WO2024077548A1 - 点云解码方法、点云编码方法、解码器和编码器 - Google Patents

点云解码方法、点云编码方法、解码器和编码器 Download PDF

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WO2024077548A1
WO2024077548A1 PCT/CN2022/125110 CN2022125110W WO2024077548A1 WO 2024077548 A1 WO2024077548 A1 WO 2024077548A1 CN 2022125110 W CN2022125110 W CN 2022125110W WO 2024077548 A1 WO2024077548 A1 WO 2024077548A1
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refinement
point
layer
target
target point
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PCT/CN2022/125110
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English (en)
French (fr)
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元辉
郭甜
陈晨
李明
邹丹
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Oppo广东移动通信有限公司
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Priority to PCT/CN2022/125110 priority Critical patent/WO2024077548A1/zh
Publication of WO2024077548A1 publication Critical patent/WO2024077548A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T9/00—Image coding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/593—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial prediction techniques

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  • Embodiments of the present application relate to the field of coding and decoding technology, and more specifically, to a point cloud decoding method, a point cloud encoding method, a decoder and an encoder.
  • Point clouds have begun to be popularized in various fields, such as virtual/augmented reality, robotics, geographic information systems, and medicine. With the continuous improvement of the accuracy and speed of scanning equipment, a large number of point clouds on the surface of objects can be accurately obtained, and often hundreds of thousands of points can correspond to one scene. Such a large number of points also brings challenges to computer storage and transmission. Therefore, point compression has become a hot issue.
  • the attribute prediction value of the current point in the point cloud will depend on the attribute reconstruction value of the encoded point. This dependency will inevitably propagate the distortion of the previously encoded point to the current point, which will eventually lead to error accumulation and reduce the encoding performance.
  • the process of the decoder predicting the attribute information of the current point is similar to that of the encoder, it will also affect the decoding performance.
  • the present application provides a point cloud decoding method, a point cloud encoding method, a decoder and an encoder, which can improve the decoding performance and enhance the quality of the reconstructed point cloud.
  • an embodiment of the present application provides a point cloud decoding method, comprising:
  • the initial reconstruction value of the attribute information of the target point is filtered based on the filter coefficient of the target point to determine the reconstruction value of the attribute information of the target point.
  • an embodiment of the present application provides a point cloud encoding method, including:
  • the residual value of the attribute information of the target point and the filter coefficient of the target point are encoded to obtain a code stream.
  • an embodiment of the present application provides a decoder, including:
  • a decoding unit used for decoding a code stream of a point cloud and determining a residual value of attribute information of a target point in the point cloud;
  • a reconstruction unit used to determine an initial reconstruction value of the attribute information of the target point based on the residual value of the attribute information of the target point and the predicted value of the attribute information of the target point;
  • a determination unit used to determine the filter coefficient of the target point
  • the filtering unit is used to filter the initial reconstruction value of the attribute information of the target point based on the filtering coefficient of the target point to determine the reconstruction value of the attribute information of the target point.
  • an encoder including:
  • a residual unit used to determine a residual value of the attribute information of the target point based on a true value of the attribute information of the target point in the point cloud and a predicted value of the attribute information of the target point;
  • a determination unit used to determine a filter coefficient of the target point based on neighboring points of the target point
  • the encoding unit is used to encode the residual value of the attribute information of the target point and the filter coefficient of the target point to obtain a code stream.
  • an embodiment of the present application provides a decoder, including:
  • a processor adapted to implement computer instructions
  • a computer-readable storage medium stores computer instructions, wherein the computer instructions are suitable for being loaded by a processor and executing the decoding method in the first aspect or its various implementations involved above.
  • the number of the processor is one or more, and the number of the memory is one or more.
  • the computer-readable storage medium may be integrated with the processor, or the computer-readable storage medium may be disposed separately from the processor.
  • an encoder including:
  • a processor adapted to implement computer instructions
  • a computer-readable storage medium stores computer instructions, wherein the computer instructions are suitable for being loaded by a processor and executing the encoding method in the second aspect or its various implementation modes involved above.
  • the number of the processor is one or more, and the number of the memory is one or more.
  • the computer-readable storage medium may be integrated with the processor, or the computer-readable storage medium may be disposed separately from the processor.
  • an embodiment of the present application provides a computer-readable storage medium, which stores computer instructions.
  • the computer instructions When the computer instructions are read and executed by a processor of a computer device, the computer device executes the point cloud decoding method involved in the first aspect mentioned above or the point cloud encoding method involved in the second aspect mentioned above.
  • an embodiment of the present application provides a computer program product or a computer program, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium.
  • a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the point cloud decoding method involved in the first aspect mentioned above or the point cloud encoding method involved in the second aspect mentioned above.
  • an embodiment of the present application provides a code stream, which is a code stream as described in the method described in the first aspect above or a code stream generated by the method described in the second aspect above.
  • the present application decodes the code stream of the point cloud and determines the filter coefficient of the target point in the point cloud; and based on the filter coefficient of the target point, the initial reconstruction value of the attribute information of the target point is filtered to determine the reconstructed value of the attribute information of the target point.
  • the accuracy of the reconstructed value of the attribute information of the target point can be improved, thereby improving the decoding performance and achieving the enhancement of the quality of the reconstructed point cloud.
  • FIG. 1 is an example of a point cloud image provided by an embodiment of the present application.
  • FIG. 2 is a partial enlarged view of the point cloud image shown in FIG. 1 .
  • FIG. 3 is an example of a point cloud image with six viewing angles provided by an embodiment of the present application.
  • FIG4 is a schematic block diagram of a coding framework provided in an embodiment of the present application.
  • FIG. 5 is an example of a bounding box provided in an embodiment of the present application.
  • FIG. 6 is an example of performing octree division on a bounding box provided by an embodiment of the present application.
  • FIG. 10 shows the arrangement order of Morton codes in three-dimensional space.
  • FIG. 11 is a schematic block diagram of the LOD layer provided in an embodiment of the present application.
  • FIG. 12 is a schematic block diagram of a decoding framework provided in an embodiment of the present application.
  • FIG13 is a schematic flowchart of a point cloud decoding method provided in an embodiment of the present application.
  • FIG. 14 is another schematic flowchart of the point cloud decoding method provided in an embodiment of the present application.
  • FIG. 15 is another schematic flowchart of the point cloud decoding method provided in an embodiment of the present application.
  • FIG16 is a schematic flowchart of the point cloud encoding method provided in an embodiment of the present application.
  • FIG. 17 is another schematic flowchart of the point cloud encoding method provided in an embodiment of the present application.
  • FIG. 18 is another schematic flowchart of the point cloud encoding method provided in an embodiment of the present application.
  • Figure 19 is a schematic block diagram of a decoder provided in an embodiment of the present application.
  • Figure 20 is a schematic block diagram of the encoder provided in an embodiment of the present application.
  • FIG. 21 is a schematic structural diagram of an electronic device provided in an embodiment of the present application.
  • Point Cloud is a set of irregularly distributed discrete points in space that express the spatial structure and surface properties of a three-dimensional object or three-dimensional scene.
  • Figures 1 and 2 show a three-dimensional point cloud image and a local magnified image, respectively. It can be seen that the point cloud surface is composed of densely distributed points.
  • each point in a point cloud has corresponding attribute information, usually RGB color value, which reflects the color of the object; for a point cloud, the attribute information corresponding to each point can be a reflectance value in addition to color, and the reflectance value reflects the surface material of the object.
  • Each point in a point cloud can include geometric information and attribute information, wherein the geometric information of each point in a point cloud refers to the Cartesian three-dimensional coordinate data of the point, and the attribute information of each point in a point cloud can include but is not limited to at least one of the following: color information, material information, and laser reflection intensity information.
  • Color information can be information in any color space.
  • color information can be red, green, and blue (RGB) information.
  • color information can also be brightness and chromaticity (YCbCr, YUV) information. Among them, Y represents brightness (Luma), Cb (U) represents the blue chromaticity component, and Cr (V) represents the red chromaticity component.
  • Each point in the point cloud has the same amount of attribute information.
  • each point in the point cloud has two types of attribute information: color information and laser reflection intensity.
  • each point in the point cloud has three types of attribute information: color information, material information, and laser reflection intensity information.
  • Point cloud images can have multiple viewing angles, for example, the point cloud image shown in Figure 3 can have six viewing angles.
  • the data storage format corresponding to the point cloud image consists of a file header information part and a data part.
  • the header information includes the data format, data representation type, the total number of point cloud points, and the content represented by the point cloud.
  • Point clouds can flexibly and conveniently express the spatial structure and surface properties of three-dimensional objects or scenes. Point clouds are obtained by directly sampling real objects, so they can provide a strong sense of reality while ensuring accuracy. Therefore, they are widely used, including virtual reality games, computer-aided design, geographic information systems, automatic navigation systems, digital cultural heritage, free viewpoint broadcasting, three-dimensional immersive remote presentation, and three-dimensional reconstruction of biological tissues and organs.
  • point clouds can be divided into two categories based on application scenarios, namely machine-perceived point clouds and human-perceived point clouds.
  • Application scenarios of machine-perceived point clouds include, but are not limited to, autonomous navigation systems, real-time inspection systems, geographic information systems, visual sorting robots, emergency rescue robots, and other application scenarios.
  • Application scenarios of human-perceived point clouds include, but are not limited to, digital cultural heritage, free viewpoint broadcasting, three-dimensional immersive communication, and other application scenarios.
  • point clouds can be divided into dense point clouds and sparse point clouds based on the acquisition method of point clouds; point clouds can also be divided into static point clouds and dynamic point clouds based on the acquisition method of point clouds, and more specifically, they can be divided into three types of point clouds, namely, the first static point cloud, the second type of dynamic point cloud, and the third type of dynamically acquired point cloud.
  • the first static point cloud the object is stationary, and the device that acquires the point cloud is also stationary
  • the second type of dynamic point cloud the object is moving, but the device that acquires the point cloud is stationary
  • the third type of dynamically acquired point cloud the device that acquires the point cloud is moving.
  • the acquisition methods of point clouds include, but are not limited to: computer generation, 3D laser scanning, 3D photogrammetry, etc.
  • Computers can generate point clouds of virtual three-dimensional objects and scenes;
  • 3D laser scanning can obtain point clouds of static real-world three-dimensional objects or scenes, and can obtain millions of point clouds per second;
  • 3D photogrammetry can obtain point clouds of dynamic real-world three-dimensional objects or scenes, and can obtain tens of millions of point clouds per second.
  • point clouds on the surface of objects can be acquired through acquisition equipment such as photoelectric radar, laser radar, laser scanner, multi-view camera, etc.
  • the point cloud obtained according to the principle of laser measurement may include the three-dimensional coordinate information of the point and the laser reflection intensity (reflectance) of the point.
  • the point cloud obtained according to the principle of photogrammetry may include the three-dimensional coordinate information of the point and the color information of the point.
  • the point cloud obtained by combining laser measurement and photogrammetry principles may include the three-dimensional coordinate information of the point, the laser reflection intensity (reflectance) of the point, and the color information of the point.
  • These technologies reduce the cost and time cycle of point cloud data acquisition and improve the accuracy of the data.
  • magnetic resonance imaging (MRI), computed tomography (CT), and electromagnetic positioning information can be used to obtain point clouds of biological tissues and organs.
  • MRI magnetic resonance imaging
  • CT computed tomography
  • electromagnetic positioning information can be used to obtain point clouds of biological tissues and organs.
  • These technologies reduce the cost and time of obtaining point clouds and improve the accuracy of data.
  • the change in the point cloud acquisition method has reduced the difficulty of obtaining point clouds.
  • the processing of massive 3D point clouds has encountered bottlenecks in storage space and transmission bandwidth.
  • Point cloud compression generally adopts the method of compressing point cloud geometry information and attribute information separately.
  • the point cloud geometry information is first encoded in the geometry encoder, and then the reconstructed geometry information is input into the attribute encoder as additional information to assist in the attribute compression of the point cloud;
  • the point cloud geometry information is first decoded in the geometry decoder, and then the decoded geometry information is input into the attribute decoder as additional information to assist in the attribute compression of the point cloud.
  • the entire codec consists of pre-processing/post-processing, geometry encoding/decoding, and attribute encoding/decoding.
  • the point cloud can be encoded and decoded by various types of encoding frameworks and decoding frameworks, respectively.
