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

Abstract

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

Description

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

[0001] Point cloud data encoding device, point cloud data encoding method, point cloud data decoding device, and point cloud data decoding method

[0002] Point cloud content is represented as a point cloud, a collection of points within a coordinate system representing three-dimensional space. Point cloud content can represent three-dimensional media and is used to provide various services such as VR (Virtual Reality), AR (Augmented Reality), MR (Mixed Reality), and autonomous driving services. However, representing point cloud content requires tens to hundreds of thousands of point data. Therefore, a method for efficiently processing massive amounts of point data is required.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0016] Fig. 7 illustrates an example of a point cloud decoder according to embodiments.

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

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

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

[0020] Figure 11 shows an example of PSNR change in RAHT after sampling application according to embodiments.

[0021] Figure 12 shows an example of PSNR change in RAHT after correction of RAHT weights and values ​​during sampling according to embodiments.

[0022] Fig. 13 shows an encoding device according to embodiments.

[0023] Fig. 14 shows an encoding device according to embodiments.

[0024] Fig. 15 shows a decryption device according to embodiments.

[0025] Fig. 16 shows a decryption device according to embodiments.

[0026] Figure 17 shows a reference frame sampling flowchart according to embodiments.

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

[0028] Figure 19 shows a Sequence Parameter Set (SPS) according to embodiments.

[0029] Figure 20 shows a GPS (Geometry Parameter Set) according to embodiments.

[0030] Figure 21 shows an APS (Attribute Parameter Set) according to embodiments.

[0031] Figure 22 shows a TPS (Tile Parameter Set) according to embodiments.

[0032] Figure 23 shows a geometry data header according to embodiments.

[0033] Figure 24 shows an attribute data header according to embodiments.

[0034] Figure 25 shows an encoding method according to embodiments.

[0035] Figure 26 shows a decryption method according to embodiments.

[0036] 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.

[0037] 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.

[0038] Figure 1 illustrates an example of a point cloud content provision system according to embodiments.

[0039] 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.

[0040] 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.

[0041] 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).

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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)).

[0046] 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).

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0055] 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).

[0056] 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.).

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.).

[0062] 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.

[0063] FIG. 3 illustrates an example of a point cloud encoder according to embodiments.

[0064] 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.

[0065] 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.

[0066] 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).

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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).

[0076] 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.

[0077] 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).

[0078] 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.

[0079] As shown in the drawing, the converted attributes are input to the RAHT conversion unit (30008) and / or the LOD generation unit (30009).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] The coefficient quantization unit (30011) according to the embodiments quantizes attribute-coded attributes based on coefficients.

[0084] An arithmetic encoder (30012) according to embodiments encodes quantized attributes based on arithmetic coding.

[0085] 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).

[0086] Figure 4 illustrates examples of octree and occupancy codes according to embodiments.

[0087] 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.

[0088] 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.

[0089] d =Ceil(Log2(Max(x_n^int,y_n^int,z_n^int,n=1,…,N)+1))

[0090] 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.

[0091] 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.

[0092] 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.

[0093] Therefore, the point cloud encoder according to the embodiments can perform direct coding that directly codes the positions of points included in the specific area (or nodes excluding leaf nodes of the octree) without performing voxelization for the above-described specific area. 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 area (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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099]

[0100] 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.

[0101] Table 2-1. Triangles formed from vertices ordered 1,… ,n

[0102] n triangles

[0103] 3 (1,2,3)

[0104] 4 (1,2,3), (3,4,1)

[0105] 5 (1,2,3), (3,4,5), (5,1,3)

[0106] 6 (1,2,3), (3,4,5), (5,6,1), (1,3,5)

[0107] 7 (1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)

[0108] 8 (1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1)

[0109] 9 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7,9,3)

[0110] 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)

[0111] 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)

[0112] 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)

[0113] 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).

[0114] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0115] 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.

[0116] 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.

[0117] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] graph. Attribute prediction residuals quantization pseudo code

[0124] int PCCQuantization(int value, int quantStep) {

[0125] if( value >=0) {

[0126] return floor(value / quantStep + 1.0 / 3.0);

[0127] } else {

[0128] return -floor(-value / quantStep + 1.0 / 3.0);

[0129] }

[0130] }

[0131] graph. Attribute prediction residuals inverse quantization pseudo code

[0132] int PCCInverseQuantization(int value, int quantStep) {

[0133] if( quantStep ==0) {

[0134] return value;

[0135] } else {

[0136] return value * quantStep;

[0137] }

[0138] }

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 3) Create temporary arrays called updateweight and update and initialize them to 0.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149]

[0150]

[0151] *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 calculated as wl-1 x, y, z=wl 2x, y, z+wl 2x+1, y, z. The root node is the last g 1 0, 0, 0 and g 1 0, 0, 1 It is generated as follows:

[0152]

[0153] The gDC values ​​are also quantized and entropy coded, like the high-pass coefficients.

[0154] Fig. 7 illustrates an example of a point cloud decoder according to embodiments.

[0155] 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.

[0156] As described in Figure 1, the point cloud decoder can perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

[0157] 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).

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] An arithmetic decoder (7005) according to embodiments decodes an attribute bitstream using arithmetic coding.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Figure 8 is an example of a transmission device according to embodiments.

[0171] 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).

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] A slice is a series of syntax elements that represent all or part of a coded point cloud frame.

[0187] 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.

[0188] Fig. 9 is an example of a receiving device according to embodiments.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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).

[0195] 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).

[0196] The inverse quantization processing unit (9005) according to the embodiments can inverse quantize the decoded geometry.

[0197] 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.

[0198] 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.

[0199] 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).

[0200] 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).

[0201] 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.

[0202] Fig. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] <PCC+XR>

[0209] 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.

[0210] 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.

[0211] <PCC+XR+모바일폰>

[0212] The XR / PCC device (1030) can be implemented as a mobile phone (1040) or the like by applying PCC technology.

[0213] The mobile phone (1040) can decode and display point cloud content based on PCC technology.

