15, when multiple octree layers are included in a slice, a single scalable layer can be configured for the layers.

[0270] The embodiments may be used to distinguish between scalable transmission and scalable decoding depending on the purpose. In the case of scalable transmission, the embodiments may be used for the purpose of selecting information up to a specific layer without going through a decoder at the transmitting and receiving ends. In the case of scalable decoding, a specific layer can be selected during coding. Scalable transmission can support selecting required information without going through a decoder in a compressed state (at the bitstream stage), so that it can be identified at the transmitting or receiving end. In the case of scalable decoding, it 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.

[0271] The layer configuration for scalable transmission and the layer configuration for scalable decoding can differ. For example, the three lower octree layers including leaf nodes can constitute a single layer from a scalable transmission perspective. However, from a scalable decoding perspective, if all layer information is included, scalable decoding can be possible for each of the leaf node layer, leaf node layer-1, and leaf node layer-2.

[0272] The encoding / decoding method according to the embodiments can encode and decode point cloud data based on slice level scalability and octree level scalability.

[0273] A layer-group can represent a unit for representing a set of one or more octree layers. A layer-group can represent a bundle of octree layers to be configured in slice units. Spatial scalability by actual octree layers (or scalable attribute layers) can be provided for each octree layer, but when scalability is configured in slice units before bitstream parsing, it can be selected in layer-group units. In Fig. 15, when utilizing slice-unit scalability such as scalable transmission, the provided scalability stages can include three stages of group 1, group 2, and group 3. The scalability stages that can be provided in the decoding stage by the octree structure can include eight stages from the root to the leaf.

[0274] In an embodiment, when groups 1 to 3 are each composed of slices, the receiver or transmitter can 1) select only group 1, 2) select group 1 and group 2, or 3) select group 1, group 2, and group 3.

[0275] 1) If only Group 1 is selected, the maximum octree level is 4, and one scalable layer from octree layers 0 to 4 can be selected during the decoding process. The receiver (decoder) can consider the node size that can be obtained through the maximum octree depth as a leaf node. The node size can then be transmitted to the decoder as signaling information (parameter information).

[0276] 2) When selecting Group 1 and Group 2, Layer 5 is added, so that the maximum octree level becomes 5, and one scalable layer among the 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 can be transmitted to the decoder as signaling information (parameter information).

[0277] 3) When selecting Group 1, Group 2, or Group 3, Layers 6 and 7 are added, so that the maximum octree level becomes 7. One scalable layer among the 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. In addition, the node size can be transmitted to the decoder as signaling information (parameter information).

[0278] The decoding layers for geometry and attributes can be chosen identically or asymmetrically.

[0279] Figure 16 shows partial geometry and partial attributes according to embodiments.

[0280] The encoding method according to the embodiments can encode an attribute based on partial geometry. The decoding method according to the embodiments can decode an attribute based on partial geometry.

[0281] When scalable transport is used, partial decoding of geometry and attributes may be performed, which may result in a difference between the encoded and decoded octree depths. For scalable lifting, which performs LoD generation based on the entire octree depth, a mismatch may occur due to the difference in encoded / decoded octree depths.

[0282] When performing scalable lifting LoD generation for octree depth 4, the first child node is selected for the leaf node level, the last child node is selected for the leaf node level + 1, the first child node is selected for the leaf node level + 2, and so on, alternating between the first and last as the octree depth decreases. The method for selecting a node closer to the center of the upper node has the advantage of improving coding efficiency. Since the entire octree depth is assumed, the following problems may occur in partial geometry situations. Figure 16 illustrates a case where scalable lifting LoD generation is performed in a situation where partial decoding is performed up to octree depth 3 in a situation where encoding is performed for octree depth 4. Scalable lifting LoD generation alternates between selecting the first and last child nodes starting from the leaf node level and working up to the root node level depending on the octree level. Considering the decoded geometry octree depth, the receiver (decoder) selects the first child node from octree level 3 and the last child node from octree level 2 during scalable lifting LoD generation. However, this results in selecting different properties from the scalable lifting LoD generation performed based on octree depth 4 at the time of encoding. This results in decoding a partial point cloud that is different from the encoder's intention.

[0283] As shown in Fig. 16, the encoder can sample the attribute of the first child node among the child nodes belonging to a parent node at depth 4 among the four octree depths (octree layers or levels of level of detail) as the representative attribute of the parent node, and sample the attribute of the last child node among the child nodes belonging to a parent node at depth 3 as the representative attribute of the parent node. The decoder can decode the point cloud data related to octree depths 1 to 3 for partial decoding. It may sample the attribute of the first child node among the child nodes belonging to a parent node at depth 2 as the representative attribute of the parent node, knowing that depth 3 is the last layer. Since the encoder and decoder perform sampling in different orders for depth (layer) 3, an error occurs in the reconstructed point cloud.

[0284] Figure 17 shows partial geometry and partial attributes according to embodiments.

[0285] To address the issue of sampling order mismatch between the encoder and decoder when generating scalable lifting LoD due to partial geometry, the encoder can signal the sampling position to the decoder. The sampling position for each level can be signaled separately. By simultaneously conveying information about the entire octree depth, accurate sampling positions can be used in partial geometry situations. This has the advantage of supporting partial geometry and attributes through signaling without modifying the existing encoder / decoder.

[0286] The decoder can partially decode geometry associated with octree depths (layers or levels of detail) 1 to 3, and partially decode attributes associated with octree depths (layers or levels of detail) 1 to 3. When the encoder encodes attributes for depths 1 to 4, the point cloud data can be compressed by sampling the attribute of the first child node belonging to a parent node at depth 4 as the representative attribute of the parent node. When restoring the compressed point cloud data, the decoder can partially decode the point cloud data up to depths 1 to 3, and when starting decoding at depth 3, sample the attribute of the last child node among the child nodes belonging to a parent node at depth 3 as the representative attribute of the parent node, rather than the first child node. The encoder can transmit the sampling positions for all depths 2 and 1 to the decoder as signaling information (parameter information).

[0287] Figure 18 shows partial geometry and partial attributes according to embodiments.