  • the encoding and decoding framework can be a geometry point cloud compression (G-PCC) encoding and decoding framework or a video point cloud compression (V-PCC) encoding and decoding framework provided by the Moving Picture Experts Group (MPEG), or it can be an AVS-PCC encoding and decoding framework or a point cloud compression reference platform (PCRM) framework provided by the Audio Video Standard (AVS) Task Force.
  • G-PCC geometry point cloud compression
  • V-PCC video point cloud compression
  • PCM point cloud compression reference platform
  • the G-PCC encoding and decoding framework can be used to compress the first static point cloud and the third type of dynamically acquired point cloud, and the V-PCC encoding and decoding framework can be used to compress the second type of dynamic point cloud.
  • the G-PCC encoding and decoding framework is also called Test Model 13 (TMC13), and the V-PCC encoding and decoding framework is also called Test Model 2 (TMC2).
  • TMC13 Test Model 13
  • TMC2 Test Model 2
  • G-PCC and AVS-PCC are frameworks for static sparse point clouds, and their encoding and decoding frameworks are roughly the same.
  • the following uses the G-PCC framework as an example to illustrate the coding and decoding framework applicable to the embodiments of the present application.
  • the input point cloud is first divided into slices, and then the slices obtained by the division are independently encoded.
  • the geometric information of the point cloud and the attribute information corresponding to the points in the point cloud are encoded separately.
  • the G-PCC coding framework first encodes the geometric information; specifically, the coordinates of the geometric information are first transformed so that all the point clouds are contained in a bounding box; then quantization is performed. This step of quantization mainly plays a role in scaling. Due to the quantization rounding, the geometric information of some points is the same. Whether to remove duplicate points is determined based on parameters. The process of quantization and removal of duplicate points is also called the "voxelization process".
  • the bounding box is divided based on the octree.
  • the encoding of geometric information is divided into a geometric information encoding framework based on the octree and a geometric information encoding framework based on the triangle soup (trisoup).
  • the bounding box is first divided into eight equal parts to obtain eight sub-cubes, and then the placeholder bits of the sub-cubes are recorded (1 for non-empty, 0 for empty), and the non-empty sub-cubes are further divided into eight equal parts.
  • the division is stopped when the leaf node obtained by the division is a 1x1x1 unit cube.
  • the placeholder bits are predicted (prediction) using the spatial correlation between the node and the surrounding nodes, and the corresponding binary arithmetic encoder is selected based on the prediction result for arithmetic encoding, so as to implement context-based adaptive binary arithmetic coding (CABAC) and generate a binary code stream.
  • CABAC context-based adaptive binary arithmetic coding
  • octree division In the geometric information coding framework based on triangle face sets, octree division must also be performed first. However, unlike the geometric information coding framework based on octree, the geometric information coding framework based on triangle face sets does not need to divide the point cloud into unit cubes with a side length of 1x1x1 step by step. Instead, the division is stopped when the block has a side length of W. Based on the surface formed by the distribution of the point cloud in each block, at most twelve intersections (vertexes) generated by the surface and the twelve edges of the block are obtained, and then the coordinates of the intersection of each block are encoded in turn to generate a binary code stream.
  • the G-PCC coding framework After completing the geometric information encoding, the G-PCC coding framework reconstructs the geometric information and uses the reconstructed geometric information to encode the attribute information of the point cloud.
  • the attribute encoding of the point cloud mainly encodes the color information of the point in the point cloud.
  • the G-PCC coding framework can perform color space conversion on the color information of the point. For example, when the color information of the point in the input point cloud is represented by the RGB color space, the G-PCC coding framework can convert the color information from the RGB color space to the YUV color space. Then, the G-PCC coding framework uses the reconstructed geometric information to recolor the point cloud so that the unencoded attribute information corresponds to the reconstructed geometric information.
  • FIG4 is a schematic block diagram of a coding framework provided in an embodiment of the present application.
  • the coding framework 100 can obtain geometric information and attribute information of the point cloud from the acquisition device.
  • the coding of the point cloud includes geometric coding and attribute coding.
  • the process of geometric coding includes: performing coordinate transformation, quantization and removal of duplicate points on the point cloud obtained from the acquisition device; and encoding based on octree division to form a geometric code stream.
  • the position encoding process of the encoder can be implemented by the following units:
  • Coordinate transformation transformation (Tanmsform coordinates) unit 101, quantization and removal of duplicate points (Quantize and remove points) unit 102, octree analysis (Analyze octree) unit 103, geometry reconstruction (Reconstruct geometry) unit 104 and first arithmetic encoding (Arithmetic encode) unit 105.
  • the coordinate transformation unit 101 can be used to transform the world coordinates of the points in the point cloud into relative coordinates. For example, the geometric coordinates of the points are respectively subtracted from the minimum value of the xyz coordinate axis, which is equivalent to a DC removal operation, so as to realize the transformation of the coordinates of the points in the point cloud from the world coordinates to the relative coordinates, and make the point cloud all contained in a bounding box.
  • the quantization and removal of duplicate points unit 102 can reduce the number of coordinates by quantization; after quantization, the originally different points may be assigned the same coordinates, based on which, the duplicate points can be deleted by deduplication operation; for example, multiple points with the same quantization position and different attribute information can be merged into one point through attribute transformation.
  • the quantization and removal of duplicate points unit 102 is an optional unit module.
  • the octree analysis unit 103 can encode the geometric information of the quantized points using an octree encoding method.
  • the point cloud is regularized in the form of an octree, so that the position of the point can correspond to the position of the octree one by one, and the position of the point in the octree is counted and its flag is recorded as 1 to perform geometric encoding.
  • the first arithmetic coding unit 105 can use entropy coding to perform arithmetic coding on the geometric information output by the octree analysis unit 103, that is, to generate a geometric code stream using arithmetic coding of the geometric information output by the octree analysis unit 103; the geometric code stream can also be called a geometry bit stream (geometry bit stream).
  • the boundary values of the point cloud in the x-axis, y-axis and z-axis directions are:
  • x min min(x 0 ,x 1 ,...,x K-1 );
  • y min min(y 0 ,y 1 ,...,y K-1 );
  • z min min(z 0 ,z 1 ,...,z K-1 );
  • xmax max( x0 , x1 ,..., xK-1 );
  • y max max(y 0 ,y 1 ,...,y K-1 );
  • z max max(z 0 ,z 1 ,...,z K-1 ).
  • origin of the bounding box (x origin , y origin , z origin ) can be calculated as follows:
  • floor() represents floor operation or rounding down operation.
  • int() represents integer operation.
  • the encoder can calculate the size of the bounding box in the x-axis, y-axis, and z-axis directions based on the calculation formula of the boundary value and the origin as follows:
  • the encoder After the encoder obtains the size of the bounding box in the x-axis, y-axis and z-axis directions, it first divides the bounding box into octrees to obtain eight sub-blocks each time, and then divides the non-empty blocks (blocks containing points) in the sub-blocks into octrees again, and recursively divides them until a certain depth.
  • the non-empty sub-blocks of the final size are called voxels.
  • Each voxel contains one or more points, and the geometric positions of these points are normalized to the center point of the voxel.
  • the attribute value of the center point can be the average of the attribute values of all points in the voxel.
  • the encoder can encode each voxel based on the determined encoding order, that is, encode the point (or node) represented by each voxel.
  • the encoder After the encoder completes the geometric encoding, it reconstructs the geometric information and uses the reconstructed geometric information to encode the attribute information.
  • the attribute encoding process includes: given the reconstruction information of the input point cloud's geometric information and the true value of the attribute information, selecting one of the three prediction modes for point cloud prediction, quantizing the predicted results, and performing arithmetic coding to form an attribute code stream.
  • the attribute encoding process of the encoder can be implemented by the following units:
  • a color space transform (Transform colors) unit 110 an attribute transform (Transfer attributes) unit 111, a Region Adaptive Hierarchical Transform (RAHT) unit 112, a predicting transform (predicting transform) unit 113, a lifting transform (lifting transform) unit 114, a quantization (Quantize) unit 115 and a second arithmetic coding unit 116.
  • RAHT Region Adaptive Hierarchical Transform
  • the color space transformation unit 110 can be used to transform the RGB color space of the points in the point cloud into a YCbCr format or other formats.
  • the attribute transformation unit 111 can be used to transform the attribute information of the points in the point cloud to minimize the attribute distortion.
  • the attribute transformation unit 111 is required to reallocate the attribute value to each point after the geometry coding so that the attribute error between the reconstructed point cloud and the point cloud before the reconstruction is minimized.
  • the attribute information can be the color information of the point.
  • the attribute transformation unit 111 can be used to obtain the original attribute value of the point.
  • any prediction unit can be selected to predict the points in the point cloud.
  • the unit for predicting points in the point cloud may include at least one of: RAHT 112, a predicting transform unit 113, and a lifting transform unit 114.
  • any one of the RAHT 112, the predicting transform unit 113, and the lifting transform unit 114 can be used to predict the attribute information of the point in the point cloud to obtain the attribute prediction value of the point, and then the residual value of the attribute information of the point can be obtained based on the attribute prediction value of the point.
  • the residual value of the attribute information of the point can be the original value of the attribute of the point minus the attribute prediction value of the point.
  • the quantization unit 115 can be used to quantize the residual value of the attribute information of the point. For example, if the quantization unit 115 is connected to the predicting transform unit 113, the quantization unit 115 can be used to quantize the residual value of the attribute information of the point output by the predicting transform unit 113. For example, the residual value of the attribute information of the point output by the predicting transform unit 113 is quantized using a quantization step size to improve system performance.
  • the second arithmetic coding unit 116 can use zero run length coding to entropy encode the residual value of the attribute information of the point to obtain an attribute code stream.
  • the attribute code stream can be bit stream information.
  • the prediction transformation unit 113 can be used to obtain the original order of the point cloud and divide the point cloud into detail layers (level of detail, LOD) based on the original order of the point cloud. After the prediction transformation unit 113 obtains the LOD of the point cloud, the attribute information of the points in the LOD can be predicted in sequence, and then the residual value of the attribute information of the points can be calculated, so that the subsequent units can perform subsequent quantization encoding processing based on the residual value of the attribute information of the points.
  • detail layers level of detail, LOD
  • the three neighboring points located before the current point are found based on the neighboring point search results on the LOD where the current point is located, and then the attribute reconstruction value of at least one of the three neighboring points is used to predict the current point to obtain the attribute prediction value of the current point; based on this, the residual value of the attribute information of the current point can be obtained based on the attribute prediction value of the current point and the original attribute value of the current point.
  • the original order of the point cloud acquired by the prediction transformation unit 113 may be the arrangement order obtained by performing Morton sorting on the current point cloud by the prediction transformation unit 113.
  • the points in the point cloud may be divided into layers according to the original order of the current point cloud to obtain the LOD of the current point cloud, and then the attribute information of the points in the point cloud may be predicted based on the LOD.
  • the encoder can use a “z”-shaped Morton arrangement order in the two-dimensional space formed by 2*2 blocks.
  • the encoder can use a “z”-shaped Morton arrangement order in the two-dimensional space formed by 4 2*2 blocks, wherein the “z”-shaped Morton arrangement order can also be used in the two-dimensional space formed by each 2*2 block, and finally the Morton arrangement order used by the encoder in the two-dimensional space formed by 4*4 blocks can be obtained.
  • FIG. 8 the encoder can use a “z”-shaped Morton arrangement order in the two-dimensional space formed by 4 2*2 blocks.
  • the encoder can use a “z”-shaped Morton arrangement order in the two-dimensional space formed by 4 4*4 blocks, wherein the “z”-shaped Morton arrangement order can also be used in the two-dimensional space formed by each 4 2*2 blocks and the two-dimensional space formed by each 2*2 block, and finally the Morton arrangement order used by the encoder in the two-dimensional space formed by 8*8 blocks can be obtained.
  • FIG. 10 shows the arrangement order of Morton codes in three-dimensional space.
  • the Morton arrangement order is not only applicable to two-dimensional space, but can also be extended to three-dimensional space.
  • Figure 10 shows 16 points. Inside each "z”, the Morton arrangement order between each "z” and "z” is first encoded along the x-axis, then along the y-axis, and finally along the z-axis.
  • the Euclidean distance between points may be obtained according to the geometric information of the points in the point cloud; and then the points may be divided into different LODs according to the Euclidean distance between the points.
  • the Euclidean distances between points can be sorted, and the Euclidean distances in different ranges can be divided into different LODs.