[0214] <PCC+자율주행+XR>

[0215] Autonomous vehicles (1020) can be implemented as mobile robots, vehicles, unmanned aerial vehicles, etc. by applying PCC technology and XR technology.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] The PCC method / device according to the embodiments can be applied to a vehicle providing an autonomous driving service.

[0223] Vehicles providing autonomous driving services are connected to PCC devices to enable wired / wireless communication.

[0224] 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.

[0225] The encoding method / device according to the embodiments may include and perform operations such as Fig. 1 transmitting device (10000), acquisition unit (10001), encoder (10002), transmitter (10003), Fig. 2 acquisition (20000), encoding (20001), transmission (20002), Fig. 3, Fig. 8 encoder, Fig. 10 each device, Fig. 13 to Fig. 14 encoder, Fig. 17 reference frame-based sampling and value / weight correction, Fig. 18 to Fig. 24 bitstream generation, Fig. 15 encoding, etc.

[0226] The decoding method / device according to the embodiments may include and perform operations such as the receiving device (10004) in Fig. 1, the receiving unit (10005), the decoder (10006), the renderer (10007) in Fig. 2, the decoding (20003) and the rendering (20004) in Fig. 7, the decoder in Fig. 9, the respective devices in Fig. 10, the decoder in Figs. 15 to 16, the reference frame-based sampling and value / weight correction in Fig. 17, the bitstream parsing in Figs. 18 to 24, and the decoding in Fig. 26.

[0227] The decryption method / device according to the embodiments may follow the reverse process of the operation of the encoding method / device according to the embodiments.

[0228] The encoding / decoding device according to the embodiments of each drawing may be comprised of memory and a processor. The components of each drawing may correspond to hardware, software, a processor, and / or a combination thereof. The encoding / decoding method / device according to the embodiments may be referred to as "method / device" for short.

[0229] The method and apparatus according to the embodiments may include a method for supporting RAHT weight when using reference frame sampling.

[0230] Embodiments include a method for supporting weight and value correction for a Region Adaptive Hierarchical Transform (RAHT) when sampling point cloud frames to a reference frame used for inter-prediction compression of Geometry-based Point Cloud Compression (G-PCC). For example, when using reference frame sampling, a method for supporting RAHT weight and value correction and / or a signaling method may be included.

[0231] The embodiments relate to a method for improving the compression efficiency of G-PCC (Geometry-based Point Cloud Compression) for compressing 3D point cloud data.

[0232] Hereinafter, the encoder is referred to as an encoder, the decoder as a decoder, and the decoder as a decoder.

[0233] A point cloud consists of a collection of points, each of which can have geometry and attribute information. Geometry information includes 3D positional (XYZ) information, while attribute information includes color (RGB, YUV, etc.) and / or reflectance values.

[0234] The G-PCC encoding process can be comprised of dividing a point cloud into tiles by region, dividing each tile into slices for parallel processing, compressing geometry for each slice, and compressing attribute information based on the reconstructed geometry (decoded geometry) based on the positional information changed through compression.

[0235] The G-PCC decoding process can be composed of a process of receiving an encoded slice unit geometry bitstream and attribute bitstream, decoding the geometry, and decoding attribute information based on the geometry reconstructed through the decoding process.

[0236] Octree-based, predictive tree-based, or trisoup-based compression techniques can be used to compress geometry information.

[0237] For attribute information compression, compression techniques based on predicting transform, lifting transform, or RAHT transform can be used.

[0238] In these embodiments, a method for reducing compression ratio loss in a RAHT transform-based attribute compression tool is provided when a sampling technique of a reference frame is applied to efficiently apply compression through inter prediction of point cloud contents.

[0239] Figure 11 shows an example of PSNR change in RAHT after sampling application according to embodiments.

[0240] Embodiments can increase the compression ratio through inter-prediction compression operation between frames in a point cloud having multiple frames. Inter-prediction according to embodiments can increase the compression ratio by performing prediction from a current frame and a reference frame and applying entropy coding to the residual value of the predicted value and the current value. The reference frame can be one frame or multiple frames. When using multiple reference frames, memory usage and complexity may increase, but the compression ratio can be increased. The method / device according to embodiments can apply a sampling method to reduce the memory usage of the reference frames. In this case, in the RAHT transform-based attribute compression tool, the PSNR may decrease.

[0241] Referring to Fig. 11, if values ​​such as 88.74, 84.66, 80.80, 78.33, 75.87, and 74.32 are PSNR reference values ​​before applying sampling to the contents of point cloud data, then values ​​such as 88.74, 84.66, 80.79, 78.29, 75.76, and 74.15 are PSNR values ​​after applying sampling to the contents of point cloud data. The sampling operation according to the embodiments provides the effect of reducing the value of PSNR.

[0242] The embodiments include a method for improving the compression ratio (or PSNR) reduction of RAHT that occurs during reference frame sampling to efficiently apply a prediction compression technique using reference frames to point clouds having multiple frames. For example, a method for minimizing the compression ratio reduction (loss) while reducing memory usage is included.

[0243] Variations and combinations between embodiments are possible. The terms used in this document are to be understood based on their intended meaning, within the scope widely used in the relevant field.

[0244] Reference frame buffer management can be performed and applied in both PCC encoder / decoder.

[0245] The encoding method and / or decoding method according to the embodiments may include 1) a reference frame generation step, 2) a sampling step for the reference frame, 3) a representative degree value generation step for sampling for the reference frame, and / or 4) a RAHT weight and / or value correction step when applying reference frame sampling.

[0246] 1) Reference frame creation step:

[0247] The method according to the embodiments uses a reference frame for inter prediction. Points matching the geometry (position information of points, 'geometry data') of points in the current frame are found in the reference frame, and prediction is performed on the current point of the current frame from the selected reference points. Entropy coding of the residual value is performed based on the reference point having the smallest residual value among the differences between the predicted value and the value of the current point, and an encoded bitstream ('geometry bitstream') can be transmitted to the decoder.

[0248] Therefore, the reference frame can be essentially a previous frame, an accumulated frame, or one or more frames selected when using bi-prediction or multi-frame prediction.

[0249] Points within a reference frame may have geometric information based on Cartesian coordinates, or based on angular coordinates, or a combination of both.