[0288] The encoding method according to the embodiments can signal a sampling position for octree depth 1. Since partial geometry decoding is a method of discarding consecutive octree levels from the leaf node level, if sampling information for octree depth 1, which is always decoded, is transmitted, subsequent sampling positions can be applied alternately according to the octree depth. For example, if the sampling position for octree depth 1 is the last child node, the first child node and the last child node for the subsequent octree depth can be inferred. There is an advantage that partial geometry and attributes can be supported through signaling without changing the existing encoder / decoder. If the sampling position for depth 1 is the last node, the encoder can transmit information indicating that the sampling position for depth 1 is the last node to the decoder. The decoder can sequentially sample the first node at depth 2 and the last node at depth 3 as attributes of the parent node for depth 2 and depth 3, respectively.

[0289] Figure 19 shows partial geometry and partial attributes according to embodiments.

[0290] The encoding method according to the embodiments can fix the sampling position for octree depth 1. Contrary to the existing method of fixing the sampling position for the leaf node level, by fixing it for octree depth 1, there is an advantage that the scalable LoD generation sampling position does not change even if partial geometry coding occurs.

[0291] As shown in Fig. 19, the sampling position among the child nodes belonging to a parent node at depth (layer or level) 1 can be fixed to the first node. The decoder can sample the first node at depth 1 according to the fixed order, sequentially infer the sampling position at depth 2 to sample the last node, and then infer the sampling position at depth 2 to sample the first node.

[0292] Furthermore, the encoding method according to the embodiments may use fixed sampling locations for all depths. The encoding method according to the embodiments may signal that a specific order of points should be used.

[0293] octree depthencoder (full octree depth 4)decoder (partial octree depth 3)1First (fixed)First (fixed)2First (fixed)First (fixed)3First (fixed)First (fixed)4First (fixed)-

[0294] octree depthencoder (full octree depth 4)decoder (partial octree depth 3)1Last (fixed)Last (fixed)2Last (fixed)Last (fixed)3Last (fixed)Last (fixed)4Last (fixed)-

[0295] As above, the sampling position can be fixed, such as sampling the first child node at each depth, or sampling the last mode at each depth. The encoder can transmit signaling information indicating the use of a fixed position (order) to the decoder.

[0296] In applications such as layer-group slicing or scalable coding, when skip layer groups or skip layers are used, the sampling direction used by the encoder can be inferred to maintain coding efficiency. The missing layer information can be inferred through the layer information, bounding box information, or root node information of the actual decoded / encoded sub-data together with the full layer information, bounding box information, or root node information of the encoder's input point cloud data. The layer can be estimated in the encoder. The used sampling direction can be inferred as (octreeNodeSizeLog2 + rootNodeSizeLog2 - rootNodeSizeLog2_coded).

[0297] if (layer_group_enabled_flag) {

[0298] octreeNodeSizeLog2 = lodIndex;

[0299] direction = (octreeNodeSizeLog2 + rootNodeSizeLog2 - rootNodeSizeLog2_coded) & 1;

[0300] subsampleByOctree(pointCloud, packedVoxel, input, octreeNodeSizeLog2, retained, indexes, direction);

[0301] }

[0302] LoD Index (lodIndex): An index representing the current lod. The index value can increase or decrease sequentially from the upper lod to the lower lod.

[0303] Octree Node Size (octreeNodeSizeLog2): A log2 scale value for the node size of the current lod.

[0304] Root Node Size (rootNodeSizeLog2): A log2 scale value for the size of the node or bounding box that covers the entire input point cloud data of the encoder.

[0305] Coded root node size (rootNodeSizeLog2_coded): A log2 scale value for the size of the node or bounding box that covers the actual coded point cloud data.

[0306] Figure 20 illustrates a bitstream including point cloud data and parameters according to embodiments.

[0307] The encoding method according to the embodiments can transmit information related to a separated slice according to the embodiments by including it in a bitstream. A parameter set and / or an SEI message in the bitstream can include information related to the separated slice. A sequence parameter set (SPS), a geometry parameter set (GPS), an attribute parameter set (APS) and / or a geometry slice header (GSH) and / or an attribute slice header (ASH) in the bitstream can include information related to the separated slice. Depending on the application or system, information related to the separated slice can be defined at a corresponding location in the bitstream or at a separate location, and the scope of application, application method, etc. can be used differently. Depending on where the signal is transmitted within the bitstream, it can have different meanings. If defined in SPS, it can be applied equally to the entire sequence. If defined in GPS, it can indicate that it is used for position recovery. If defined in APS, it can indicate that it is applied to attribute recovery. If defined in TPS, it can indicate that the signaling is applied only to points within a tile. If 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, it can be defined in a corresponding location or a separate location so that the application scope, application method, etc. can be used differently. In addition, if the syntax element defined below can be applied not only to the current point cloud data stream but also to multiple point cloud data streams, information related to separated slices can be transmitted through a higher-level concept of parameter sets, etc.

[0308] Each abbreviation stands for the following. Each abbreviation may be referred to by other terms within the same meaning: 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: Attribute bitstream = attribute blick header + attribute brick data.

[0309] A slice contains point cloud data and represents a unit of encoding and decoding. A slice may be referred to as a data unit. A slice represents the geometry and attributes of part or all of an encoded point cloud frame. A slice may be referred to as a brick. A data unit represents a sequence of bytes that conveys a syntax structure of a fixed length.

[0310] The embodiments define the information described below independently of the coding technique. Furthermore, the information described below can be defined in conjunction with the coding method and defined in a tile parameter set to support regionally varying scalability. Furthermore, if the syntax elements defined below can be applied to multiple point cloud data streams, not just the current point cloud data stream, they can be conveyed through a higher-level parameter set, etc.

[0311] The encoding method according to the embodiments can define a NAL (Network abstract layer) unit and define related information for selecting a layer, such as a layer ID (layer_id), within the bitstream and transmit it to the decoder. The decoding method according to the embodiments can select and decode a bitstream at the system level.

[0312] Parameters according to the embodiments (which may be called various names such as metadata, signaling information, etc.) may be generated in the process of the transmitter according to the embodiments described below, and may be transmitted to the receiver according to the embodiments and used in the reconstruction process.

[0313] 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.

[0314] Figure 21 shows scalable lifting LoD generation information according to embodiments.

[0315] Figure 22 shows a set of attribute parameters according to embodiments.