  • a point can be randomly selected as the first LOD; then the Euclidean distances between the remaining points and the point are calculated, and the points whose Euclidean distances meet the first threshold requirement are classified as the second LOD; then, the centroid of the points in the second LOD is obtained, and the Euclidean distances between the points other than the first LOD and the second LOD and the centroid are calculated, and the points whose Euclidean distances meet the second threshold are classified as the third LOD; and so on, all points are classified into the LOD layer. Furthermore, by adjusting the threshold of the Euclidean distance, the number of points in each layer of LOD can be increased.
  • the point cloud may be divided into refinement layers based on different Euclidean distances, and then the LOD layer may be obtained based on the refinement layers.
  • the LOD layer division method can also be other methods, and this application does not limit this.
  • the point cloud can be directly divided into one or more LOD layers, or the point cloud can be first divided into multiple point cloud slices, and then each point cloud slice can be divided into one or more LOD layers.
  • the point cloud can be divided into multiple point cloud slices, and the number of points in each point cloud slice can be between 550,000 and 1.1 million.
  • Each point cloud slice can be regarded as a separate point cloud.
  • Each point cloud slice can be divided into multiple LOD layers, and each LOD layer includes multiple points.
  • FIG. 11 is a schematic block diagram of the LOD layer provided in an embodiment of the present application.
  • the point cloud may include a plurality of points arranged in an original order, namely, P0, P1, P2, P3, P4, P5, P6, P7, P8, and P9. It is assumed that the point cloud is divided into three LOD layers, namely, LOD0, LOD1, and LOD2, based on the Euclidean distance between points.
  • LOD0 may include P0, P5, P4, and P2
  • LOD1 may include points in LOD0
  • LOD2 may include points in LOD0, points in LOD1, P9, P8, and P7.
  • LOD0, LOD1, and LOD2 may be used to form a LOD-based order of the point cloud, namely, P0, P5, P4, P2, P1, P6, P3, P9, P8, and P7.
  • the LOD-based order may be used as the encoding order of the point cloud.
  • the encoder when predicting the attribute information of the current point, the encoder first generates one or more refinement layers based on the original order of the point cloud, then searches for the nearest neighbor of the current point from the previously encoded refinement layers according to the generation order of the refinement layers, and predicts the attribute information of the current point based on the nearest neighbor found to obtain the attribute prediction value of the current point.
  • the encoder may first create multiple candidate prediction values based on the search results of the neighboring points of the current point, and then use the rate distortion optimization (RDO) technology to select the attribute prediction value with the best prediction performance for the current point from the multiple candidate prediction values, and then use the best attribute prediction value as the attribute prediction value of the current point, and encode the attribute information by subtracting it from the original attribute value to obtain the attribute code stream of the point cloud.
  • RDO rate distortion optimization
  • the encoder may first use multiple prediction modes (predMode) to create multiple candidate prediction values based on the search results of neighboring points of the current point.
  • predMode multiple prediction modes
  • the index values of the multiple prediction modes can be 0 to 3.
  • predMode the index values of the multiple prediction modes
  • the prediction mode with an index of 0 refers to determining the weighted average of the reconstructed attribute values of the three neighbor points as the candidate prediction value of the current point based on the distance between the three neighbor points and the current point;
  • the prediction mode with an index of 1 refers to: using the attribute reconstruction value of the nearest neighbor point among the three neighbor points as the candidate prediction value of the current point;
  • the prediction mode with an index of 2 refers to: using the attribute reconstruction value of the second nearest neighbor point as the candidate prediction value of the current point;
  • the prediction mode with an index of 3 refers to: using the attribute reconstruction value of the neighbor points other than the nearest neighbor point and the second nearest neighbor point among the three neighbor points as the candidate prediction value of the current point; after obtaining multiple candidate prediction values for the current point based on the above-mentioned various prediction modes, the rate distortion optimization (RDO) technology can be used to select the best attribute prediction value, and then the selected best attribute prediction value is used as the attribute prediction value of the current point for subsequent encoding.
  • RDO rate
  • the prediction mode with an index of 0 means: the weighted average of the distances of the neighboring points P0, P5 and P4 and the reconstructed attribute values of the neighboring points P0, P5 and P4 is determined as the candidate prediction value of the current point P2;
  • the prediction mode with an index of 1 means: the attribute reconstruction value of the nearest neighbor point P4 is determined as the candidate prediction value of the current point P2;
  • the prediction mode with an index of 2 means: the attribute reconstruction value of the next nearest neighbor point P5 is determined as the candidate prediction value of the current point P2;
  • the prediction mode with an index of 3 means: the attribute reconstruction value of the three nearest neighbor points P0 is determined as the candidate prediction value of the current point P2; finally, RDO is used to select the best attribute prediction value for the current point P2.
  • the index of the prediction model used at the current point may or may not be written into the bitstream.
  • the index of the prediction model used at the current point is 0, the index of the prediction model used at the current point does not need to be encoded in the bitstream.
  • the index of the prediction model used at the current point is the index of the prediction mode selected by RDO, which is 1, 2 or 3, the index of the prediction model used at the current point needs to be encoded in the bitstream, that is, the index of the selected prediction mode needs to be encoded into the attribute bitstream.
  • the encoder when the encoder uses the RDO technology to select the best attribute prediction value, it can first calculate the maximum attribute difference maxDiff of at least one neighboring point of the current point, compare maxDiff with the set threshold, and if it is less than the set threshold, use the prediction mode of weighted average of the attribute values of the neighboring points; otherwise, use the RDO technology to select the optimal prediction mode for this point. Specifically, the encoder calculates the maximum attribute difference maxDiff of at least one neighboring point of the current point.
  • the encoder can calculate the corresponding rate-distortion cost for each prediction mode of the current point, and then select the prediction mode with the smallest rate-distortion cost, that is, the optimal prediction mode as the attribute prediction mode of the current point.
  • ⁇ is determined according to the quantization parameter of the current point
  • R indx_i represents the number of bits required in the bitstream for the quantized residual value obtained when the current point adopts the prediction mode with index i.
  • the encoder determines the best attribute prediction value attrPred for the current point, it can subtract the attribute original value attrValue of the current point from the attribute prediction value attrPred of the current point and quantize the result to obtain the quantization residual value attrResidualQuant of the current point.
  • attrResidualQuant (attrValue-attrPred)/Qs; wherein attrResidualQuant represents the quantization residual value of the current point, attrValue represents the attribute original value of the current point, attrPred represents the attribute prediction value of the current point, and Qstep represents the quantization step size.
  • Qstep is calculated by the quantization parameter (Quantization Parameter, Qp).
  • the current point can be used as a neighbor point of the subsequent point, that is, the attribute reconstruction value of the current point can be used to predict the attribute information of the subsequent point.
  • Qstep is calculated by the quantization parameter (Quantization Parameter, Qp).
  • the predicted value of the attribute of the current point may also be referred to as the predicted value of the attribute information or the predicted color value (predictedColor).
  • the original value of the attribute of the current point may also be referred to as the original value of the attribute information of the current point or the original color value.
  • the residual value of the current point may also be referred to as the difference between the original value of the attribute of the current point and the predicted value of the attribute of the current point, or may also be referred to as the color residual value (residualColor) of the current point.
  • the reconstructed value of the attribute of the current point (reconstructedvalue) may also be referred to as the reconstructed value of the attribute information of the current point, or may be referred to as the color reconstructed value (reconstructedColor).
  • FIG. 12 is a schematic block diagram of a decoding framework 200 provided in an embodiment of the present application.
  • the geometric information and attribute information of the points in the point cloud can be obtained by parsing the code stream.
  • the decoding of the point cloud includes position decoding and attribute decoding.
  • the process of position decoding includes: performing arithmetic decoding on the geometric code stream; then constructing an octree structure in the same way as geometric encoding, and obtaining reconstruction information of the geometric information based on the constructed octree structure; then, performing coordinate transformation on the reconstruction information of the geometric information of the point to obtain the transformed geometric information.
  • the geometric information of the point can also be called the position information of the point.
  • the attribute decoding process includes: obtaining the attribute residual value of the point in the point cloud by parsing the attribute code stream; dequantizing the attribute residual value of the point to obtain the dequantized attribute residual value of the point; based on the reconstruction information of the geometric information of the point obtained during the position decoding process, selecting one of the three prediction modes for point cloud prediction to obtain the attribute prediction value of the point, and determining the attribute reconstruction value of the point based on the dequantized attribute residual value of the point and the attribute prediction value of the point; then obtaining the decoded attribute information through color space inverse transformation.
  • position decoding can be implemented by the following units: a first arithmetic decoding unit 201, an octree synthesis unit 202, a geometry reconstruction unit 203, and an inverse transform coordinates unit 204.
  • Attribute encoding can be implemented by the following units: a second arithmetic decoding unit 210, an inverse quantize unit 211, a RAHT unit 212, a predicting transform unit 213, a lifting transform unit 214, and an inverse transform colors unit 215. It should be noted that decompression is the inverse process of compression.
  • the functions of each unit in the decoding framework 200 can refer to the functions of the corresponding units in the encoding framework 100.
  • the predicting transform unit 213 can divide the point cloud into multiple LODs according to the Euclidean distance between points in the point cloud; then, the attribute information of the points in the LODs is decoded in turn.
  • the second arithmetic decoding unit 210 may be used to parse the number of zeros (zero_cnt) in the zero run encoding technique to decode the residual based on the number of zeros. To avoid repetition, it will not be described here.
  • the encoder predicts the attribute information of the current point, it first generates one or more refinement layers based on the original order of the point cloud, and then searches for the nearest neighbor of the current point from the previously encoded refinement layer according to the generation order of the refinement layer, and predicts the attribute information of the current point based on the nearest neighbor found to obtain the attribute prediction value of the current point.
  • the attribute prediction value of the point in the current refinement layer will depend on the attribute reconstruction value of the point in the encoded refinement layer, and this dependency relationship will inevitably propagate the distortion of the previously encoded point to the current point, which will eventually lead to error accumulation and reduce the encoding performance.
  • the present application provides a point cloud decoding method that can improve decoding performance and enhance the quality of the reconstructed point cloud.
  • FIG13 is a schematic flow chart of a point cloud decoding method 310 provided in an embodiment of the present application. It should be understood that the point cloud decoding method 310 can be executed by a decoder or a decoding framework. For example, it is applied to the decoding framework 200 shown in FIG12. For ease of description, the following description is made by taking a decoder as an example.
  • the point cloud decoding method 310 may include:
  • the decoder decodes the bit stream of the point cloud and determines the residual value of the attribute information of the target point in the point cloud;
  • the decoder determines an initial reconstructed value of the attribute information of the target point based on the residual value of the attribute information of the target point and the predicted value of the attribute information of the target point;
  • the decoder determines the filter coefficient of the target point
  • S314 The decoder filters the initial reconstructed value of the attribute information of the target point based on the filter coefficient of the target point to determine the reconstructed value of the attribute information of the target point.
  • the decoder filters the initial reconstruction value of the attribute information of the target point based on the filter coefficient of the target point to obtain a filtered reconstruction value, and uses the filtered reconstruction value to cover or replace the initial reconstruction value of the attribute information of the target point.
  • the decoder can use the updated reconstructed values to predict the attribute information of subsequent points.
  • the filter coefficient of the target point may be information carried in the bitstream or a parameter determined by a decoder, and this application does not limit this.
  • the decoder decodes the code stream of the point cloud to determine the filter coefficient of the target point in the point cloud; and based on the filter coefficient of the target point, the initial reconstruction value of the attribute information of the target point is filtered to determine the reconstruction value of the attribute information of the target point.
  • the accuracy of the reconstruction value of the attribute information of the target point can be improved, thereby improving the decoding performance and achieving the enhancement of the quality of the reconstructed point cloud.
  • Table 2 shows the bit distortion (BD-rate) under lossless geometry and near lossless attribute coding.
  • BD-Rate reflects the difference in PSNR curves under the two conditions (with or without filtering). When BD-Rate decreases, it means that when PSNR is equal, the bit rate decreases and the performance improves; otherwise, the performance decreases. That is, the more BD-Rate decreases, the better the compression effect.
  • Cat1-A represents a point cloud that includes only the reflectance information of the point, and Cat1-A average represents the average BD-rate of each component of Cat1A in the geometric lossless and attribute near-lossless coding mode
  • Cat1-B represents a point cloud that includes only the color information of the point, and Cat1-B average represents the average BD-rate of each component of Cat1-B in the geometric lossless and attribute near-lossless coding mode
  • Cat3-fused represents a point cloud that includes the color information of the point and other attribute information.