[0250] Inter prediction according to embodiments can also be used for attribute encoding. A reference point is found from a reference frame that matches a point in the current frame. A reference point having attribute data most similar to the attribute data of the current point can be found. The attribute data for the current point can be predicted using the reference point, and an encoded bitstream ('attribute bitstream') can be generated by entropy coding the smallest residual value between the predicted value and the attribute data of the current point, and transmitted to a decoder.

[0251] A decoding method according to embodiments decodes geometry data (residual value) from a geometry bitstream based on an entropy method, predicts a predicted value for the current geometry data from a reference frame, and adds the predicted value and the residual value to restore the geometry data.

[0252] Similarly, the decoding method according to the embodiments decodes attribute data (residual value) from an attribute bitstream based on an entropy method, predicts a predicted value for the current attribute data from a reference frame, and adds the predicted value and the residual value to restore the attribute data.

[0253] 2) Sampling step for reference frame:

[0254] Methods according to embodiments may use one or more reference frames for inter prediction. Using multiple reference frames may increase memory usage. To address this issue, methods according to embodiments may reduce memory usage by applying a sampling technique to the reference frames. This reduction in the number of points provided through sampling may impact the inter prediction compression ratio. Therefore, the reference frame sampling method can reduce memory usage while minimizing the decrease in compression ratio.

[0255] 3) Step to generate representative degree values ​​for sampling for the reference frame:

[0256] When sampling a reference frame, points representing a specific region may exist. The degree to which points within a specific region are merged into a single point may vary. Only by adding this information to RAHT can the compression ratio (or PSNR) loss resulting from RAHT be reduced.

[0257] Therefore, the reference frame removes points that are considered duplicates based on criteria in a specific area through sampling and selects one, but can separately store the number of points removed (num_points_removed[index]) for how many points that one point represents (the number of points considered duplicates) in the reference frame buffer. Since the encoder and decoder have the same reference frame, the encoder and decoder can store the point values ​​(geometry data and attribute data) according to num_points_removed[index] in the reference frame buffer while downsampling the reference frame. A point considered duplicate may be a duplicate point, or it can be defined as a point removed from the reference frame within a specific area.

[0258] Points are sampled based on the number of removed points (num_points_removed[index]). Points sampled in the reference frame can be stored in the same order as sampled_points_in_reference_frame[index]) according to their index.

[0259] Sampled_points_in_reference_Frame[index] samples points in the reference frame, and represents the geometry data (values) and attribute data (values) of the points that remain unsampled (or removed). The reference frame buffer stores the unsampled points (Sampled_points_in_reference_Frame[index]) in the order of their indexes, in a number equal to num_points_removed[index].

[0260] Depending on whether to store the representative degree value (reference_sampling_removed_points_enabled), information on the number of removed points (num_points_removed[index]) may be stored. This value may be transmitted to the decryption unit as signaling information.

[0261] Figure 12 shows an example of PSNR change in RAHT after correction of RAHT weights and values ​​during sampling according to embodiments.

[0262] 4) RAHT weight and / or value correction step when applying reference frame sampling:

[0263] When applying inter prediction in RAHT, the point values ​​and weights can be changed as follows through the information on the number of removed points (num_points_removed[index]). This can prevent the part where PSNR drops by applying higher weights to points with high representativeness.

[0264] If the low frequency weight value of the reference frame of RAHT is stored in the weight information (weightsLf_ref[index]), the value calculated through weightsLf_ref[index] = num_points_removed[index]+1 can become the default weight value.

[0265] weightsLf_ref[index] may be referred to as a RahtBlkWeight variable. With respect to the transform block weight, the weight of the DC transform coefficient for the block located at (bs, bt, bv) of the transform level (lvl) is expressed as RahtBlkWeight[lvl][bs][bt][bv]. When a point located in the block located at (bs, bt, bv) of the transform level (lvl) is removed through sampling, when performing a transform such as RAHT, the necessary values ​​can be restored for the points (geometry data and / or attribute data) that were in the block.

[0266] If the attribute values ​​of the reference frame of RAHT are stored in attribute information (attrsLf_ref), the value calculated through attrsLf_ref[index] = sampled_points_in_reference_frame[index] * num_points_removed[index] can become the attribute value.

[0267] sampled_points_in_reference_frame[index] is a structure that contains point cloud data and information about the points (e.g., location (geometry data), properties (attribute data), duplicate points).

[0268] As follows, the method according to the embodiments can perform weight initialization.

[0269] weightsLf_ref.emplace_back(UrahtNode{attrInterPredParams.paramsForInterRAHT.mortonCode[i],

[0270] - 1,

[0271] + (attrInterPredParams.paramsForInterRAHT.numExcludePoints[i]+1),

[0272] {0, 0}});

[0273] As follows, the method according to the embodiments can perform modified value initialization.

[0274] for (int k = 0; k < numAttrs; k++) {

[0275] auto& attr_element =

[0276] attrInterPredParams.paramsForInterRAHT.attributes[i * numAttrs + k];

[0277] + if (!rahtPredParams.integer_haar_enable_flag) {

[0278] + attr_element += (attr_element) *

[0279] + attrInterPredParams.paramsForInterRAHT.numExcludPoints[i];

[0280] +}

[0281] attrsLf_ref.push_back(attr_element);

[0282] }

[0283] The sum of all block weights at a transform level is equal to the number of coded points (PointCnt). The weight of a block is equal to the sum of the weights of its child blocks.

[0284] The process of correcting (updating or calculating) weights and / or values ​​according to embodiments can be expressed as follows:

[0285] RahtBlkWeight[lvl][bs][bt][bv] :=

[0286] RahtBlkWeight = 0

[0287] for (ptIdx = 0; ptIdx < PointCnt; ptIdx++)

[0288] RahtBlkWeight += isPointInSubtree[ptIdx]

[0289] where

[0290] isPointInSubtree[ptIdx] :=

[0291] bs == AttrPos[ptIdx][0] >> lvl

[0292] && bt == AttrPos[ptIdx][1] >> lvl

[0293] && bv == AttrPos[ptIdx][2] >> lvl

[0294] By reflecting the representativeness of the values ​​and weights, the PSNR value can be restored to the state before sampling.