[0316] Figures 21 and 22 illustrate the syntax of parameter information included in the bitstream of Figure 20. The information of Figures 21 and 22 can be generated by an encoding device and decoded by a decoding device.

[0317] Top-down LoD generation flag (top_down_LoD_generation_flag): If this value is 1, when sampling attributes from child nodes for scalable lifting LoD generation, the sampling location is alternately changed in the top-down direction based on the initial sampling depth (e.g., octree depth (depth of the accumulation tree) 1). If this value is 0, when sampling attributes from child nodes for scalable lifting LoD generation, the sampling location is alternately changed in the bottom-up direction based on the leaf node level. The standard for determining the sampling position can be changed from the bottom to the top. Scalable lifting LoD generation can be performed bottom-up.

[0318] First depth sampling direction present flag (first_depth_sampling_direction_present_flag): If this value is 1, it can indicate that the sampling position is explicitly passed when sampling attributes from child nodes for the reference octree depth (e.g., octree depth 1). If this value is 0, it can indicate that the sampling position is implicitly passed when sampling attributes from child nodes. For example, either the first child node or the last child node can be the sampling position.

[0319] First Depth Sampling Direction Forward Flag (first_depth_sampling_direction_forward_flag): If the value is 1, it can indicate that sampling is performed in the forward direction when sampling attributes from child nodes. That is, it can indicate that the first node among child nodes is sampled for the first depth of sampling. If this value is 0, it can indicate that sampling is performed in the reverse direction when sampling attributes from child nodes. That is, it can indicate that the last node among child nodes is sampled for the first depth of sampling.

[0320] Number of full octree depths (num_full_octree_depth): This can indicate the octree depths considered in encoding. If top_down_LoD_generation_flag is 0, the sampling direction of scalable lifting LoD generation is determined in the bottom-up direction, so the decoder can use num_full_octree_depth, which indicates the octree depths considered in encoding, rather than the decoded geometry depth, as a criterion for determining the directionality at each octree depth.

[0321] child_node_sampling_forward_direction_flag): If this value is 1, it can indicate that the sampling position is explicitly forwarded when sampling attributes from child nodes for each octree depth. If it is 0, it can indicate that the sampling position is implicitly forwarded when sampling attributes from child nodes. For example, the sampling position can be either the first child node or the last child node.

[0322] Child Node Sampling Position (child_node_sampling_position): This allows you to directly signal the sampling position at each octree level. A value of 0 indicates the first node, while a value of 1 indicates the last node. A value between 2 and 7 indicates that the first to sixth nodes (or the nearest adjacent nodes) are used. For example, a value between 2 and 7 indicates a node located between the first and last nodes.

[0323] lifting_scalability_enabled_flag: If this value is 1, it indicates that the attribute decoding process allows pruned octree decoding results for the input geometry points. If lifting_scalability_enabled_flag is 0, it indicates that the attribute decoding process requires complete octree decoding results for the input geometry points. If this flag is not present, the value of this flag is inferred to be 0. If trisoup_enabled_flag is 0, the value of lifting_scalability_enabled_flag is 0. If geom_tree_coded_axis_list_present_flag is 1, lifting_scalability_enabled_flag is 0.

[0324] If the geometry scaling enable flag (geom_scaling_enabled_flag) is 1, lifting_scalability_enabled_flag is 0 to ensure partial decoding of the geometry point cloud.

[0325] Lifting nearest neighbor range maximum (lifting_max_nn_range_minus1): If this value is 1, it indicates the nearest neighbor maximum range used to limit the distance of points registered as neighbors. The value of lifting_max_nn_range is the number of octree nodes surrounding the point.

[0326] Fixed sampling direction flag (fixed_sampling_direction_forward_flag): If this value is 1, it indicates that a sampling method is used that selects points in a fixed order for the points included in the sampling unit.

[0327] Sampling direction type (sampling_direction_type): This can indicate the sampling method or point order. 0 indicates that the first point in the Morton code sorted points is used, 1 indicates that the last point in the Morton code sorted points is used, 2 indicates that the second point is used, and 3 indicates that the third point is used.

[0328] inferred_sampling_direction_enabled_flag: If this value is 1, it can indicate that the method of inferring the sampling direction is used.

[0329] Root Node Size (rootNodeSizeLog2): This can represent a log2 scale value for the size of the node or bounding box that covers the entire input point cloud data of the encoder.

[0330] Coded root node size (rootNodeSizeLog2_coded): A log2 scale value representing the size of the node or bounding box covering the actual coded point cloud data. The encoder can signal the coded root node size. Alternatively, the root node size can be inferred internally within the encoder or decoder based on the decoded geometry point cloud data.

[0331] The encoder and / or decoder can infer the number of lost layers via rootNodeSizeLog2 - rootNodeSizeLog2_coded and, if necessary, signal the number of lost layers directly as parameter information.

[0332] Fig. 23 shows an encoder (transmitter) according to embodiments.

[0333] Fig. 23 corresponds 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 Figs. 11 to 13, the partial encoding of Figs. 14 to 19, the bitstream and parameter generation of Figs. 20 to 22 and Figs. 29 to 30, the encoding of Figs. 23 to 28, and the encoding method of Fig. 31.

[0334] Below, the components of the transmitter and receiver of the present invention are described. Each component may correspond to a processor, software, or hardware. Furthermore, the components below may be combined with the PCC transmitter and receiver structure and / or signaling information. Each encoder (encoding device) and decoder (decoding device) may be configured with memory and a processor.

[0335] 1) Encoder (encoding device):

[0336] The encoder acquires point cloud data as input. The encoder can encode position information (geometric data (e.g., XYZ coordinates, phi-theta coordinates, etc.)) and attribute information (attribute data (e.g., color, reflectance, intensity, grayscale, opacity, medium, material, glossiness, etc.)) respectively. The compressed data can be divided into units for transmission. The sub-bitstream generator can select the necessary information from the bitstream unit according to the layering structure information and pack it into appropriate units.

[0337] Fig. 24 shows a decoder (receiving device) according to embodiments.

[0338] FIG. 24 corresponds 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 to FIG. 13, partial decoding of FIG. 14 to FIG. 19, bitstream and parameter parsing of FIG. 20 to FIG. 22 and FIG. 29 to FIG. 30, decoding of FIG. 23 to FIG. 28, decoding method of FIG. 32, etc.