  • Cat3-fused average represents the average BD-rate of each component of Cat3-fused in the geometric lossless and attribute near-lossless coding mode; Overall average represents the average BD-rate of Cat1-A to Cat3-fused in the geometric lossless and attribute near-lossless coding mode.
  • L, Cb and Cr also called Y, U, V represent the performance of the three components of brightness and chrominance of the point cloud color.
  • the solution provided by this application has obvious performance improvements for Cat1-A, Cat1-B and Cat3-fused. Specifically, compared with the original solution before improvement, the solution provided by this application obtains gains of -0.0%, -4.4%, and -6.5% on the Y, Cb, and Cr components, respectively.
  • the reconstructed value of the attribute information of the target point involved in the embodiment of the present application may be the final reconstructed value of the target point.
  • the decoder filters the initial reconstructed value of the attribute information of the target point based on the filter coefficient of the target point, and the reconstructed value of the attribute information of the target point determined is the final reconstructed value of the target point.
  • the final reconstructed value of the target point can be understood as the value of the attribute information of the target point in the decoded point cloud.
  • S313 may include:
  • the decoder determines at least one refinement layer based on the target refinement layer where the target point is located; the refinement layer includes one or more points; determines the filter coefficient corresponding to the at least one refinement layer; the decoder determines the filter coefficient of any point in the at least one refinement layer based on the filter coefficient corresponding to the at least one refinement layer.
  • the structure of the code stream may be as follows:
  • n the total number of points in the point cloud
  • m the number of refinement layers in the point cloud that have been subjected to Wiener filtering
  • k the length of the filter coefficients corresponding to each refinement layer (also called the order of the Wiener filter, or the length of the Wiener filter).
  • bits, flagbits, and coefbits are the residual values of the attribute information of the points in the point cloud, the filter flags corresponding to each refinement layer, and the filter coefficients corresponding to each refinement layer, respectively.
  • the decoder first decodes the code stream to determine the reconstructed value of the geometric information of the point cloud; then, the decoder divides the point cloud into L refinement layers based on the reconstructed value of the geometric information of the point cloud, and the refinement layer includes one or more points, and L is an integer greater than 0; then, the decoder determines the target refinement layer based on the index of the target point.
  • the decoder may divide the point cloud into L refinement layers based on different Euclidean distances, and then obtain L LOD layers based on the refinement layers.
  • the LOD layer division method can also be other methods, and this application does not limit this.
  • the point cloud can be directly divided into one or more LOD layers, or the point cloud can be first divided into multiple point cloud slices, and then each point cloud slice can be divided into one or more LOD layers.
  • the point cloud can be divided into multiple point cloud slices, and the number of points in each point cloud slice can be between 550,000 and 1.1 million.
  • Each point cloud slice can be regarded as a separate point cloud.
  • Each point cloud slice can be divided into multiple LOD layers, and each LOD layer includes multiple points.
  • the decoder may determine at least one refinement layer based on the index of the target point and the number of points in each refinement layer and based on the target refinement layer where the target point is located.
  • the decoder decodes the bitstream and determines a filter identifier corresponding to the at least one refinement layer; the filter identifier corresponding to the at least one refinement layer indicates a position of a filter coefficient corresponding to the at least one refinement layer in the bitstream; then, the decoder decodes the bitstream based on the filter identifier corresponding to the at least one refinement layer and determines a filter coefficient corresponding to the at least one refinement layer.
  • the filter identifier corresponding to the at least one refinement layer indicates the position of the filter coefficient corresponding to the at least one refinement layer in the bitstream by indicating the identifier of the at least one refinement layer.
  • the structure of the code stream can be as follows: Flagbits_0, coefbits_0[k], ..., Flagbits_m-1, coefbits_m-1[k], bits_0, ..., bits_n-2, bits_n-1.
  • n represents the total number of points in the point cloud
  • m represents the number of refinement layers in the point cloud that have been subjected to Wiener filtering
  • k represents the length of the filter coefficient corresponding to each refinement layer (also called the order of the Wiener filter, or the length of the Wiener filter).
  • k 16.
  • bits, flagbits, coefbits are respectively the residual value of the attribute information of the point in the point cloud, the filter identifier corresponding to each refinement layer, and the filter coefficient corresponding to each refinement layer; in this case, the filter identifier corresponding to the at least one refinement layer indicates the identifier of the at least one refinement layer, which is equivalent to indicating that the filter coefficient corresponding to the at least one refinement layer is the filter coefficient located after the filter identifier corresponding to the at least one refinement layer in the code stream.
  • Flagbits_0 indicates the identifier of the at least one refinement layer
  • coefbits_0[k] following Flagbits_0 may default to the filter coefficient corresponding to the refinement layer indicated by Flagbits_0.
  • the filter identifier corresponding to the at least one refinement layer indicates whether the filter coefficient corresponding to the at least one refinement layer is carried. For example, if the code stream carries the filter identifier corresponding to the at least one refinement layer, it means that the code stream carries the filter coefficient corresponding to the at least one refinement layer. If the code stream does not carry the filter identifier corresponding to the at least one refinement layer, it means that the code stream does not carry the filter coefficient corresponding to the at least one refinement layer.
  • the filter identifier corresponding to the at least one refinement layer can indicate whether to filter the at least one refinement layer; for example, the decoder can adopt a method similar to the encoder to determine the filter coefficient corresponding to the at least one refinement layer.
  • the filter identifier corresponding to the at least one refinement layer can indicate whether to filter the at least one refinement layer. This application does not make specific limitations on this.
  • the decoder sets the filter coefficient of any point in the at least one refinement layer to be equal to the filter coefficient corresponding to the at least one refinement layer.
  • the filter coefficients of all points in the at least one refinement layer are the same filter coefficients.
  • the filter coefficients of each point in the at least one refinement layer may also be carried in the code stream, and this application does not make any specific limitation to this.
  • the at least one refinement layer includes the first N refinement layers arranged in the order of generation of the refinement layers, or the at least one refinement layer includes one or more refinement layers located after the first N refinement layers in the order of generation of the refinement layers, and N is an integer greater than 0.
  • this embodiment merges and filters the first N refinement layers, and overwrites the filtered values with the initial reconstruction values to obtain updated reconstruction values, which can improve filtering accuracy and decoding efficiency.
  • the amount of calculation of the filter coefficients is reduced, and the coding efficiency is improved.
  • the value of N may be 6 or other values, which is not specifically limited in this application.
  • the at least one refinement layer includes a single refinement layer that is located before the last refinement layer and after the first N refinement layers in the generation order of the refinement layers.
  • this embodiment performs independent filtering on the single refinement layer, which can improve filtering accuracy.
  • FIG. 14 is a schematic flowchart of a point cloud decoding method 320 provided in an embodiment of the present application.
  • the point cloud decoding method 320 may include:
  • the decoder decodes the bit stream of the point cloud, determines the residual value of the attribute information of the target point in the point cloud, and predicts the attribute information of the target point to obtain the predicted value of the attribute information of the target point.
  • the decoder when predicting the attribute information of the target point, the decoder first generates one or more refinement layers based on the original order of the point cloud, then searches for the nearest neighbor of the target point from the previously encoded refinement layers according to the generation order of the refinement layers, and predicts the attribute information of the target point based on the nearest neighbor found to obtain the attribute prediction value of the target point.
  • the decoder may first create multiple candidate prediction values based on the search results of the neighbor points of the target point, and then use the rate distortion optimization (RDO) technology to select the attribute prediction value with the best prediction performance for the target point from the multiple candidate prediction values, and then use the best attribute prediction value as the attribute prediction value of the target point, and encode the attribute information by subtracting it from the original attribute value to obtain the attribute code stream of the point cloud.
  • RDO rate distortion optimization
  • the decoder may first use multiple prediction modes (predMode) to create multiple candidate prediction values based on the search results of neighboring points of the target point.
  • predMode multiple prediction modes
  • the index values of the multiple prediction modes can be 0 to 3.
  • the decoder When the decoder encodes the attribute information of the target point, the decoder first finds the three neighbor points before the target point based on the neighbor point search results on the LOD where the target point is located, and then obtains multiple candidate prediction values based on the prediction mode with an index value of 0 to 3.
  • the prediction mode with index 0 means that the weighted average of the reconstructed attribute values of the three neighbor points is determined as the candidate prediction value of the target point based on the distance between the three neighbor points and the target point;
  • the prediction mode with index 1 means: the attribute reconstruction value of the nearest neighbor point among the three neighbor points is used as the candidate prediction value of the target point;
  • the prediction mode with index 2 means: the attribute reconstruction value of the second nearest neighbor point is used as the candidate prediction value of the target point;
  • the prediction mode with index 3 means: the attribute reconstruction values of the neighbor points other than the nearest neighbor point and the second nearest neighbor point among the three neighbor points are used as the candidate prediction value of the target point; after obtaining multiple candidate prediction values of the target point based on the above-mentioned various prediction modes, the rate distortion optimization (RDO) technology can be used to select the best attribute prediction value, and then the selected best attribute prediction value is used as the attribute prediction value of the target point for subsequent encoding.
  • RDO rate distortion optimization
  • the implementation method of the decoder predicting the target point can refer to the relevant content involved above, and will not be repeated here to avoid repetition.
  • the decoder determines an initial reconstructed value of the attribute information of the target point based on the residual value of the attribute information of the target point and the predicted value of the attribute information of the target point.
  • Qstep is calculated by a quantization parameter (Quantization Parameter, Qp).
  • the decoder decodes the code stream, determines the filter coefficient corresponding to R7 , and sets the filter coefficient of the target point to be equal to the filter coefficient corresponding to R7 .
  • the decoder filters the initial reconstructed value of the attribute information of the target point based on the filter coefficient of the target point, and determines the reconstructed value of the attribute information of the target point for prediction of the attribute information of subsequent points.
  • the decoder decodes the filter coefficient corresponding to R 7 , and uses the filter coefficient corresponding to R 7 to filter the initial reconstruction value of the attribute information of the midpoint of R 7 , and the filtered reconstruction value is used to cover the initial reconstruction value to obtain an updated reconstruction value.
  • the reconstruction value of the midpoint of LOD m is used to predict the attribute information of the midpoint of the refined layer after R 7 and the predicted value of the attribute information of the midpoint of the refined layer after R 7 is obtained.
  • the decoder may repeat the above process until the filtering of the penultimate refinement layer is also completed and the initial reconstruction value of the point in the penultimate refinement layer is overwritten with the filtered value, so that the filtering of the points in the point cloud is completed.
  • the decoder decodes the code stream of the point cloud to determine the filter coefficient of the target point in the point cloud; and based on the filter coefficient of the target point, the initial reconstruction value of the attribute information of the target point is filtered to determine the reconstructed value of the attribute information of the target point.
  • the initial reconstruction value of the attribute information of the target point is filtered to determine the reconstructed value of the attribute information of the target point.
  • this embodiment merges and filters the first N refinement layers, and overwrites the filtered values with the initial reconstruction values to obtain updated reconstruction values, which can improve filtering accuracy and decoding efficiency.
  • FIG. 15 is a schematic flowchart of a point cloud decoding method 330 provided in an embodiment of the present application.
  • the point cloud decoding method 330 may include:
  • the decoder obtains the code stream of the point cloud.
  • the decoder decodes the code stream to obtain a residual value of the attribute information of the current point in Rm .
  • the decoder decodes the code stream to obtain the filter coefficient corresponding to R m .
  • the decoder predicts the attribute information of the current point and obtains its predicted value.
  • the decoder when predicting the attribute information of the current point, the decoder first generates one or more refinement layers based on the original order of the point cloud, then searches for the nearest neighbor of the current point from the previously encoded refinement layers according to the generation order of the refinement layers, and predicts the attribute information of the current point based on the nearest neighbor found to obtain the attribute prediction value of the current point.
  • the decoder may first create multiple candidate prediction values based on the search results of the neighboring points of the current point, and then use the rate distortion optimization (RDO) technology to select the attribute prediction value with the best prediction performance for the current point from the multiple candidate prediction values, and then use the best attribute prediction value as the attribute prediction value of the current point, and encode the attribute information by subtracting it from the original attribute value to obtain the attribute code stream of the point cloud.