[0295] Referring to FIG. 12, PSNR values ​​of 88.74, 84.66, 80.80, 78.35, 75.87, 74.32, etc. can be changed to 88.74, 84.66, 80.81, 78.33, 75.88, 74.32 in RAHT after correcting the values ​​of sampling and RAHT weights and points according to the embodiments.

[0296] Values ​​and weights can be adjusted based on information (raht_inter_weight_update_enabled) indicating whether to perform RAHT weight and value adjustment. The raht_inter_weight_update_enabled value can be transmitted to the decryption unit as signaling information.

[0297] Generation and sampling of reference frames can be performed in the same way in both the encoder and decoder.

[0298] Fig. 13 shows an encoding device according to embodiments.

[0299] The encoding device may be composed of a memory and a processor, and the processor may perform the operations of each component of FIG. 13. The encoding device of FIG. 13 may perform and include operations such as the transmitting device (10000), the obtaining unit (10001), the encoder (10002), the transmitter (10003), the obtaining (20000), the encoding (20001), the transmitting (20002) of FIG. 2, the encoder of FIG. 3, FIG. 8, the respective devices of FIG. 10, the encoder of FIG. 14, the sampling and value / weight correction based on the reference frame of FIG. 17, the bitstream generation of FIG. 18 to FIG. 24, and the encoding of FIG. 15.

[0300] The data input section can read the input data (e.g., ply, configuration file, etc.) and control the settings required for the data.

[0301] The coordinate system transformation unit can support coordinate system changes, such as changing the xyz axes or converting from the xyz rectangular coordinate system to the spherical coordinate system.

[0302] The geometry information transformation quantization processing unit can adjust the scale by multiplying the x, y, and z values ​​of the geometry location of the point cloud point by the scale (scale = geometry quantization value) setting.

[0303] The spatial partitioning unit can divide point cloud data into tiles or slices for area-specific access or parallel processing of content.

[0304] The voxelization processing unit can support the process of rounding the geometric location values ​​of points to which scale is applied to integers.

[0305] The geometry information intra prediction unit can apply geometry intra coding. Intra coding methods may include octree coding, predictive tree coding, and trisoup coding.

[0306] The geometry information inter prediction unit can perform octree-based inter-coding, predictive-tree-based inter-coding, and trisoup-based inter-coding based on the difference between the predicted values ​​between the current frame and the motion-compensated reference frame.

[0307] The geometry entropy encoding unit can support entropy coding of the results of the geometry information intra / inter prediction unit.

[0308] The color conversion processor can support attribute type conversion, such as changing RGB color to YUV.

[0309] The color rescaling unit can predict attribute values ​​appropriate for the changed location when the geometry is scaled and the location information values ​​are changed.

[0310] The attribute information intra prediction unit can apply attribute information intra coding. Intra coding methods may include the Predicting Transform coding method, the Lift Transform coding method, and the RAHT coding method.

[0311] The attribute information inter prediction unit may include a method for coding residual values ​​based on the difference in attribute prediction values ​​between the current frame and a motion-compensated reference frame.

[0312] Attribute information entropy encoding can support entropy coding of the results of attribute information intra / inter prediction.

[0313] The reference frame generation unit can store restored geometry and restored attribute information in a reference frame buffer and transfer reference frame data from the reference frame to other modules.

[0314] In the reference frame sampling section, reference frame sampling can be performed depending on whether reference frame sampling is enabled (reference_sampling_enabled).

[0315] Fig. 14 shows an encoding device according to embodiments.

[0316] FIG. 14 further includes additional configurations for sampling and / or RAHT inter prediction operations according to embodiments in the FIG. 13 encoding device.

[0317] The reference frame sampling unit can perform the reference frame sampling unit when reference frame sampling is required.

[0318] In the reference frame sampling unit, reference frame sampling (see the sampling step for the reference frame described above, 2) can be performed depending on whether reference frame sampling is enabled (reference_sampling_enabled).

[0319] In the reference frame sampling section, while performing reference frame sampling, points considered to be duplicates are removed based on criteria in a specific area and one is selected, but the number of points represented by that one point (the number of points considered to be duplicates) can be stored separately in num_points_removed[index] (see the step of generating representative degree values ​​for sampling for the reference frame described above, 3). However, this can be performed depending on whether or not representative degree values ​​are stored (reference_sampling_removed_points_enabled).

[0320] The RAHT inter prediction unit can perform RAHT-based inter prediction.

[0321] The RAHT inter prediction part can have its values ​​and weights updated according to raht_inter_weight_update_enabled.

[0322] When performing inter prediction, the RAHT inter prediction unit can adjust attribute values ​​and weight values ​​using the num_points_removed[index] value saved in the reference frame sampling unit when sampling is applied to the reference frame and reference_sampling_removed_points_enabled and raht_inter_weight_update_enabled are true (see RAHT weight and / or value correction step when applying reference frame sampling, 4 described above).

[0323] The RAHT inter prediction unit can perform RAHT inter prediction based on the adjusted values ​​and weights.

[0324] Although the embodiments are described from the RAHT perspective of attribute encoding, when generating LoD of attribute encoding and performing LoD-based inter-prediction, inter-prediction can also be performed by generating a reference frame, applying sampling to the reference frame, and correcting values ​​and / or weights for inter-prediction.

[0325] Fig. 15 shows a decryption device according to embodiments.

[0326] The decoding device may be configured with a memory and a processor, and the processor may perform the operations of each configuration of FIG. 13. The decoding device of FIG. 15 may include and perform operations such as the receiving device (10004), receiving unit (10005), decoder (10006), renderer (10007) of FIG. 1, decoding (20003), rendering (20004) of FIG. 2, decoder of FIG. 7 and FIG. 9, each device of FIG. 10, decoder of FIG. 16, reference frame-based sampling and value / weight correction of FIG. 17, bitstream parsing of FIG. 18 to FIG. 24, decoding of FIG. 26, etc.

[0327] The geometry entropy decoding unit can entropy decode a geometry bitstream.