[0339] The decoder receives a bitstream as input from the encoder. The decoder can distinguish and process the bitstream for position information and the bitstream for attribute information. At this time, the sub-bitstream classifier can pass the information in the bitstream header to the appropriate decoder. Alternatively, the receiver can select the layer it needs during this process. The classified bitstream can be used to restore the geometry data and attribute data, respectively, in the geometry decoder and attribute decoder according to the characteristics of the data, and then converted into a format for final output by the renderer.

[0340] Figure 25 illustrates scalable lifting according to embodiments.

[0341] A decoding device according to embodiments can decode point cloud data based on scalable lifting, as shown in FIG. 25. When slices are configured in layers for scalable transmission, scalable lifting according to metadata can be performed, as shown in FIG. 25. When scalable lifting is used, signaling information of scalable lifting LoD generation information (scalable_lifting_lod_generation_info()) transmitted through an SEI message or APS according to embodiments can be used. If the top-down LoD generation flag is 1, the sampling direction of the top level can be explicitly signaled through first_depth_sampling_direction_forward_flag (when first_depth_sampling_direction_present_flag = 1), or the sampling direction of the top level defined in the standard document can be used (when first_depth_sampling_direction_present_flag = 0). If top-down LoD generation is not used, the sampling positions can be estimated in the bottom-up direction using the octree depth information used during encoding (num_full_octree_depth), or the positions can be specified using the sampling positions for each octree depth given through child_node_sampling_forward_dicrection_flag or child_node_sampling_position.

[0342] The decryption method according to the embodiments estimates the sampling position in the bottom-up direction if the scalable lifting enable is true and the top-down LoD generation is false. If the top-down LoD generation is true and the sampling direction existence is false, the sampling position is estimated in the top-down direction. If the sampling direction existence is true, the sampling position is estimated in the top-down direction.

[0343] Figure 26 illustrates subgroup LoD sampling considering missing layers according to embodiments.

[0344] The method according to the embodiments can change the LoD sampling direction for each layer in attribute layer-group slicing. In each layer, child nodes are arranged in Morton code order, and the attributes of the first or last node can be selected (sampled) as the attributes of the parent node according to the octree hierarchy.

[0345] When the total number of octree depths is passed to the decoder, the decoder can sample points (nodes) in the octree in the same direction as the LoD sampling of the encoder. However, when a subset of the coded bitstream is decoded, the sampling direction of the bottom layer is fixed to the forward direction, and the sampling direction (first or last node) may vary depending on the encoding layer, so the sampling direction of the decoder may not be the same as that of the encoder.

[0346] To address this issue, the method according to the embodiments can compensate for the number of missing layers in the subgroup LoD generation as follows:

[0347] Number of missing layers = maximum depth of encoded LoD - maximum depth of the current subgroup LoD

[0348] The method according to the embodiments can calculate the number of missing layers through the difference between the maximum depth value of the LoD encoded by the encoder and the maximum depth value of the current subgroup LoD.

[0349] Additionally, the number of missing layers can be derived from two parameters.

[0350] Root node size (rootNodeSizeLog2) is a parameter that indicates the maximum depth of the encoded LoD.

[0351] The encoded root node size (rootNodeSizeLog2_coded) is a derived value representing the maximum depth of the current subgroup LoD.

[0352] These parameters can be used to compensate for the octree node size (octreeNodeSizeLog2) in determining the LoD sampling direction.

[0353] if (layer_group_enabled_flag) {

[0354] int32_t octreeNodeSizeLog2 = lodIndex;

[0355] bool direction = (octreeNodeSizeLog2 + (rootNodeSizeLog2 - rootNodeSizeLog2_coded)) & 1;

[0356] subsampleByOctree(pointCloud, packedVoxel, input, octreeNodeSizeLog2, retained, indexes, direction);

[0357] }

[0358] The sampling direction can be calculated based on the octree node size (octreeNodeSizeLog2), root node size (rootNodeSizeLog2), and coded tree node size (rootNodeSizeLog2_coded).

[0359] OctreeNodeSizeLog2 can be the maximum depth value of LoD.

[0360] Based on this, the octreeNodeSizeLog2 parameter can be used as follows:

[0361] if (layer_group_enabled_flag) {

[0362] int32_t octreeNodeSizeLog2 = lodIndex;

[0363] bool direction = octreeNodeSizeLog2 & 1; / octreeNodeSizeLog2 = maxDepth - endDepth[layerId]

[0364] / maxDepth += sps.num_layers_minus1[layerGroupId]+1;

[0365] subsampleByOctree(pointCloud, packedVoxel, input, octreeNodeSizeLog2, retained, indexes, direction);

[0366] }

[0367] You can calculate the maximum number of layers (max.layer) using a method such as maxDepth += sps.num_layers_minus1[layerGroupId]+1.

[0368] The two methods described above for generating subgroup LoDs are identical in their methods for deriving the LoD sampling direction. However, the maximum depth value of the encoded LoD may be derived differently. One implementation derives the value using defined parameters for the number of layers in each layer group, while the other signals this value as explicit parameter information.

[0369] Figure 27 illustrates FGS-related encoder and decoder use cases according to embodiments.

[0370] Figure 28 illustrates FGS-related encoder and decoder use cases according to embodiments.

[0371] In progressive decoding and partial decoding, the target application can be considered a single decoder. Fine-grained slicing (FGS) according to embodiments can be used to support multiple decoders with different capabilities due to memory, clock speed, display devices, etc.

[0372] For example, if we illustrate decoders A, B, and C with different resolution capabilities, one way to support them is to generate different sets of bitstreams based on the target device requirements. As shown in Figure 27(b), an efficient approach is to select fine-grained slices and generate bitstreams with fine-grained slicing that can support the three different decoders.

[0373] For example, an encoder can select a sublayer group of a bitstream based on a decoder's request. If fine-grained slicing is not used, another bitstream must be generated by another encoder and optionally stored or transmitted. As shown in Figure 28(b), a single bitstream with fine-grained slicing can support multiple decoders without requiring additional encoders or storage.

[0374] To support these use cases, a granular slicing encoder can generate a different parameter set for each target device. This is used to verify decoder capabilities and configure decoder parameters. In this case, the encoder or transcoder can set different parameter values ​​for each target device for a subset of the bitstream when the syntax definition is unclear.