  • RDO rate distortion optimization
  • the decoder may first use multiple prediction modes (predMode) to create multiple candidate prediction values based on the search results of neighboring points of the current point.
  • predMode multiple prediction modes
  • the index values of the multiple prediction modes can be 0 to 3.
  • the decoder When the decoder encodes the attribute information of the current point, the decoder first finds the three neighbor points before the current point based on the neighbor point search results on the LOD where the current point is located, and then obtains multiple candidate prediction values based on the prediction mode with an index value of 0 to 3.
  • the prediction mode with an index of 0 refers to determining the weighted average of the reconstructed attribute values of the three neighbor points as the candidate prediction value of the current point based on the distance between the three neighbor points and the current point;
  • the prediction mode with an index of 1 refers to: using the attribute reconstruction value of the nearest neighbor point among the three neighbor points as the candidate prediction value of the current point;
  • the prediction mode with an index of 2 refers to: using the attribute reconstruction value of the second nearest neighbor point as the candidate prediction value of the current point;
  • the prediction mode with an index of 3 refers to: using the attribute reconstruction value of the neighbor points other than the nearest neighbor point and the second nearest neighbor point among the three neighbor points as the candidate prediction value of the current point; after obtaining multiple candidate prediction values for the current point based on the above-mentioned various prediction modes, the rate distortion optimization (RDO) technology can be used to select the best attribute prediction value, and then the selected best attribute prediction value is used as the attribute prediction value of the current point for subsequent encoding.
  • RDO rate
  • the decoder determines the initial reconstruction value of the attribute information of the current point.
  • Qstep is calculated by the quantization parameter (Quantization Parameter, Qp).
  • the decoder determines that the current point is the last point in R m .
  • the decoder filters the initial reconstructed value of the attribute information of the point in R m based on the filter coefficient corresponding to R m , which is the initial reconstructed value of the attribute information of the current point in R m .
  • the decoder decodes the code stream of the point cloud, determines the filter coefficient of the target point in the point cloud; and filters the initial reconstruction value of the attribute information of the target point based on the filter coefficient of the target point to determine the reconstruction value of the attribute information of the target point.
  • the accuracy of the reconstruction value of the attribute information of the target point can be improved, thereby improving the decoding performance and enhancing the quality of the reconstructed point cloud.
  • this embodiment merges and filters the first N refinement layers, and covers the initial reconstruction value with the filtered value to obtain an updated reconstruction value, which can improve filtering accuracy and decoding efficiency.
  • S313 may include:
  • the decoder first determines the target detail layer where the target point is located; the target detail layer includes one or more refinement layers; then, the decoder determines the filter coefficient corresponding to the target detail layer; then, the decoder determines the filter coefficient of any point in the target detail layer based on the filter coefficient corresponding to the target detail layer.
  • the structure of the code stream may be as follows:
  • n the total number of points in the point cloud
  • m the number of detail layers in the point cloud that have been Wiener filtered
  • k the length of the filter coefficients corresponding to each detail layer (also called the order of the Wiener filter, or the length of the Wiener filter).
  • k 16. bits, flagbits LOD, coefbits LOD are the residual values of the attribute information of the point in the point cloud, the filter flags corresponding to each detail layer, and the filter coefficients corresponding to each detail layer, respectively.
  • the decoder first decodes the code stream to determine the reconstructed value of the geometric information of the point cloud; then, the decoder divides the point cloud into L refinement layers based on the reconstructed value of the geometric information of the point cloud, and the refinement layer includes one or more points, and L is an integer greater than 0; then, the decoder determines L detail layers of the point cloud based on the L refinement layers; wherein the detail layer numbered i in the L detail layers includes: one or more refinement layers numbered 0 to i arranged in the order of generation of the refinement layers, and i is an integer greater than 0; then, the decoder determines the target detail layer based on the index of the target point.
  • the decoder may divide the point cloud into L refinement layers based on different Euclidean distances, and then obtain L LOD layers based on the refinement layers.
  • the LOD layer division method can also be other methods, and this application does not limit this.
  • the point cloud can be directly divided into one or more LOD layers, or the point cloud can be first divided into multiple point cloud slices, and then each point cloud slice can be divided into one or more LOD layers.
  • the point cloud can be divided into multiple point cloud slices, and the number of points in each point cloud slice can be between 550,000 and 1.1 million.
  • Each point cloud slice can be regarded as a separate point cloud.
  • Each point cloud slice can be divided into multiple LOD layers, and each LOD layer includes multiple points.
  • the decoder may determine the target detail layer based on the index of the target point and the number of points in each refinement layer.
  • the decoder first decodes the bitstream to determine the filter identifier corresponding to the target detail layer; the filter identifier corresponding to the target detail layer indicates the position of the filter coefficient corresponding to the target detail layer in the bitstream; then, the decoder decodes the bitstream of the point cloud based on the filter identifier corresponding to the target detail layer to determine the filter coefficient corresponding to the target detail layer.
  • the filter identifier corresponding to the target detail layer indicates the position of the filter coefficient corresponding to the target detail layer in the bitstream by indicating the identifier of the target detail layer.
  • n the total number of points in the point cloud
  • m the number of detail layers in the point cloud that have been Wiener filtered
  • k the length of the filter coefficients corresponding to each detail layer (also called the order of the Wiener filter, or the length of the Wiener filter).
  • bits, flagbits LOD, coefbits LOD are the residual values of the attribute information of the point in the point cloud, the filter identifiers corresponding to each detail layer, and the filter coefficients corresponding to each detail layer, respectively.
  • the filter identifier corresponding to the target detail layer indicates the identifier of the target detail layer, which is equivalent to indicating that the filter coefficients corresponding to the target detail layer are the filter coefficients located after the filter identifier corresponding to the target detail layer in the code stream.
  • Flagbits_LOD 0 indicates the identifier of the target detail layer
  • coefbits_LOD 0[k] following Flagbits_LOD 0 can default to the filter coefficient corresponding to the target detail layer indicated by Flagbits_LOD 0.
  • the filter identifier corresponding to the target detail layer indicates whether the filter coefficient corresponding to the target detail layer is carried. For example, if the code stream carries the filter identifier corresponding to the target detail layer, it means that the code stream carries the filter coefficient corresponding to the target detail layer. If the code stream does not carry the filter identifier corresponding to the target detail layer, it means that the code stream does not carry the filter coefficient corresponding to the target detail layer. Even, the filter identifier corresponding to the target detail layer can indicate whether the target detail layer is filtered; for example, the decoder can adopt a method similar to the encoder to determine the filter coefficient corresponding to the target detail layer. When determining the filter coefficient corresponding to the target detail layer, the filter identifier corresponding to the target detail layer can indicate whether the target detail layer is filtered. This application does not make specific limitations on this.
  • the decoder sets the filter coefficient of any point in the target detail layer to be equal to the filter coefficient corresponding to the target detail layer.
  • the filter coefficients of all points in the target detail layer are the same filter coefficients.
  • the filter coefficients of each point in the target detail layer may also be carried in the bitstream, and this application does not make any specific limitation to this.
  • S314 may include:
  • the decoder multiplies the initial reconstruction value or the vector formed by the reconstruction value of the attribute information of the neighboring points of the target point by the filter coefficient of the target point to obtain the reconstruction value of the attribute information of the target point.
  • the decoder multiplies the initial reconstruction value or the vector formed by the reconstruction value of the attribute information of the k neighboring points of the target point by the filter coefficient of the target point to obtain the reconstruction value of the attribute information of the target point.
  • k represents the length of the filter coefficient corresponding to each detail layer (also called the order of the Wiener filter, or the length of the Wiener filter).
  • k 16.
  • k can also be other values, and this application does not specifically limit this.
  • the decoder may multiply the vector formed by the initial reconstruction value of the attribute information of the target point and the initial reconstruction value of the attribute information of the target point's k-1 neighboring points by the filter coefficient of the target point to obtain the reconstructed value of the attribute information of the target point, or the decoder may multiply the vector formed by the initial reconstruction value of the attribute information of the target point and the reconstruction value of the attribute information of the target point's k-1 neighboring points by the filter coefficient of the target point to obtain the reconstructed value of the attribute information of the target point, and the present application does not make specific limitations on this.
  • the present application does not specifically limit the implementation method of the decoder searching for the neighboring points of the target point.
  • the KNN algorithm or other methods can be used to search for the neighboring points of the target point.
  • the decoder selects neighboring points of the target point in the first N refinement layers, and multiplies the vector formed by the initial reconstruction values of the attribute information of the neighboring points of the target point by the filter coefficient of the target point to obtain the reconstructed value of the attribute information of the target point.
  • the decoder selects k neighboring points of the target point in the first N refinement layers, and multiplies the vector formed by the initial reconstruction values of the attribute information of the k neighboring points of the target point by the filter coefficient of the target point to obtain the reconstructed value of the attribute information of the target point.
  • the decoder selects k-1 neighboring points of the target point in the first N refinement layers, and multiplies the vector formed by the initial reconstruction value of the attribute information of the target point and the initial reconstruction value of the attribute information of the k-1 neighboring points of the target point by the filter coefficient of the target point to obtain the reconstructed value of the attribute information of the target point.
  • the decoder selects k neighboring points of the target point in the filtered refinement layer, and multiplies the vector formed by the reconstructed values of the attribute information of the k neighboring points of the target point by the filtering coefficient of the target point to obtain the reconstructed value of the attribute information of the target point.
  • the decoder selects k neighboring points of the target point in the filtered refinement layer, and multiplies the vector formed by the initial reconstructed values of the attribute information of the k neighboring points of the target point by the filtering coefficient of the target point to obtain the reconstructed value of the attribute information of the target point.
  • the decoder selects k-1 neighboring points of the target point in the filtered refinement layer, and multiplies the initial reconstruction value of the attribute information of the target point and the initial reconstruction value of the attribute information of the target point and the initial reconstruction value of the attribute information of the k-1 neighboring points of the target point by the filtering coefficient of the target point to obtain the reconstructed value of the attribute information of the target point.
  • the method 310 may further include:
  • the decoder obtains a decoded point cloud based on the reconstructed value of the attribute information of the target point.
  • the decoder obtains the decoded point cloud based on the reconstructed values of the attribute information of all the points in the point cloud.
  • the decoding method according to the embodiment of the present application is described in detail above from the perspective of the decoder.
  • the encoding method according to the embodiment of the present application will be described from the perspective of the encoder in combination with Figures 16 to 18.
  • FIG16 is a schematic flow chart of a point cloud encoding method 410 provided in an embodiment of the present application.
  • the point cloud encoding method 410 can be performed by an encoder or an encoding framework. For example, it is applied to the encoding framework 100 shown in FIG1.
  • the encoder is used as an example for description below.
  • the point cloud encoding method 410 may include:
  • the encoder determines a residual value of the attribute information of the target point based on the true value of the attribute information of the target point in the point cloud and the predicted value of the attribute information of the target point;
  • the encoder determines a filter coefficient of the target point based on neighboring points of the target point;
  • the encoder encodes the residual value of the attribute information of the target point and the filter coefficient of the target point to obtain a code stream.
  • the encoder filters the initial reconstruction value of the attribute information of the target point based on the filter coefficient of the target point to obtain a filtered reconstruction value, and uses the filtered reconstruction value to cover or replace the initial reconstruction value of the attribute information of the target point.
  • the encoder can use the updated reconstructed values to predict the attribute information of subsequent points.
  • the encoder encodes the residual value of the attribute information of the target point and the filter coefficient of the target point, which is beneficial for the decoder to decode the code stream of the point cloud and determine the filter coefficient of the target point in the point cloud; and further, it is beneficial for the decoder to filter the initial reconstruction value of the attribute information of the target point based on the filter coefficient of the target point and determine the reconstructed value of the attribute information of the target point.
  • the encoder encodes the residual value of the attribute information of the target point and the filter coefficient of the target point, which is beneficial for the decoder to decode the code stream of the point cloud and determine the filter coefficient of the target point in the point cloud; and further, it is beneficial for the decoder to filter the initial reconstruction value of the attribute information of the target point based on the filter coefficient of the target point and determine the reconstructed value of the attribute information of the target point.
  • the encoder encodes the residual value of the attribute information of the target point and the filter coefficient of the target point, which is beneficial for the decoder to
  • the filter coefficient of the target point may be a filter coefficient output by a Wiener encoder.