[0328] The geometry information intra prediction restoration unit can restore predicted values ​​predicted using geometry intra coding. Intra coding methods may include octree coding, predictive-tree coding, and trisoup coding.

[0329] The geometry information inter prediction restoration unit can restore predicted values ​​predicted by geometry inter coding. Inter coding methods may include octree-based inter coding, predictive-tree-based inter coding, and trisoup-based inter coding.

[0330] The coordinate system inversion unit can restore the changed xyz axes or inversely transform the transformed coordinate system into the xyz orthogonal coordinate system.

[0331] The geometry information conversion dequantization processing unit can restore the signaled scale (scale = geometry quantization value) and apply it to the geometry location x, y, and z values ​​of the restored point.

[0332] Attribute residual information entropy decoding can entropy decode attribute bitstreams.

[0333] The attribute information intra prediction restoration unit can restore predicted values ​​predicted using attribute information intra coding. Intra coding methods may include the Predicting Transform coding method, the Lift Transform coding method, and the RAHT coding method.

[0334] The attribute information inter prediction restoration unit can restore the predicted values ​​predicted by attribute information inter coding.

[0335] The color inversion processing unit can restore the converted attributes to RGB colors.

[0336] The reference frame generation unit can store restored geometry and restored attribute information in a reference frame buffer and transfer reference frame data from the reference frame to other modules.

[0337] In the reference frame sampling section, reference frame sampling can be performed depending on whether reference frame sampling is enabled (reference_sampling_enabled).

[0338] Fig. 16 shows a decryption device according to embodiments.

[0339] FIG. 16 further includes additional configurations for sampling and / or RAHT inter prediction operations according to embodiments in the decoding device of FIG. 15.

[0340] The decoding device of Fig. 15-16 can follow the reverse process of the operation of the encoding device of Fig. 13-14.

[0341] If reference frame sampling is required, the reference frame generation unit can perform the reference frame sampling unit. The reference frame sampling unit can perform reference frame sampling (see the sampling step for the reference frame described above, 2) depending on whether the restored reference frame is sampled (reference_sampling_enabled).

[0342] In the reference frame sampling section, while performing reference frame sampling, points considered to be duplicates are removed based on criteria in a specific area and one is selected, but the number of points represented by that one point (the number of points considered to be duplicates) can be separately stored in num_points_removed[index]. (Refer to the representative degree value generation step for sampling for the reference frame described above, 3) However, this can be performed depending on whether to store the restored representative degree value (reference_sampling_removed_points_enabled).

[0343] The RAHT inter prediction unit can perform RAHT-based inter prediction.

[0344] The RAHT inter prediction part can have its values ​​and weights adjusted according to the restored raht_inter_weight_update_enabled.

[0345] When performing inter prediction, the RAHT inter prediction unit can adjust attribute values ​​and weight values ​​using the num_points_removed[index] value saved in the reference frame sampling unit when sampling is applied to the reference frame and reference_sampling_removed_points_enabled and raht_inter_weight_update_enabled are true (see RAHT weight and / or value correction step when applying reference frame sampling, 4 described above).

[0346] The RAHT inter prediction unit can perform the RAHT inter prediction decoding process based on the adjusted values ​​and weights.

[0347] Figure 17 shows a reference frame sampling flowchart according to embodiments.

[0348] FIG. 17 illustrates in detail a flowchart of operations related to reference frame sampling used in encoding operations and decoding operations, as described in FIGS. 13 to 16.

[0349] If the reference_sampling_enabled information is true, reference frame-based sampling operation can be performed. When the encoder performs reference frame-based sampling operation, it can define the reference_sampling_enabled information as true and include the reference_sampling_enabled information as syntax in the bitstream and pass it to the decoder. The decoder can receive the bitstream and perform reference frame-based sampling operation if the reference_sampling_enabled information is true.

[0350] If reference_sampling_removed_points_enabled is true, sampling can be performed based on the degree of representativeness of points representing a specific area (the number of points considered duplicates).

[0351] The encoder can perform a sampling operation based on the degree of representativeness of points representing an area (the number of points considered duplicates) and pass the reference_sampling_removed_points_enabled information to the decoder by setting it to true. If the reference_sampling_removed_points_enabled information is true, the decoder can perform a sampling operation based on the degree of representativeness of points representing a specific area (the number of points considered duplicates).

[0352] Step 1700: The encoder can store points representing a number of points in a specific region within a reference frame according to a reference as sampled_points_in_reference_frame[index] information. The decoder can identify points representing a number of points in a specific region according to a reference as a reference through the sampled_points_in_reference_frame[index] information.

[0353] Step 1701: The number of points that constitute one representative point can be expressed as num_points_removed[index].

[0354] When performing RAHT (or LoD) based inter prediction in attribute encoding and decoding, the following actions can be performed further:

[0355] Step 1702: The weights for inter-prediction, weightsLf_ref[index], can be adjusted based on the value of num_points_removed[index]. For example, weightsLf_ref[index] = num_points_removed[index]+1.

[0356] Step 1703: Attribute data (attribute values) for inter-prediction can be corrected based on sampling. The attribute values ​​can be corrected based on the number of removed points and the number of sampled points within the reference frame. For example, the attribute value can be corrected as attrsLf_ref[index]=sampled_points_in_reference_frame[index]*num_points_removed[index].

[0357] Figure 18 shows a bitstream according to embodiments.

[0358] The encoding method / device according to the embodiments (Fig. 1 transmitting device (10000), acquisition unit (10001), encoder (10002), transmitter (10003), Fig. 2 acquisition (20000), encoding (20001), transmission (20002), Figs. 3 and 8 encoder, Fig. 10 each device, Figs. 13 to 14 encoder, Fig. 17 reference frame-based sampling and value / weight correction, Figs. 18 to 24 bitstream generation, Fig. 15 encoding, etc.) can encode point cloud data, generate related parameter (signaling) information, and generate and transmit a Fig. 18 bitstream including encoded point cloud data and parameters.