[0375] If necessary, multiple parameter sets, such as SPS, GPS, and APS, can be generated for different target devices, and each parameter set can provide criteria for selecting a different level of bitstream subset that can be supported in the bitstream.

[0376] For example, the number of layer groups (num_layer_group_minus1) can indicate the maximum depth that the target device must support. Since the number of layer groups (num_layer_group_minus1) has different values ​​for decoders A, B, and C, the bitstreams that can be processed by each decoder can be selectively used.

[0377] For example, it is possible to determine whether a final displayable subset is present based on the number of points in a subgroup or layer group (in subgroup / in layer-group).

[0378] Referring to Fig. 27(a), in the case of a multi-encoder and multi-decoder without FGS, the multi-encoder downsamples and encodes the entire point cloud data, or encodes the entire point cloud data and transmits bitstreams having different sizes, and the multi-decoder can decode point cloud data of low resolution and high resolution, respectively.

[0379] Referring to Fig. 27(b), there is a single encoder and multi-decoder based on FGS, and when the single encoder encodes and transmits point cloud data into multiple FGSs through layer group slicing, the multi-decoder can decode low-resolution or high-resolution point cloud data based on FGS.

[0380] Referring to Fig. 28(a), there is a multi-encoder and multi-decoder without FGS, and the multi-encoder can downsample the entire point cloud data to encode partial point cloud data or encode the entire point cloud data. The slice selector can select a low-resolution or high-resolution slice, transcode the selected slice, and transmit it to the multi-decoder. The multi-decoder can decode the point cloud data within the received low-resolution or high-resolution slice.

[0381] Referring to FIG. 28(b), there is a single encoder and a multi-decoder with FGS, the single encoder encodes the entire point cloud data based on layer group slices, transcodes low-resolution to high-resolution FSGs through a slice selector and transmits them to the decoder, and the multi-decoder can decode the point cloud data within the low-resolution to high-resolution FSGs.

[0382] Considering the use cases of Figures 27 and 28, two possible examples of maximum depth of encoded LoD are described below.

[0383] Figure 29 shows a sequence parameter set according to embodiments.

[0384] The encoding method according to the embodiments can generate a sequence parameter set of Fig. 29 and include it in a bitstream. The decoding method according to the embodiments can decode point cloud data based on information in the sequence parameter set of Fig. 29.

[0385] As shown in FIG. 29, the method according to the embodiments can generate and transmit FGS-related parameter information through explicit signaling.

[0386] In an explicit way, the root subgroup bounding box size (root_subgroup_bbox_size), which represents the maximum depth of the LoD in the encoder, can be passed from the SPS.

[0387] The root node size (root_node_size_log2) represents the log 2 value of the cubic root node size of the encoded octree.

[0388] Root node size (root_node_size_log2) can mean the number of encoded tree layers from the root to the leaf layer.

[0389] In fine-grained slicing, the number of missing layers in a subgroup can be calculated based on the difference between the root_node_size_log2 and the maximum depth of the current subgroup. The number of missing layers in a subgroup is used to derive the sampling direction in subgroup LoD generation. The number of missing layers in a subgroup is also used to compensate for the approximate positions of nodes in intermediate layers.

[0390] Figure 30 shows a sequence parameter set according to embodiments.

[0391] The method according to the embodiments may further add descriptive information within the bitstream, for example within the sequence parameter set.

[0392] For example, one could add constraints to the maximum depth of the encoded LoD and the associated signaling information. Considering that EE SW uses num_layers_minus1 to derive the maximum depth of the encoded LoD, the following semantics could be added:

[0393] Number of Layer Groups (num_layer_groups_minus1): If this value is 1, it indicates 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. num_layer_groups_minus1 is the encoder's number of layer groups.

[0394] Number of layers (num_layers_minus1): Adding 1 to this value indicates the number of coded layers included in the ith layer group. The maximum number of coded layers in the current frame is calculated by accumulating num_layers_minus1[i] + 1 for all i in the range from 0 to num_layer_groups_minus1.

[0395] The maximum number of layers derived from num_layers_minus1 is the number of encoded tree layers from the root to the leaf layers.

[0396] In fine-grained slicing, the number of missing layers in a subgroup can be the difference between the maximum number of layers and the maximum depth of the current subgroup. The number of missing layers in a subgroup is used to derive the sampling direction for subgroup LoD generation. The number of missing layers in a subgroup is also used to compensate for the approximate locations of nodes in intermediate layers.

[0397] The number of layer groups (num_layer_groups_minus1) may change depending on the number of receivers when the transcoder supports various receivers.

[0398] Figure 31 shows a sequence parameter set according to embodiments.

[0399] The method according to the embodiments may add signaling information (parameter information) within the Sequence Parameter Set (SPS) level to explicitly indicate the depth of an encoded accommodative tree, to avoid syntax misuse in a single bitstream multi-decoder use case. Since the relevant signaling information is used in the decoding process of a fine-grained slice (FGS), this signaling information (parameter information) may not be changed when generating a parameter set to support various functions of the decoder.

[0400] An occupancy tree according to embodiments may be referred to as an octree, and may be referred to as a geometry tree as a tree relating to geometry data. Depending on the type of the geometry tree, the geometry tree may include an occupancy tree or a prediction tree. When encoding and decoding point cloud data according to embodiments, the geometry data may be encoded / decoded based on an FGS matching a layer group and a subgroup of a layer group of a geometry tree of the geometry data. Similarly, attribute data may be encoded / decoded based on an FGS matching a layer group and a subgroup of a layer group of an attribute tree matching the geometry tree. An attribute tree is a tree aligned with a geometry tree structure and refers to a tree having attribute data corresponding to points. The depth of the tree may be referred to as a layer, a level, etc.

[0401] Additionally, the method according to the embodiments can indicate the actual depth of the coded occupancy tree of the geometry data unit as parameter information in the geometry data unit header. This can prevent information inconsistency when only the first FGS (fine granularity slice) is passed to the decoder (see decoder A illustrated in FIGS. 27 and 28).

[0402] Below, we describe proposed syntax information for the Sequence Parameter Set (SPS) and the Geometry Data Unit header.