  • the Wiener filter is a linear filter proposed by mathematician Norbert Wiener that uses the least square as the optimal criterion. Under certain constraints, the square of the difference between its output and a given function (usually called the expected output) is minimized, and through mathematical operations it can eventually become a problem of solving a Toblitz equation.
  • the Wiener filter is also called the least squares filter or the least squares filter.
  • y(n) represents the output signal after filtering
  • k represents the order of the filter
  • H(k) represents the coefficient of the filter
  • X(n) represents the input signal of the filter, that is, the input signal mixed with noise.
  • R xd represents the cross-correlation vector between the input signal and the expected signal
  • R xx represents the autocorrelation matrix between the input signal and the input signal
  • the optimal filter coefficient can be calculated using the k nearest neighbor points of each point (including the point).
  • the order of the filter also called the length of the filter
  • a matrix P(n,k) can be constructed to represent the attribute reconstruction value (i.e., input signal) of the k nearest neighbor points of each point in the n points
  • a vector S(n) can be constructed to represent the true value of the attribute information of each point in the n points (i.e., expected signal).
  • S412 may include:
  • the encoder first determines at least one refinement layer based on the target refinement layer where the target point is located; the refinement layer includes one or more points; then, the encoder determines a first matrix and a first vector, the first matrix is a matrix formed by initial reconstruction values or reconstruction values of attribute information of neighboring points of the point in the at least one refinement layer, and the first vector is a vector formed by real values of the attribute information of the point in the at least one refinement layer; the encoder multiplies the first matrix and the first vector to obtain a mutual correlation matrix of the at least one refinement layer; multiplies the transposed matrix of the first matrix and the first matrix to obtain an autocorrelation matrix of the at least one refinement layer; based on the at least one refinement layer, The encoder determines the filter coefficient corresponding to the at least one refinement layer based on the mutual correlation matrix of the refinement layer and the autocorrelation matrix of the at least one refinement layer; then, the encoder determines the filter coefficient of any point in the at least one refinement
  • the first matrix is a matrix formed by initial reconstruction values or reconstruction values of attribute information of k neighboring points of each point in the at least one refinement layer
  • the first vector is a vector formed by true values of attribute information of each point in the at least one refinement layer.
  • k represents the length of the filter coefficient corresponding to each detail layer (also called the order of the Wiener filter, or the length of the Wiener filter).
  • k 16.
  • k can also be other values, which are not specifically limited in this application.
  • the filter identifier corresponding to the at least one refinement layer indicates the position of the filter coefficient corresponding to the at least one refinement layer in the bitstream by indicating the identifier of the at least one refinement layer.
  • the structure of the code stream can be as follows: Flagbits_0, coefbits_0[k], ..., Flagbits_m-1, coefbits_m-1[k], bits_0, ..., bits_n-2, bits_n-1.
  • n represents the total number of points in the point cloud
  • m represents the number of refinement layers in the point cloud that have been subjected to Wiener filtering
  • k represents the length of the filter coefficient corresponding to each refinement layer (also called the order of the Wiener filter, or the length of the Wiener filter).
  • k 16.
  • bits, flagbits, coefbits are respectively the residual value of the attribute information of the point in the point cloud, the filter identifier corresponding to each refinement layer, and the filter coefficient corresponding to each refinement layer; in this case, the filter identifier corresponding to the at least one refinement layer indicates the identifier of the at least one refinement layer, which is equivalent to indicating that the filter coefficient corresponding to the at least one refinement layer is the filter coefficient located after the filter identifier corresponding to the at least one refinement layer in the code stream.
  • Flagbits_0 indicates the identifier of the at least one refinement layer
  • coefbits_0[k] following Flagbits_0 may default to the filter coefficient corresponding to the refinement layer indicated by Flagbits_0.
  • the filter identifier corresponding to the at least one refinement layer indicates whether the filter coefficient corresponding to the at least one refinement layer is carried. For example, if the code stream carries the filter identifier corresponding to the at least one refinement layer, it means that the code stream carries the filter coefficient corresponding to the at least one refinement layer. If the code stream does not carry the filter identifier corresponding to the at least one refinement layer, it means that the code stream does not carry the filter coefficient corresponding to the at least one refinement layer.
  • the filter identifier corresponding to the at least one refinement layer can indicate whether to filter the at least one refinement layer; for example, the decoder can adopt a method similar to the encoder to determine the filter coefficient corresponding to the at least one refinement layer.
  • the filter identifier corresponding to the at least one refinement layer can indicate whether to filter the at least one refinement layer. This application does not make specific limitations on this.
  • the encoder first determines the reconstruction value of the geometric information of the point cloud; then, the encoder divides the point cloud into L refinement layers based on the reconstruction value of the geometric information of the point cloud, and the refinement layer includes one or more points, and L is an integer greater than 0; then, the encoder determines the target refinement layer based on the index of the target point.
  • the encoder may divide the point cloud into L refinement layers based on different Euclidean distances, and then obtain L LOD layers based on the refinement layers.
  • the LOD layer division method can also be other methods, and this application does not limit this.
  • the point cloud can be directly divided into one or more LOD layers, or the point cloud can be first divided into multiple point cloud slices, and then each point cloud slice can be divided into one or more LOD layers.
  • the point cloud can be divided into multiple point cloud slices, and the number of points in each point cloud slice can be between 550,000 and 1.1 million.
  • Each point cloud slice can be regarded as a separate point cloud.
  • Each point cloud slice can be divided into multiple LOD layers, and each LOD layer includes multiple points.
  • the encoder may determine at least one refinement layer based on the index of the target point and the number of points in each refinement layer and based on the target refinement layer where the target point is located.
  • the encoder selects neighboring points of the point in at least one refinement layer from the first N refinement layers, and determines the matrix formed by the initial reconstructed values of the attribute information of the neighboring points of the point in the at least one refinement layer as the first matrix.
  • the encoder selects k neighboring points of the target point in the first N refinement layers, and determines the matrix formed by the initial reconstructed values of the attribute information of the k neighboring points of the target point as the first matrix.
  • the encoder selects k-1 neighboring points of the target point in the first N refinement layers, and determines the matrix formed by the initial reconstruction value of the attribute information of the target point and the initial reconstruction value of the attribute information of the k-1 neighboring points of the target point as the first matrix.
  • the encoder selects neighboring points of the point in at least one refinement layer in the filtered refinement layers, and determines the matrix formed by the reconstructed values of the attribute information of the neighboring points of the point in the at least one refinement layer as the first matrix.
  • the encoder selects k neighboring points of the target point in the filtered refinement layer, and determines the matrix formed by the initial reconstructed values of the attribute information of the k neighboring points of the target point as the first matrix.
  • the encoder selects k-1 neighboring points of the target point in the filtered refinement layer, and determines the matrix formed by the initial reconstructed value of the attribute information of the target point, the initial reconstructed value of the attribute information of the target point, and the initial reconstructed value of the attribute information of the k-1 neighboring points of the target point as the first matrix.
  • the encoder sets the filter coefficient of any point in the at least one refinement layer to be equal to the filter coefficient corresponding to the at least one refinement layer.
  • the filter coefficients of all points in the at least one refinement layer are the same filter coefficients.
  • the filter coefficients of each point in the at least one refinement layer may also be carried in the code stream, and this application does not make any specific limitation to this.
  • the at least one refinement layer includes the first N refinement layers arranged in the order of generation of the refinement layers, or the at least one refinement layer includes one or more refinement layers located after the first N refinement layers in the order of generation of the refinement layers, and N is an integer greater than 0.
  • the KNN algorithm is used to find the k neighboring points of all the points in the first N refinement layers, and then the true values of the attribute information of all the points in the first N refinement layers and the initial reconstruction values (or reconstruction values) of the attributes of the k neighboring points of all the points found are used as the input of the Wiener filter, the filter coefficients corresponding to the first N refinement layers are derived, and the initial reconstruction values of all the points in the first N refinement layers are filtered, the filtered values are overwritten with the initial reconstruction values to obtain updated reconstruction values, the updated reconstruction values are used to predict the points in the subsequent refinement layers, and the filter coefficients corresponding to the first N refinement layers are written into the bitstream.
  • this embodiment merges and filters the first N refinement layers, and overwrites the filtered values with the initial reconstruction values to obtain updated reconstruction values, which can improve filtering accuracy and decoding efficiency.
  • the amount of calculation of the filter coefficients is reduced, and the coding efficiency is improved.
  • the at least one refinement layer includes a single refinement layer that is located before the last refinement layer and after the first N refinement layers in the generation order of the refinement layers.
  • this embodiment performs independent filtering on the single refinement layer, which can improve filtering accuracy.
  • FIG. 17 is a schematic flowchart of a point cloud encoding method 420 provided in an embodiment of the present application.
  • the point cloud encoding method 420 may include:
  • the encoder determines a residual value of the attribute information of the target point based on the true value of the attribute information of the target point in the point cloud and the predicted value of the attribute information of the target point.
  • the encoder when predicting the attribute information of the target point, the encoder first generates one or more refinement layers based on the original order of the point cloud, then searches for the nearest neighbor of the target point from the previously encoded refinement layers according to the generation order of the refinement layers, and predicts the attribute information of the target point based on the nearest neighbor found to obtain the attribute prediction value of the target point.
  • the encoder may first create multiple candidate prediction values based on the search results of the neighbor points of the target point, and then use the rate distortion optimization (RDO) technology to select the attribute prediction value with the best prediction performance for the target point from the multiple candidate prediction values, and then use the best attribute prediction value as the attribute prediction value of the target point, and encode the attribute information by subtracting it from the original attribute value to obtain the attribute code stream of the point cloud.
  • RDO rate distortion optimization
  • the encoder may first use multiple prediction modes (predMode) to create multiple candidate prediction values based on the search results of neighboring points of the target point.
  • predMode multiple prediction modes
  • the index values of the multiple prediction modes can be 0 to 3.
  • the encoder When the encoder encodes the attribute information of the target point, the encoder first finds the three neighbor points before the target point based on the neighbor point search results on the LOD where the target point is located, and then obtains multiple candidate prediction values based on the prediction mode with an index value of 0 to 3.
  • the prediction mode with index 0 means that the weighted average of the reconstructed attribute values of the three neighbor points is determined as the candidate prediction value of the target point based on the distance between the three neighbor points and the target point;
  • the prediction mode with index 1 means: the attribute reconstruction value of the nearest neighbor point among the three neighbor points is used as the candidate prediction value of the target point;
  • the prediction mode with index 2 means: the attribute reconstruction value of the second nearest neighbor point is used as the candidate prediction value of the target point;
  • the prediction mode with index 3 means: the attribute reconstruction values of the neighbor points other than the nearest neighbor point and the second nearest neighbor point among the three neighbor points are used as the candidate prediction value of the target point; after obtaining multiple candidate prediction values of the target point based on the above-mentioned various prediction modes, the rate distortion optimization (RDO) technology can be used to select the best attribute prediction value, and then the selected best attribute prediction value is used as the attribute prediction value of the target point for subsequent encoding.
  • RDO rate distortion optimization
  • S422 The encoder quantizes and dequantizes the residual value of the attribute information of the target point, and then adds the residual value to the predicted value of the attribute information of the target point to obtain an initial reconstructed value of the attribute information of the target point.
  • the encoder may subtract the true value attrValue of the attribute information of the target point from the predicted value attrPred of the attribute information of the target point and quantize the result to obtain the residual value attrResidualQuant of the attribute information of the target point.
  • attrResidualQuant (attrValue-attrPred)/Qs; wherein attrResidualQuant represents the residual value of the attribute information of the target point, attrValue represents the true value of the attribute information of the target point, attrPred represents the predicted value of the attribute information of the target point, and Qstep represents the quantization step size.
  • Qstep is calculated by the quantization parameter (Quantization Parameter, Qp).
  • the encoder determines the filter coefficient corresponding to R7 based on the neighboring points of the point in R7 , and sets the filter coefficient of the target point to be equal to the filter coefficient corresponding to R7 .
  • the encoder encodes the residual value of the attribute information of the point in R7 , the filter identifier corresponding to R7 , and the filter coefficient corresponding to R7 to obtain a code stream of the point cloud.
  • the encoder determines the filter coefficient corresponding to R7 , and uses the filter coefficient corresponding to R7 to filter the initial reconstruction value of the attribute information of the midpoint of R7 , and the filtered reconstruction value is used to cover the initial reconstruction value to obtain an updated reconstruction value.
  • the reconstruction value of the midpoint of LODm is used to predict the attribute information of the midpoint of the refined layer after R7 and obtain the predicted value of the attribute information of the midpoint of the refined layer after R7 .