[0359] The decoding method / device according to the embodiments (Fig. 1 receiving device (10004), receiving unit (10005), decoder (10006), renderer (10007), Fig. 2 decoding (20003), rendering (20004), Fig. 7, Fig. 9 decoder, Fig. 10 each device, Fig. 15 to Fig. 16 decoder, Fig. 17 reference frame-based sampling and value / weight correction, Fig. 18 to Fig. 24 bitstream parsing, Fig. 26 decoding, etc.) can receive the bitstream of Fig. 18 and decode point cloud data based on parameters in the bitstream.

[0360] 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 PU header + Geometry PU data] | geometry slice data), Attr (Attribute bitstream = attribute data unit header + [attribute PU header + attribute PU data] | attribute data unit data).

[0361] The method according to the embodiments may signal reference frame sampling and RAHT inter related option information by adding it to at least one of SPS, GPS, and / or TPS.

[0362] The method according to the embodiments may signal reference frame sampling and RAHT inter related option information by adding them to at least one of the TPS, the geometry header and / or the attribute header for each slice.

[0363] Tiles or slices are provided to allow point clouds to be divided and processed by region.

[0364] When dividing by region, you can set different options for generating neighboring point sets for each region, providing a choice between low complexity and somewhat lower reliability, or conversely, high complexity but high reliability. These settings can vary depending on the receiver's processing capacity.

[0365] Therefore, when a point cloud is divided into tiles, different options can be applied to each tile.

[0366] When a point cloud is divided into slices, different options can be applied to each slice.

[0367] Figure 19 shows a Sequence Parameter Set (SPS) according to embodiments.

[0368] Fig. 19 SPS may be included in the Fig. 18 bitstream. SPS may further include reference frame sampling and RAHT inter-related option information.

[0369] Profile (profile_idc): Indicates the profile to which the bitstream conforms. The value of profile_idc can follow ISO / IEC values.

[0370] Profile Compatibility Flags (profile_compatibility_flags): If this value is 1, it indicates that the bitstream complies with the profile whose profile_idc is j.

[0371] Number of attribute sets (sps_num_attribute_sets): Indicates the number of coded attributes in the bitstream. The value of sps_num_attribute_sets can range from 0 to 63.

[0372] Attribute dimension (attribute_dimension[i]): Indicates the number of components of the i-th attribute.

[0373] Attribute instance ID (attribute_instance_id[i]): Represents the instance ID of the i-th attribute.

[0374] Reference sampling enabled (reference_sampling_enabled): Indicates whether reference frame sampling is used for the sequence.

[0375] reference_sampling_removed_points_enabled: When using reference frame sampling in a sequence, indicates whether to store the degree of representativeness of points representing a specific area (the number of points considered duplicates).

[0376] Figure 20 shows a GPS (Geometry Parameter Set) according to embodiments.

[0377] Fig. 20 GPS may be included in the Fig. 18 bitstream. GPS may further include reference frame sampling and RAHT inter-related optional information during the geometry information encoding / decoding process.

[0378] Geometry parameter set ID (gps_geom_parameter_set_id): Indicates a GPS identifier that can be referenced by other syntax elements. The value of gps_seq_parameter_set_id can be in the range 0 to 15.

[0379] Sequence Parameter Set ID (gps_seq_parameter_set_id): Indicates the sps_seq_parameter_set_id value for the active SPS. The value of gps_seq_parameter_set_id can be in the range of 0 to 15.

[0380] Geometry Tree Type (geom_tree_type): If geom_tree_type is 0, it indicates that the location information is coded using an octree (accumulation tree). If geom_tree_type is 1, it indicates that the location information is coded using a prediction tree.

[0381] Reference sampling enabled (reference_sampling_enabled): Indicates whether reference frame sampling is used for the frame.

[0382] reference_sampling_removed_points_enabled: Indicates whether to store the degree of representativeness (the number of points considered duplicates) of points representing a specific area when using reference frame sampling for a frame.

[0383] Figure 21 shows an APS (Attribute Parameter Set) according to embodiments.

[0384] Fig. 21 APS may be included in the Fig. 18 bitstream. APS may further include parameter information for reference frame sampling and RAHT inter.

[0385] Attribute Parameter Set ID (aps_attr_parameter_set_id): Indicates an identifier for the APS so that other syntax elements can reference it. The value of aps_attr_parameter_set_id can be in the range of 0 to 15.

[0386] Sequence Parameter Set ID (aps_seq_parameter_set_id): Indicates the sps_seq_parameter_set_id value for the active SPS. The value of aps_seq_parameter_set_id can be in the range of 0 to 15.

[0387] Lifting_num_pred_nearest_neighbours: Indicates the maximum number of nearest neighbors to use for prediction. The value of lifting_num_pred_nearest_neighbours can be in the range of 1 to xx.

[0388] Lifting_max_num_direct_predictors: Indicates the maximum number of predictors to use for direct prediction. The value of lifting_max_num_direct_predictors can range from 0 to lifting_num_pred_nearest_neighbors. The value of the variable MaxNumPredictors used in the decoding process is as follows: MaxNumPredictors = lifting_max_num_direct_predicots + 1

[0389] Reference sampling enabled (reference_sampling_enabled): Indicates whether reference frame sampling is used for the frame.

[0390] reference_sampling_removed_points_enabled: Indicates whether to store the degree of representativeness (the number of points considered duplicates) of points representing a specific area when using reference frame sampling for a frame.

[0391] RAHT Inter-Weight Update Enabled (raht_inter_weight_update_enabled): Indicates whether to adjust values ​​and weights for RAHT inter prediction when using reference frame sampling for a frame. Depending on the implementation, this information may indicate whether to adjust weights for RAHT inter-prediction as well as for LoD inter-prediction.

[0392] Figure 22 shows a TPS (Tile Parameter Set) according to embodiments.

[0393] Fig. 22 TPS may be included in the Fig. 18 bitstream. TPS may further include parameter information for reference frame sampling and RAHT inter.

[0394] Number of tiles (num_tiles): Indicates the number of tiles signaled for the bitstream. If not present, num_tiles is inferred to be 0.

[0395] Tile bounding box offset X (tile_bounding_box_offset_x[i]): Indicates the x-offset of the i-th tile in Cartesian coordinates. If not present, the value of tile_bounding_box_offset_x[0] is inferred as sps_bounding_box_offset_x.