[0403] Information about the root node size of the encoded occupancy tree can be transmitted as signaling information from the encoder to the decoder. Explicitly, the root node size (root_node_size_log2_minus1) can be transmitted as signaling information in the SPS. The root node size represents the maximum depth of the LoD in the encoder.

[0404] Root node size (root_node_size_log2_minus1): Adding 1 to this value represents the log 2 value of the cubic root node size of the encoded octree.

[0405] Root node size (root_node_size_log2_minus1): Adding 1 to this value gives the number of encoded tree layers from the root to the leaf layer.

[0406] In FGS (Fine Granularity Slicing), the number of missing layers in a subgroup is the difference between the root_node_size_log2_minus1 plus 1 and the maximum depth generated in the subgroup. The number of missing layers in a subgroup is used to derive the sampling direction in the subgroup LoD generation. The number of missing layers in a subgroup is used to compensate for the geometric positions of nodes in intermediate layers.

[0407] Figure 32 shows a decoding process according to embodiments.

[0408] The root node size (root_node_size_log2_minus1) can be used to represent the cubic bounding box of the encoded point cloud data. The root node size can be used to determine the point density of the decoded output data. Since the entire position resolution cannot be expressed when the intermediate subgroup is decoded, the position resolution can be adjusted through a shift operation based on the value of the minimum geometry node size (minGeomNodeSizeLog2) after the subgroup decoding, as shown in Fig. 32. In this case, the minimum geometry node size (minGeomNodeSizeLog2) can mean the number of missing layers mentioned above.

[0409] Figure 33 illustrates a geometry data unit header (geometry_data_unit_header) according to embodiments.

[0410] Information about the depth of a partially coded occupancy tree can be transmitted from an encoder to a decoder as signaling information. The method according to embodiments can indicate the depth of the occupancy tree in the first FGS (fine-grained slice) using the octree depth (occtree_depth_minus1) in the geometry data unit header by signaling the maximum depth of the occupancy tree of the encoded point cloud in the SPS or the like. The tree of the corresponding data unit can be described in the geometry data unit header, and confusion that may occur when the decoder receives only the first slice of the fine-grained slice can be prevented.

[0411] To this end, the meaning of the octree depth (occtree_depth_minus1) for the layer group slicing case can be defined as follows.

[0412] Octree Depth (occtree_depth_minus1): Adding 1 to this value indicates the maximum number of tree levels that can exist in the coded occupancy tree. If the octree coded axis list present (occtree_coded_axis_list_present) is 0, the root node size is a cubic volume with edge lengths equal to Exp2(occtreeMaxDepthMinus1 + trisoupNodeSizeLog2 + 1).

[0413] The number of coded tree levels is equal to the octree maximum depth (occtreeMaxDepthMinus1) plus 1. When the trisoup enable flag (trisoup_enable_flag) is 0, the value of occtreeMaxDepthMinus1 is set equal to occtree_depth_minus1, and when trisoup_enable_flag is 1, the value of occtreeMaxDepthMinus1 is set to: occtreeMaxDepthMinus1 = occtree_depth_minus1 - 1.

[0414] If trisoup_enable_flag is 1, occupancy tree coding is performed until the node size trisoupNodeSizeLog2 is reached, not the unit cube size. The TriSoup process is performed for each node whose node size is trisoupNodeSizeLog2.

[0415] When trisoup_enable_flag is 1, the trisoupNodeSizeLog2 value is set to trisoup_node_size_log2_minus2 + 2. Otherwise, if trisoup_enable_flag is equal to 0, the trisoupNodeSizeLog2 value is set to 0.

[0416] If the layer group enabled flag (layer_group_enabled_flag) is equal to 1 (if FGS is applied), then occtree_depth_minus1 plus 1 indicates the maximum number of tree levels of partially coded occupancy trees in the first fine-grained slice. In this case, occtreeMaxDepthMinus1 indicates that the maximum number of coded occupancy tree levels by decoding subsequent fine-grained slices depends on the current data unit. The value of occtreeMaxDepthMinus1 is set to: occtreeMaxDepthMinus1 = root_node_size_log2_minus1.

[0417] occtree_depth_minus1 can indicate the partial depth of the current GDU. The encoder can generate separate signaling information for the partial depth of the GDU for FGS and transmit it to the decoder. For example, an additional element such as fgs_occtree_depth_minus1 can be defined at the GDU level instead of occtree_depth_minus1. The GDU can include a layer group enable flag (layer_group_enabled_flag), and if the layer group enable flag is true, fgs_occtree_depth_minus1 can be passed at a lower level of the GDU.

[0418] Figure 34 shows a geometry data unit (geometry_data_unit) according to embodiments.

[0419] Among the geometry parameters transmitted within a GDU (geometry data unit), parameters with different values ​​depending on the depth may include accuracy tree information for the first slice (the first FGS (fine granularity slice)).

[0420] Figure 34 shows the form of a geometry data unit that changes due to fine granularity slicing (FGS), and the depth range of the occupancy tree contained in each data unit can be indicated by the input start depth (startDepth) and end depth (endDepth). The meaning of the start depth and end depth may change depending on FGS.

[0421] As in Fig. 34, depending on whether the geometry tree type is an acknowledgment tree or a prediction tree, a geometry tree corresponding to the GDU can be defined within the GDU based on the start depth and end depth of the FSG.

[0422] Figure 35 illustrates a geometry data unit header (geometry_data_unit_header) according to embodiments.

[0423] When the encoding / decoding method according to the embodiments encodes and decodes point cloud data based on fine granularity slicing (FGS), the octree coded axis (occtree_coded_axis) that indicates whether each axis of the occupancy tree is split may include only the depth of the occupancy tree belonging to the current subgroup rather than transmitting values ​​for the entire encoded depth (occtreeMaxDepthMinus1).

[0424] The header of the geometry data unit according to the embodiments may transmit occtree_coded_axis information for the occupancy tree having the start depth and end depth to which the current subgroup belongs, as shown in FIG. 35.

[0425] Figure 36 illustrates a dependent geometry data unit header (dependent_geometry_data_unit_header) according to embodiments.