  • the encoding can repeat the above process until the filtering of the penultimate refinement layer is also completed and the initial reconstruction value of the point in the penultimate refinement layer is overwritten with the filtered value. At this point, the filtering of the points in the point cloud is completed.
  • the encoder encodes the residual value of the attribute information of the target point and the filter coefficient of the target point, which is beneficial for the decoder to decode the code stream of the point cloud and determine the filter coefficient of the target point in the point cloud; and further, it is beneficial for the decoder to filter the initial reconstruction value of the attribute information of the target point based on the filter coefficient of the target point to determine the reconstructed value of the attribute information of the target point.
  • the initial reconstruction value of the attribute information of the target point using the filter coefficient of the target point, the accuracy of the reconstructed value of the attribute information of the target point can be improved, thereby improving the decoding performance and achieving the enhancement of the quality of the reconstructed point cloud.
  • this embodiment merges and filters the first N refinement layers, and overwrites the filtered values with the initial reconstruction values to obtain updated reconstruction values, which can improve filtering accuracy and decoding efficiency.
  • FIG. 18 is a schematic flowchart of a point cloud encoding method 430 provided in an embodiment of the present application.
  • the point cloud encoding method 430 may include:
  • the encoder obtains the code stream of the point cloud.
  • the encoder divides the point cloud into L refinement layers, and predicts the attribute information of the current point in Rm and obtains its predicted value.
  • the encoder when predicting the attribute information of the current point, the encoder first generates one or more refinement layers based on the original order of the point cloud, then searches for the nearest neighbor of the current point from the previously encoded refinement layers according to the generation order of the refinement layers, and predicts the attribute information of the current point based on the nearest neighbor found to obtain the attribute prediction value of the current point.
  • the encoder may first create multiple candidate prediction values based on the search results of the neighboring points of the current point, and then use the rate distortion optimization (RDO) technology to select the attribute prediction value with the best prediction performance for the current point from the multiple candidate prediction values, and then use the best attribute prediction value as the attribute prediction value of the current point, and encode the attribute information by subtracting it from the original attribute value to obtain the attribute code stream of the point cloud.
  • RDO rate distortion optimization
  • the encoder may first use multiple prediction modes (predMode) to create multiple candidate prediction values based on the search results of neighboring points of the current point.
  • predMode multiple prediction modes
  • the index values of the multiple prediction modes can be 0 to 3.
  • predMode the index values of the multiple prediction modes
  • the prediction mode with an index of 0 refers to determining the weighted average of the reconstructed attribute values of the three neighbor points as the candidate prediction value of the current point based on the distance between the three neighbor points and the current point;
  • the prediction mode with an index of 1 refers to: using the attribute reconstruction value of the nearest neighbor point among the three neighbor points as the candidate prediction value of the current point;
  • the prediction mode with an index of 2 refers to: using the attribute reconstruction value of the second nearest neighbor point as the candidate prediction value of the current point;
  • the prediction mode with an index of 3 refers to: using the attribute reconstruction value of the neighbor points other than the nearest neighbor point and the second nearest neighbor point among the three neighbor points as the candidate prediction value of the current point; after obtaining multiple candidate prediction values for the current point based on the above-mentioned various prediction modes, the rate distortion optimization (RDO) technology can be used to select the best attribute prediction value, and then the selected best attribute prediction value is used as the attribute prediction value of the current point for subsequent encoding.
  • RDO rate
  • the encoder divides the point cloud into L refinement layers, and processes the attribute information of the current point in Rm and obtains its true value.
  • the encoder determines a residual value of the attribute information of the current point.
  • the encoder determines an initial reconstruction value of the attribute information of the current point.
  • the encoder may subtract the true value attrValue of the attribute information of the current point from the predicted value attrPred of the attribute information of the current point and quantize the result to obtain the residual value attrResidualQuant of the attribute information of the current point.
  • attrResidualQuant (attrValue-attrPred)/Qs; wherein attrResidualQuant represents the residual value of the attribute information of the current point, attrValue represents the true value of the attribute information of the current point, attrPred represents the predicted value of the attribute information of the current point, and Qstep represents the quantization step size.
  • Qstep is calculated by the quantization parameter (Quantization Parameter, Qp).
  • the encoder determines that the current point is the last point in R m .
  • the encoder determines a filter coefficient corresponding to R m , and filters the initial reconstructed value of the attribute information of the point in R m based on the filter coefficient corresponding to R m , which is the initial reconstructed value of the attribute information of the current point in R m .
  • the encoder determines a code stream of the point cloud.
  • the encoder encodes the residual value of the attribute information of the target point and the filter coefficient of the target point, which is beneficial for the decoder to decode the code stream of the point cloud and determine the filter coefficient of the target point in the point cloud; and further, it is beneficial for the decoder to filter the initial reconstruction value of the attribute information of the target point based on the filter coefficient of the target point to determine the reconstruction value of the attribute information of the target point.
  • the encoder encodes the residual value of the attribute information of the target point and the filter coefficient of the target point, which is beneficial for the decoder to decode the code stream of the point cloud and determine the filter coefficient of the target point in the point cloud; and further, it is beneficial for the decoder to filter the initial reconstruction value of the attribute information of the target point based on the filter coefficient of the target point to determine the reconstruction value of the attribute information of the target point.
  • the accuracy of the reconstruction value of the attribute information of the target point can be improved, thereby improving the decoding performance and achieving the enhancement of the quality of the reconstructed
  • this embodiment merges and filters the first N refinement layers, and overwrites the filtered values with the initial reconstruction values to obtain updated reconstruction values, which can improve filtering accuracy and decoding efficiency.
  • S412 may include:
  • the encoder first determines the target detail layer where the target point is located; the target detail layer includes one or more refinement layers; then, the encoder obtains a second matrix and a second vector, the second matrix is a matrix formed by initial reconstruction values or reconstruction values of attribute information of neighboring points of the target detail layer, and the second vector is a vector formed by true values of the attribute information of the point in the target detail layer; the second matrix and the second vector are multiplied to obtain a mutual correlation matrix of the target detail layer; the transposed matrix of the second matrix and the second matrix are multiplied to obtain an autocorrelation matrix of the target detail layer; based on the mutual correlation matrix of the target detail layer and the autocorrelation matrix of the target detail layer, the filter coefficient corresponding to the target detail layer is determined; then, the encoder determines the filter coefficient of any point in the target detail layer based on the filter coefficient corresponding to the target detail layer; wherein, S413 may include: the encoder encodes the residual value of the attribute information of the point in the target detail layer, the
  • the second matrix is a matrix formed by the initial reconstruction values or reconstruction values of the attribute information of the k neighboring points of each point in the target detail layer
  • the second vector is a vector formed by the true value of the attribute information of each point in the target detail layer.
  • k represents the length of the filter coefficient corresponding to each detail layer (also called the order of the Wiener filter, or the length of the Wiener filter).
  • k 16.
  • k can also be other values, and this application does not specifically limit this.
  • the filter identifier corresponding to the target detail layer indicates the position of the filter coefficient corresponding to the target detail layer in the bitstream by indicating the identifier of the target detail layer.
  • n the total number of points in the point cloud
  • m the number of detail layers in the point cloud that have been Wiener filtered
  • k the length of the filter coefficients corresponding to each detail layer (also called the order of the Wiener filter, or the length of the Wiener filter).
  • bits, flagbits LOD, coefbits LOD are the residual values of the attribute information of the point in the point cloud, the filter identifiers corresponding to each detail layer, and the filter coefficients corresponding to each detail layer, respectively.
  • the filter identifier corresponding to the target detail layer indicates the identifier of the target detail layer, which is equivalent to indicating that the filter coefficients corresponding to the target detail layer are the filter coefficients located after the filter identifier corresponding to the target detail layer in the code stream.
  • Flagbits_LOD 0 indicates the identifier of the target detail layer
  • coefbits_LOD 0[k] following Flagbits_LOD 0 can default to the filter coefficient corresponding to the target detail layer indicated by Flagbits_LOD 0.
  • the filter identifier corresponding to the target detail layer indicates whether the filter coefficient corresponding to the target detail layer is carried. For example, if the code stream carries the filter identifier corresponding to the target detail layer, it means that the code stream carries the filter coefficient corresponding to the target detail layer. If the code stream does not carry the filter identifier corresponding to the target detail layer, it means that the code stream does not carry the filter coefficient corresponding to the target detail layer. Even, the filter identifier corresponding to the target detail layer can indicate whether the target detail layer is filtered; for example, the decoder can adopt a method similar to the encoder to determine the filter coefficient corresponding to the target detail layer. When determining the filter coefficient corresponding to the target detail layer, the filter identifier corresponding to the target detail layer can indicate whether the target detail layer is filtered. This application does not make specific limitations on this.
  • the encoder first determines the reconstruction value of the geometric information of the point cloud; then, the encoder divides the point cloud into L refinement layers based on the reconstruction value of the geometric information of the point cloud, and the refinement layer includes one or more points, and L is an integer greater than 0; based on the L refinement layers, L detail layers of the point cloud are obtained; wherein the detail layer numbered i in the L detail layers includes: one or more refinement layers numbered 0 to i arranged in the order of generation of the refinement layers, and i is an integer greater than 0; then, the encoder determines the target detail layer based on the index of the target point.
  • the encoder may divide the point cloud into L refinement layers based on different Euclidean distances, and then obtain L LOD layers based on the refinement layers.
  • the LOD layer division method can also be adopted in other ways, and this application does not limit this.
  • the point cloud can be directly divided into one or more LOD layers, or the point cloud can be first divided into multiple point cloud slices, and then each point cloud slice is divided into one or more LOD layers.
  • the point cloud can be divided into multiple point cloud slices, and the number of points in each point cloud slice can be between 550,000 and 1.1 million.
  • Each point cloud slice can be regarded as a separate point cloud.
  • Each point cloud slice can be divided into multiple LOD layers, and each LOD layer includes multiple points.
  • the encoder may determine the target detail layer based on the index of the target point and the number of points in each refinement layer.
  • the encoder selects neighboring points of the midpoint of the target detail layer in the first N refinement layers, and determines the matrix formed by the initial reconstructed values of the attribute information of the neighboring points of the midpoint of the target detail layer as the second matrix.
  • the encoder selects k neighboring points of the target point in the first N refinement layers, and determines the matrix formed by the initial reconstructed values of the attribute information of the k neighboring points of the target point as the second matrix.
  • the encoder selects k-1 neighboring points of the target point in the first N refinement layers, and determines the matrix formed by the initial reconstruction value of the attribute information of the target point and the initial reconstruction value of the attribute information of the k-1 neighboring points of the target point as the second matrix.
  • the encoder selects neighboring points of the midpoint of the target detail layer in the filtered refinement layer, and determines the matrix formed by the reconstructed values of the attribute information of the neighboring points of the midpoint of the target detail layer as the second matrix.
  • the encoder selects k neighboring points of the target point in the filtered refinement layer, and determines the matrix formed by the initial reconstructed values of the attribute information of the k neighboring points of the target point as the second matrix.
  • the encoder selects k-1 neighboring points of the target point in the filtered refinement layer, and determines the matrix formed by the initial reconstructed value of the attribute information of the target point, the initial reconstructed value of the attribute information of the target point, and the initial reconstructed value of the attribute information of the k-1 neighboring points of the target point as the second matrix.
  • the encoder sets the filter coefficient of any point in the target detail layer to be equal to the filter coefficient corresponding to the target detail layer.
  • the filter coefficients of all points in the at least one refinement layer are the same filter coefficients.
  • the filter coefficients of each point in the at least one refinement layer may also be carried in the code stream, and this application does not make any specific limitation to this.
  • the encoder determines an initial reconstruction value of the attribute information of the target point based on the residual value of the attribute information of the target point and the predicted value of the attribute information of the target point; and filters the initial reconstruction value of the attribute information of the target point based on the filter coefficient of the target point to determine the reconstruction value of the attribute information of the target point.
  • the encoder multiplies the initial reconstructed value or a vector formed by the reconstructed value of the attribute information of the neighboring points of the target point by the filter coefficient of the target point to obtain the reconstructed value of the attribute information of the target point.
  • the encoder selects neighboring points of the target point in the first N refinement layers, and multiplies the vector formed by the initial reconstruction values of the attribute information of the neighboring points of the target point by the filter coefficient of the target point to obtain the reconstructed value of the attribute information of the target point.