[0396] Tile bounding box offset Y (tile_bounding_box_offset_y[i]): Indicates the y-offset of the i-th tile in Cartesian coordinates. If not present, the value of tile_bounding_box_offset_y[0] is inferred to be sps_bounding_box_offset_y.

[0397] Tile bounding box offset Z (tile_bounding_box_offset_z[i]): Indicates the z-offset of the i-th tile in Cartesian coordinates. If not present, the value of tile_bounding_box_offset_z[0] is inferred as sps_bounding_box_offset_z.

[0398] reference_sampling_enabled: Indicates whether reference frame sampling is used for the tile.

[0399] reference_sampling_removed_points_enabled: When using reference frame sampling for tiles, indicates whether to store the degree of representation (the number of points considered duplicate) of points representing a specific area.

[0400] RAHT Inter Weight Update Enable (raht_inter_weight_update_enabled): Indicates whether to correct values ​​and weights for RAHT inter prediction when using reference frame sampling for tiles.

[0401] This information, according to the implementations, may indicate whether to adjust the weights for RAHT inter-prediction as well as the weights for Lod inter-prediction.

[0402] Figure 23 shows a geometry data header according to embodiments.

[0403] The geometry data header of Fig. 23 is included in the bitstream of Fig. 18. The geometry data header may further include reference frame sampling and RAHT inter information during the geometry information encoding / decoding process.

[0404] Geometry parameter set ID (gsh_geometry_parameter_set_id): Indicates the gps_geom_parameter_set_id value of the active GPS.

[0405] Tile ID (gsh_tile_id): Indicates the tile ID value referenced in GSH. The value of gsh_tile_id can be in the range of 0 to XX.

[0406] Slice ID (gsh_slice_id): Identifies the slice header that other syntax elements will reference. The value of gsh_slice_id can be in the range 0 to XX.

[0407] reference_sampling_enabled: Indicates whether reference frame sampling is enabled for the slice.

[0408] reference_sampling_removed_points_enabled: Indicates whether to store the degree of representativeness (the number of points considered duplicates) of points representing a specific region when using reference frame sampling for a slice.

[0409] Figure 24 shows an attribute data header according to embodiments.

[0410] The attribute data header of Fig. 24 is included in the bitstream of Fig. 18. The attribute data header may further include parameter information for reference frame sampling and RAHT inter.

[0411] Attribute parameter set ID (abh_attr_parameter_set_id): Indicates the aps_attr_parameter_set_id value of the active APS.

[0412] Attribute SPS Attribute Index (abh_attr_sps_attr_idx): Indicates the set of attributes of the active SPS. The value of abh_attr_sps_attr_idx can be in the range of 0 to sps_num_attribute_sets in the active SPS.

[0413] Attribute Geometry Slice ID (ash_attr_geom_slice_id): Indicates the gsh_slice_id value of the active geometry slice header.

[0414] reference_sampling_enabled: Indicates whether reference frame sampling is enabled for the slice.

[0415] reference_sampling_removed_points_enabled: Indicates whether to store the degree of representativeness (the number of points considered duplicates) of points representing a specific region when using reference frame sampling for a slice.

[0416] RAHT Inter-Weight Update Enabled (raht_inter_weight_update_enabled): Indicates whether to adjust values ​​and weights for RAHT inter prediction when using reference frame sampling for slices. Depending on the implementation, this information may indicate whether to adjust weights for RAHT inter-prediction as well as for LOD inter-prediction.

[0417] Figure 25 shows an encoding method according to embodiments.

[0418] The encoding method according to the embodiments may include a step of encoding geometry data of point cloud data (S2500) and / or a step of encoding attribute data of point cloud data (S2510).

[0419] Referring to FIG. 14, etc., the step of encoding geometry data (S2500) may include: a step of generating a reference frame for a frame including geometry data; and a step of sampling points within the reference frame.

[0420] Geometry data and attribute data are included in the bitstream, and the bitstream may include at least one of information indicating whether sampling is performed on a reference frame for point cloud data, or information indicating whether points are removed for a specific area in the reference frame and representative points for the specific area are stored.

[0421] Based on sampling, at least one point within a reference frame is removed, the number of removed points is stored in point removal information, a representative point for a specific area within the reference frame is defined based on the removed points, the sampled points are stored in a buffer in a certain order, and the removed points may represent duplicate points in a specific area or points removed from the reference frame through sampling in a specific area.

[0422] The step of encoding attribute data (S2510) includes: a step of predicting attribute data based on a reference frame for point cloud data, wherein the attribute data is predicted based on weights generated based on the number of points removed from the reference frame, and the attribute data of the reference frame can be corrected based on the number of points removed from the reference frame.

[0423] The encoding method can be performed by an encoding device (encoder). The encoding device, with reference to FIG. 1 and the like, includes a memory; and at least one processor connected to the memory; and the at least one processor can be configured to: encode geometry data of point cloud data; and encode attribute data of point cloud data.

[0424] The embodiments further include a computer-readable storage medium storing a bitstream generated by the method according to FIG. 25.

[0425] Embodiments further include a method comprising: obtaining a bitstream for point cloud data, the bitstream being generated based on a step of encoding geometry data of the point cloud data; and a step of encoding attribute data of the point cloud data; and transmitting data including the bitstream.

[0426] Figure 26 shows a decryption method according to embodiments.

[0427] A decoding method according to embodiments may include a step of decoding geometry data of point cloud data in a bitstream (S2600) and / or a step of decoding attribute data of point cloud data (S2610).

[0428] Referring to FIG. 16 and the description related to reference frame sampling described above, the step of decoding geometry data (S2600) may include: a step of generating a reference frame for a frame including geometry data; and a step of sampling points within the reference frame.

[0429] Referring to “reference_sampling_enabled”, “reference_sampling_removed_points_enabled”, etc. of FIG. 16, FIG. 18 to FIG. 24, etc., geometry data and attribute data are included in the bitstream, and the bitstream may include at least one of information indicating whether sampling is performed on a reference frame for point cloud data, or information indicating whether points are removed for a specific area in the reference frame and representative points for the specific area are stored.