[0426] As in Fig. 35, when information related to an occupancy tree for a subgroup is transmitted in a geometry data unit header, a dependent geometry data unit header can also transmit information about an occupancy tree belonging to the subgroup. The start depth (startDepth), end depth (endDepth), and number of layers (numLayer) for the occupancy tree for the subgroup can be transmitted to the decoder based on layer-group structure information transmitted through another path, or can be combined with information directly transmitted through the data unit header, as in Fig. 36. The end depth can be calculated as endDepth = startDepth + numLayers.

[0427] Figure 37 shows an occupancy tree (occupancy_tree) according to embodiments.

[0428] The method according to the embodiments may comprise a geometry occupancy tree comprising information between a start depth (startDepth) and an end depth (endDepth).

[0429] Figure 38 illustrates a geometry data unit footer (geometry_data_unit_footer) according to embodiments.

[0430] The geometry data footer according to the embodiments may include the number of points (occtree_lvl_point_cnt_minus1) per depth (level of the occupancy tree). The starting slice (the first fine granularity slice (fgs), the root FGS) may include information on the number of points per layer for the entire encoded occupancy tree, and may include the number of points per layer for the startDepth and endDepth ranges of the occupancy tree included in the corresponding data unit, as shown in FIG. 38, or may have both pieces of information. The dependent data unit may include the number of points per layer for the startDepth and endDepth ranges of the occupancy tree included in the corresponding data unit.

[0431] Figure 39 shows an encoding method according to embodiments.

[0432] The encoding method according to the embodiments of FIG. 39 may include operations such as 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 FIGS. 11 to 13, the partial encoding of FIGS. 14 to 19, the bitstream and parameter generation of FIGS. 20 to 22 and FIGS. 29 to 30, and the parameter information generation of FIGS. 31 to 38.

[0433] The encoding method according to the embodiments may include a step of encoding point cloud data (S3800) and / or a step of transmitting a bitstream including point cloud data (S3810).

[0434] Referring to FIG. 15 together, the step of encoding point cloud data (S3800) includes a step of encoding geometry data of point cloud data, a step of encoding attribute data of point cloud data, and the geometry data is expressed based on a tree structure (such as an acknowledgment tree or a prediction tree), and the geometry data is encoded based on a layer group including at least one layer of the tree structure and a fine granularity slice (FGS) matching a subgroup of the layer group, and the attribute data is expressed based on a tree structure (a tree having attribute data aligned to a geometry tree), and the attribute data can be encoded based on a layer group including at least one layer of the tree structure and a fine granularity slice (FGS) matching a subgroup of the layer group.

[0435] Embodiments further include a computer-readable storage medium storing a bitstream generated by the method of FIG. 39.

[0436] Embodiments further include a method comprising: obtaining a bitstream for point cloud data, the bitstream being generated based on: encoding geometry data of the point cloud data; encoding attribute data of the point cloud data; and transmitting data including the bitstream.

[0437] Figure 40 shows a decryption method according to embodiments.

[0438] The decoding method according to the embodiments may include operations such as 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 FIGS. 11 to 13, the partial decoding of FIGS. 14 to 19, the bitstream and parameter parsing of FIGS. 20 to 22 and FIGS. 29 to 30, the decoding of FIGS. 23 to 28, and the parameter information parsing of FIGS. 31 to 38.

[0439] A decryption method according to embodiments may include a step of receiving a bitstream including point cloud data (S3900) and / or a step of decoding point cloud data (S3910).

[0440] Referring to FIG. 15 together, in relation to FGS-based partial decoding, a step (S3910) for decoding point cloud data includes a step for decoding geometry data of point cloud data, a step for decoding attribute data of point cloud data, and the geometry data is expressed based on a tree structure (such as an acknowledgment tree or a prediction tree), and the geometry data is decoded based on a layer group including at least one layer of the tree structure and a FGS (Fine granularity slice) matching a subgroup of the layer group, and the attribute data is expressed based on a tree structure, and the attribute data is decoded based on a layer group including at least one layer of the tree structure (a tree having attribute data aligned to a geometry tree) and a FGS (Fine granularity slice) matching a subgroup of the layer group.

[0441] Referring to FIG. 31, with respect to root_node_size_log2_minus1 of SPS, the bitstream includes a sequence parameter set, and the sequence parameter set includes information about the size of the root node of the geometry tree for the geometry data of the point cloud data, and the information about the size of the root node can indicate the number of layers including the leaf layers from the root layer of the geometry tree.

[0442] Referring to FIG. 33, with respect to occtree_depth_minus1 of the GDU header, the bitstream includes a geometry data unit regarding geometry data of point cloud data, and the header of the geometry data unit may include information regarding the maximum number of levels of a geometry tree regarding the geometry data based on layer group enable information.

[0443] Referring to FIG. 34, with respect to occupancy_tree(occtreeMaxDepthMinus1, startDepth, endDepth) of GDU, the bitstream includes a geometry data unit regarding geometry data of point cloud data, and the geometry data unit may include information related to the geometry tree regarding the start depth (startDepth) and end depth (endDepth) regarding a layer group of the geometry data.

[0444] Referring to FIG. 35 together, with respect to occtree_depth_minus1 and occtree_coded_axis[dpth][] of the GDU header, the bitstream includes a geometry data unit regarding geometry data of point cloud data, and the header of the geometry data unit may include information indicating whether the axis of the geometry tree regarding the geometry data is split based on the depth to which a subgroup of a layer group of the geometry data belongs.

[0445] Referring to FIG. 36 together, with respect to startDepth, numLayers, startDepth, endDepth, occtree_coded_axis[dpth][] of the dependent GDU header, the bitstream includes a dependent geometry data unit regarding geometry data of point cloud data, and the header of the dependent geometry data unit includes at least one of a start depth to which a subgroup of a layer group of the geometry data belongs or the number of layers belonging to the subgroup, and may include information indicating whether the axis of the geometry tree regarding the geometry data for the start depth and end depth to which the subgroup belongs is divided.

[0446] Referring to FIG. 37, with respect to occupancy_tree(occtreeMaxDepthMinus1, startDepth, endDepth) of the occupancy tree, the bitstream includes an occupancy tree for geometry data of point cloud data, and the occupancy tree may include information about the occupancy tree for the start depth and end depth to which the subgroup belongs.

[0447] Referring to FIG. 38, with respect to geometry_data_unit_footer(startDepth, endDepth), occtree_lvl_point_cnt_minus1, the bitstream includes a geometry data unit footer regarding geometry data of point cloud data, and the geometry data unit footer may include information indicating the number of points belonging to layers for the start depth and end depth to which the subgroup belongs.

[0448] The decryption method of FIG. 40 may be performed by a decryption device. The decryption device may include a memory; and at least one processor connected to the memory; wherein the at least one processor may be configured to: receive a bitstream including point cloud data; and decode the point cloud data.

[0449] The method and device according to the embodiments provide the following technical effects.

[0450] The LoD generation used in scalable lifting assumes full octree depth geometry and alternates sampling locations according to the octree depth, thereby achieving compression efficiency by utilizing attributes located closer to the node center. When scalable transmission is supported, partial geometry is supported. In this case, the sampling locations of the scalable lifting LoD generation may not match between the encoder and decoder, which may cause inaccurate representation of the partial point cloud. The mismatch between geometry and attributes can be resolved in the case of partial decoding by changing the sampling reference location or explicitly signaling it.

[0451] In embodiments, when encoding and decoding point cloud data based on FGS, by transmitting information about FGS in data units for geometry / attributes and dependent data units, the decoder can decode point cloud data based on a layer group structure. Since FGS does not include the entire depth of the tree, but is expressed as partial depths / layers / levels, FGS can be signaled to the decoder through data units and occupancy tree information in various ways.

[0452] The point cloud data transmission / reception method / device according to the embodiments can scalably encode and / or decode point cloud data by considering partial geometry, and has the effect of efficiently compressing and restoring point cloud data based on operation and / or related signaling information according to the embodiments.

[0453] 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.

[0454] 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.

[0455] 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.

[0456] The embodiments have been described in terms of methods and / or devices, and the descriptions of methods and devices may be applied complementarily.

[0457] 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.

[0458] 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.

[0459] 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”.

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] As described above, the relevant contents have been described in the best form for carrying out the embodiments.

[0466] As described above, the embodiments may be applied in whole or in part to a point cloud data transmission and reception device and system.

[0467] Those skilled in the art may make various changes or modifications to the embodiments within the scope of the embodiments.

[0468] Embodiments may include modifications / changes, which do not depart from the scope of the claims and their equivalents.

Claims

1. A step of receiving a bitstream containing point cloud data; and A step of decoding the above point cloud data; comprising; How to decrypt.

2. In paragraph 1, The step of decoding the above point cloud data is A step of decoding geometry data of the above point cloud data, A step of decoding attribute data of the above point cloud data, comprising: The above geometry data is expressed based on a tree structure, and the geometry data is decoded based on a layer group including at least one layer of the tree structure and a fine granularity slice (FGS) matching a subgroup of the layer group. The above attribute data is expressed based on a tree structure, and the attribute data is decoded based on a layer group including at least one layer of the tree structure and a fine granularity slice (FGS) matching a subgroup of the layer group. How to decrypt.

3. In paragraph 1, The above bitstream includes a set of sequence parameters, The above sequence parameter set includes information about the size of the root node of the geometry tree for the geometry data of the point cloud data. Information about the size of the root node indicates the number of layers including the root layer and leaf layers of the geometry tree. How to decrypt.

4. In paragraph 1, The above bitstream includes a geometry data unit regarding geometry data of the point cloud data, The header of the above geometry data unit includes information related to the maximum number of levels of the geometry tree for the geometry data, based on layer group enable information. How to decrypt.

5. In paragraph 1, The above bitstream includes a geometry data unit regarding geometry data of the point cloud data, The above geometry data unit includes information related to a geometry tree for a start depth (startDepth) and an end depth (endDepth) for a layer group of the geometry data. How to decrypt.

6. In paragraph 1, The above bitstream includes a geometry data unit regarding geometry data of the point cloud data, The header of the geometry data unit includes information indicating whether the geometry tree for the geometry data is axis-divided based on the depth to which the subgroup of the layer group of the geometry data belongs. How to decrypt.

7. In paragraph 1, The above bitstream includes a dependent geometry data unit regarding geometry data of the point cloud data, The header of the above dependent geometry data unit includes at least one of a start depth to which a subgroup of the layer group of the above geometry data belongs or the number of layers belonging to the subgroup, Including information indicating whether the geometry tree is axis-split for the geometry data for the start depth and end depth to which the subgroup belongs. How to decrypt.

8. In paragraph 1, The above bitstream includes an occupancy tree regarding geometry data of the point cloud data, The above occupancy tree includes information about the occupancy tree for the start depth and end depth to which the subgroup belongs. How to decrypt.

9. In paragraph 1, The above bitstream includes a geometry data unit footer regarding geometry data of the point cloud data, The above geometry data unit footer contains information indicating the number of points belonging to the layers for the start depth and end depth to which the subgroup belongs. How to decrypt.

10. Memory; and At least one processor connected to the memory; wherein the at least one processor comprises: Receive a bitstream containing point cloud data; and configured to decode the above point cloud data; Decryption device.

11. In paragraph 10, The above processor Decoding the geometry data of the above point cloud data, It is configured to decode attribute data of the above point cloud data, The above geometry data is expressed based on a tree structure, and the geometry data is decoded based on a layer group including at least one layer of the tree structure and a fine granularity slice (FGS) matching a subgroup of the layer group. The above attribute data is expressed based on a tree structure, and the attribute data is decoded based on a layer group including at least one layer of the tree structure and a fine granularity slice (FGS) matching a subgroup of the layer group. Decryption device.

12. Step of encoding point cloud data; and A step of transmitting a bitstream including the above point cloud data; Encoding method.

13. In paragraph 12, The step of encoding the above point cloud data is A step of encoding geometry data of the above point cloud data, A step of encoding attribute data of the above point cloud data, comprising: The above geometry data is expressed based on a tree structure, and the geometry data is encoded based on a layer group including at least one layer of the tree structure and a fine granularity slice (FGS) matching a subgroup of the layer group, The above attribute data is expressed based on a tree structure, and the attribute data is encoded based on a layer group including at least one layer of the tree structure and a fine granularity slice (FGS) matching a subgroup of the layer group. How to decrypt.

14. A computer-readable storage medium storing a bitstream generated by the method according to Article 12.

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; encoding attribute data of the point cloud data; and A method comprising the step of transmitting data including the bitstream.

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