[0430] In relation to the description of the representative degree storage in the sampling described above and in FIG. 16, based on the sampling, at least one point within the reference frame is removed, the number of removed points is stored in the point removal information, a representative point for a specific area within the reference frame is defined based on the removed points, the sampled points are stored in a buffer in a certain order, and the removed points may represent duplicate points in the specific area or points removed from the reference frame through sampling in the specific area.

[0431] Referring to FIG. 16 and the description of attribute inter-prediction weight / value correction described above, the step of decoding attribute data (S2610) includes: a step of predicting attribute data based on a reference frame for point cloud data, wherein the attribute data is predicted based on weights generated based on the number of points removed from the reference frame, and the attribute data of the reference frame can be corrected based on the number of points removed from the reference frame.

[0432] Referring to “raht_inter_weight_update_enabled” in FIGS. 21 to 24, geometry data and attribute data are included in the bitstream, and the bitstream may further include information indicating whether values ​​and weights for attribute data are corrected for inter prediction on the attribute data.

[0433] The decryption method can be performed by a decryption device (decoder). The decryption device, as shown in FIG. 1, includes a memory; and at least one processor connected to the memory; and the at least one processor can be configured to: receive a bitstream including point cloud data; and decode the point cloud data.

[0434] The method and device according to the embodiments provide the following technical effects.

[0435] The PCC encoding method, PCC decoding method, and signaling method of the above-described embodiments can provide the following effects.

[0436] Point clouds with multiple frames can be compressed using inter-frame prediction compression techniques. Inter-prediction performs predictions from the current frame and a reference frame, and entropy coding is applied to the residual values ​​between the predicted values ​​and the current values ​​to increase the compression ratio. The reference frame can be a single frame or multiple frames. Using multiple reference frames may increase memory usage and complexity, but it can also increase the compression ratio. Measures to address this increased memory usage may be necessary. Sampling methods can be applied to reduce the memory usage of the reference frames. In this case, the PSNR may deteriorate in RAHT transform-based attribute compression tools.

[0437] The embodiments provide a method for improving the compression ratio (or PSNR) reduction of RAHT that occurs during reference frame sampling, which is used to efficiently apply a prediction compression technique using reference frames to point clouds having multiple frames. This provides a method for minimizing compression ratio loss while reducing memory usage.

[0438] Therefore, these examples provide a method to minimize compression ratio loss and reduce memory usage. By allowing the sampling rate of reference frames to be adjusted based on the memory availability of the receiving device, efficient storage of reference frames for inter-prediction of point cloud data is possible.

[0439] Accordingly, the transmission method / device according to the embodiments can efficiently compress point cloud data to transmit the data, and by transmitting signaling information for this, the reception method / device according to the embodiments can also efficiently decode / restore point cloud data.

[0440] The sampling-based encoding and decoding method according to the embodiments can reduce memory usage and minimize compression ratio reduction.

[0441] The embodiments have been described in terms of methods and / or devices, and the descriptions of methods and devices may be applied complementarily.

[0442] 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.

[0443] 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.

[0444] 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”.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] As described above, the relevant contents have been described in the best form for carrying out the embodiments.

[0451] As described above, the embodiments may be applied in whole or in part to a point cloud data transmission and reception device and system.

[0452] Those skilled in the art may make various changes or modifications to the embodiments within the scope of the embodiments.

[0453] Embodiments may include modifications / changes, which do not depart from the scope of the claims and their equivalents.

Claims

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

2. In paragraph 1, The steps for decoding the above geometry data are: A step of generating a reference frame for a frame including the above geometry data; and a step of sampling points within the above reference frame; How to decrypt.

3. In paragraph 1, The above geometry data and the above attribute data are included in the bitstream, The bitstream includes at least one of information indicating whether sampling is performed on a reference frame for the point cloud data, or information indicating whether points are removed for a specific area in the reference frame and representative points for the specific area are stored. How to decrypt.

4. In paragraph 2, Based on the above sampling, at least one point within the reference frame is removed, and the number of removed points is stored in the point removal information, Based on the above-described removed points, representative points for a specific area within the reference frame are defined, and the sampled points are stored in a buffer in a certain order. The above removed points represent points that are duplicated in the specific area or points that are removed from the reference frame through sampling in the specific area. How to decrypt.

5. In paragraph 1, The steps to decode the above attribute data are: A step of predicting the above attribute data based on a reference frame for the point cloud data, Based on the weights generated based on the number of points removed from the above reference frame, the attribute data is predicted, The attribute data of the above reference frame is corrected based on the number of points removed from the above reference frame. How to decrypt.

6. In paragraph 1, The above geometry data and the above attribute data are included in the bitstream, The bitstream further includes information indicating whether values ​​and weights for the attribute data are corrected for inter prediction of the attribute data. How to decrypt.

7. 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.

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

9. In paragraph 8, The steps for encoding the above geometry data are: A step of generating a reference frame for a frame including the above geometry data; and a step of sampling points within the above reference frame; Encoding method.

10. In paragraph 8, The above geometry data and the above attribute data are included in the bitstream, The bitstream includes at least one of information indicating whether sampling is performed on a reference frame for the point cloud data, or information indicating whether points are removed for a specific area in the reference frame and representative points for the specific area are stored. Encoding method.

11. In paragraph 8, Based on the above sampling, at least one point within the reference frame is removed, and the number of removed points is stored in the point removal information, Based on the above-described removed points, representative points for a specific area within the reference frame are defined, and the sampled points are stored in a buffer in a certain order. The above removed points represent points that are duplicated in the specific area or points that are removed from the reference frame through sampling in the specific area. Encoding method.

12. In paragraph 8, The steps for encoding the above attribute data are: A step of predicting the above attribute data based on a reference frame for the point cloud data, Based on the weights generated based on the number of points removed from the above reference frame, the attribute data is predicted, The attribute data of the above reference frame is corrected based on the number of points removed from the above reference frame. Encoding method.

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

14. A computer-readable storage medium storing a bitstream generated by the method according to Article 8.

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

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