  • the encoder selects the neighboring points of the target point in the filtered refinement layer, and multiplies the vector formed by the reconstructed values of the attribute information of the neighboring points of the target point by the filtering coefficient of the target point to obtain the reconstructed value of the attribute information of the target point.
  • the scheme of filtering the initial reconstruction value of the target point by the encoder can refer to the scheme of filtering the initial reconstruction value of the target point by the decoder, and to avoid repetition, it is not described here.
  • the size of the sequence number of each process mentioned above does not mean the order of execution.
  • the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.
  • FIG. 19 is a schematic block diagram of a decoder 500 according to an embodiment of the present application.
  • the decoder 500 may include:
  • a decoding unit 510 configured to decode a code stream of a point cloud and determine a residual value of attribute information of a target point in the point cloud;
  • a reconstruction unit 520 configured to determine an initial reconstruction value of the attribute information of the target point based on the residual value of the attribute information of the target point and the predicted value of the attribute information of the target point;
  • a determination unit 530 configured to determine a filter coefficient of the target point
  • the filtering unit 540 is configured to filter the initial reconstructed value of the attribute information of the target point based on the filter coefficient of the target point to determine the reconstructed value of the attribute information of the target point.
  • the determining unit 530 is specifically configured to:
  • the refinement layer includes one or more points
  • a filter coefficient of any point in the at least one refinement layer is determined.
  • the determining unit 530 is specifically configured to:
  • the point cloud is divided into L refinement layers, where the refinement layer includes one or more points, and L is an integer greater than 0;
  • the target refinement layer is determined.
  • the determining unit 530 is specifically configured to:
  • the code stream is decoded based on the filter identifier corresponding to the at least one refinement layer to determine the filter coefficient corresponding to the at least one refinement layer.
  • the determining unit 530 is specifically configured to:
  • the filter coefficient of any point in the at least one refinement layer is set to be equal to the filter coefficient corresponding to the at least one refinement layer.
  • the at least one refinement layer includes the first N refinement layers arranged in the order of generation of the refinement layers, or the at least one refinement layer includes one or more refinement layers located after the first N refinement layers in the order of generation of the refinement layers, and N is an integer greater than 0.
  • the at least one refinement layer includes a single refinement layer that is located before the last refinement layer and after the first N refinement layers in the generation order of the refinement layers.
  • the determining unit 530 is specifically configured to:
  • the target detail layer includes one or more refinement layers
  • the filter coefficient of any point in the target detail layer is determined.
  • the determining unit 530 is specifically configured to:
  • the point cloud is divided into L refinement layers, where the refinement layer includes one or more points, and L is an integer greater than 0;
  • the detail layer numbered i in the L detail layers includes: one or more detail layers numbered 0 to i arranged in the generation order of the detail layers, where i is an integer greater than 0;
  • the target detail layer is determined.
  • the determining unit 530 is specifically configured to:
  • the code stream of the point cloud is decoded based on the filter identifier corresponding to the target detail layer, and the filter coefficient corresponding to the target detail layer is determined.
  • the determining unit 530 is specifically configured to:
  • the filter coefficient of any point in the target detail layer is set to be equal to the filter coefficient corresponding to the target detail layer.
  • the filtering unit 540 is specifically used for:
  • the initial reconstruction value or the vector formed by the reconstruction value of the attribute information of the neighboring points of the target point is multiplied by the filter coefficient of the target point to obtain the reconstruction value of the attribute information of the target point.
  • the filtering unit 540 is specifically used for:
  • the refinement layer where the target point is located belongs to the first N refinement layers arranged in the order of generation of the refinement layers, then the neighboring points of the target point are selected in the first N refinement layers, and the vector formed by the initial reconstruction values of the attribute information of the neighboring points of the target point is multiplied by the filter coefficient of the target point to obtain the reconstructed value of the attribute information of the target point.
  • the filtering unit 540 is specifically used for:
  • the refinement layer where the target point is located belongs to the refinement layer after the first N refinement layers arranged in the order of generation of the refinement layers, then the neighboring points of the target point are selected in the filtered refinement layer, and the vector formed by the reconstructed values of the attribute information of the neighboring points of the target point is multiplied by the filter coefficient of the target point to obtain the reconstructed value of the attribute information of the target point.
  • the reconstruction unit 520 is further configured to:
  • a decoded point cloud is obtained.
  • FIG. 20 is a schematic block diagram of an encoder 600 according to an embodiment of the present application.
  • the encoder 600 may include:
  • a residual unit 610 configured to determine a residual value of the attribute information of the target point based on a true value of the attribute information of the target point in the point cloud and a predicted value of the attribute information of the target point;
  • a determination unit 620 configured to determine a filter coefficient of the target point based on neighboring points of the target point
  • the encoding unit 630 is used to encode the residual value of the attribute information of the target point and the filter coefficient of the target point to obtain a code stream.
  • the determining unit 620 is specifically configured to:
  • the refinement layer includes one or more points
  • the first matrix is a matrix formed by initial reconstruction values or reconstruction values of attribute information of neighboring points of the at least one refinement layer midpoint
  • the first vector is a vector formed by true values of the attribute information of the at least one refinement layer midpoint
  • the encoding unit 630 is specifically used for:
  • the residual value of the attribute information of the point in the at least one refinement layer, the filter identifier corresponding to the at least one refinement layer, and the filter coefficient corresponding to the at least one refinement layer are encoded to obtain the code stream; the filter identifier corresponding to the at least one refinement layer indicates the position of the filter coefficient corresponding to the at least one refinement layer in the code stream.
  • the determining unit 620 is specifically configured to:
  • the point cloud is divided into L refinement layers, where the refinement layer includes one or more points, and L is an integer greater than 0;
  • the target refinement layer is determined.
  • the determining unit 620 is specifically configured to:
  • the refinement layer where the target point is located belongs to the first N refinement layers arranged in the generation order of the refinement layers, neighboring points of the at least one refinement layer midpoint are selected from the first N refinement layers, and a matrix formed by initial reconstructed values of attribute information of the neighboring points of the at least one refinement layer midpoint is determined as the first matrix.
  • the determining unit 620 is specifically configured to:
  • the refinement layer where the target point is located belongs to a refinement layer after the first N refinement layers arranged in the generation order of the refinement layers, then neighboring points of the point in the at least one refinement layer are selected in the filtered refinement layers, and a matrix formed by reconstructed values of attribute information of the neighboring points of the point in the at least one refinement layer is determined as the first matrix.
  • the determining unit 620 is specifically configured to:
  • the filter coefficient of any point in the at least one refinement layer is set to be equal to the filter coefficient corresponding to the at least one refinement layer.
  • the at least one refinement layer includes the first N refinement layers arranged in the order of generation of the refinement layers, or the at least one refinement layer includes one or more refinement layers located after the first N refinement layers in the order of generation of the refinement layers, and N is an integer greater than 0.
  • the at least one refinement layer includes a single refinement layer that is located before the last refinement layer and after the first N refinement layers in the generation order of the refinement layers.
  • the determining unit 620 is specifically configured to:
  • the target detail layer includes one or more refinement layers
  • the second matrix is a matrix formed by initial reconstruction values or reconstruction values of attribute information of neighboring points of the target detail layer, and the second vector is a vector formed by true values of the attribute information of the target detail layer;
  • the encoding unit 630 is specifically used for:
  • the residual value of the attribute information of the midpoint of the target detail layer, the filter identifier corresponding to the target detail layer, and the filter coefficient corresponding to the target detail layer are encoded to obtain the code stream; the filter identifier corresponding to the target detail layer indicates the position of the filter coefficient corresponding to the target detail layer in the code stream.
  • the determining unit 620 is specifically configured to:
  • the point cloud is divided into L refinement layers, where the refinement layer includes one or more points, and L is an integer greater than 0;
  • the detail layer numbered i in the L detail layers includes: one or more detail layers numbered 0 to i arranged in the generation order of the detail layers, where i is an integer greater than 0;
  • the target detail layer is determined.
  • the determining unit 620 is specifically configured to:
  • the refinement layer where the target point is located belongs to the first N refinement layers arranged in the order of generation of the refinement layers, then the neighboring points of the midpoint of the target detail layer are selected from the first N refinement layers, and a matrix formed by initial reconstructed values of attribute information of the neighboring points of the midpoint of the target detail layer is determined as the second matrix.
  • the determining unit 620 is specifically configured to:
  • the refinement layer where the target point is located belongs to the refinement layer after the first N refinement layers arranged in the order of generation of the refinement layers, then the neighboring points of the midpoint of the target detail layer are selected in the filtered refinement layers, and the matrix formed by the reconstructed values of the attribute information of the neighboring points of the midpoint of the target detail layer is determined as the second matrix.
  • the determining unit 620 is specifically configured to:
  • the filter coefficient of any point in the target detail layer is set to be equal to the filter coefficient corresponding to the target detail layer.
  • the determining unit 620 is further configured to:
  • the initial reconstruction value of the attribute information of the target point is filtered based on the filter coefficient of the target point to determine the reconstruction value of the attribute information of the target point.
  • the determining unit 620 is specifically configured to:
  • the initial reconstruction value or the vector formed by the reconstruction value of the attribute information of the neighboring points of the target point is multiplied by the filter coefficient of the target point to obtain the reconstruction value of the attribute information of the target point.
  • the determining unit 620 is specifically configured to:
  • the refinement layer where the target point is located belongs to the first N refinement layers arranged in the order of generation of the refinement layers, then the neighboring points of the target point are selected in the first N refinement layers, and the vector formed by the initial reconstruction values of the attribute information of the neighboring points of the target point is multiplied by the filter coefficient of the target point to obtain the reconstructed value of the attribute information of the target point.
  • the determining unit 620 is specifically configured to:
  • the refinement layer where the target point is located belongs to the refinement layer after the first N refinement layers arranged in the order of generation of the refinement layers, then the neighboring points of the target point are selected in the filtered refinement layer, and the vector formed by the reconstructed values of the attribute information of the neighboring points of the target point is multiplied by the filter coefficient of the target point to obtain the reconstructed value of the attribute information of the target point.
  • the device embodiment and the method embodiment may correspond to each other, and similar descriptions may refer to the method embodiment. To avoid repetition, it will not be repeated here.
  • the decoder 500 shown in Figure 19 may correspond to the corresponding subject in the method 310 for executing the embodiment of the present application, and the aforementioned and other operations and/or functions of the various units in the decoder 500 are respectively for implementing the corresponding processes in various methods such as the method 310.
  • the encoder 600 shown in Figure 20 may correspond to the corresponding subject in the method 410 for executing the embodiment of the present application, that is, the aforementioned and other operations and/or functions of the various units in the encoder 600 are respectively for implementing the corresponding processes in various methods such as the method 410.
  • the various units in the decoder 500 or encoder 600 involved in the embodiment of the present application can be respectively or all merged into one or several other units to constitute, or some (some) units therein can also be split into multiple smaller units in function to constitute, which can achieve the same operation without affecting the realization of the technical effect of the embodiment of the present application.
  • the units involved in the above are divided based on logical functions. In practical applications, the function of a unit can also be implemented by multiple units, or the function of multiple units is implemented by one unit. In other embodiments of the present application, the decoder 500 or encoder 600 may also include other units. In practical applications, these functions can also be implemented by other units to assist in implementation, and can be implemented by multiple units in collaboration.
  • the decoder 500 or encoder 600 involved in the embodiment of the present application can be constructed by running a computer program (including program code) capable of executing each step involved in the corresponding method on a general computing device including a general-purpose computer such as a central processing unit (CPU), a random access storage medium (RAM), a read-only storage medium (ROM) and a storage element, and the encoding method or decoding method of the embodiment of the present application is implemented.
  • the computer program can be recorded on, for example, a computer-readable storage medium, and loaded into an electronic device through the computer-readable storage medium and run therein to implement the corresponding method of the embodiment of the present application.
  • the units mentioned above can be implemented in hardware form, can be implemented in software form, or can be implemented in the form of a combination of software and hardware.
  • the steps of the method embodiments in the embodiments of the present application can be completed by the hardware integrated logic circuit and/or software form instructions in the processor, and the steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to execute, or the hardware and software combination in the decoding processor can be executed.
  • the software can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc.
  • the storage medium is located in a memory, and the processor reads the information in the memory, and completes the steps in the method embodiments mentioned above in combination with its hardware.
  • FIG. 21 is a schematic structural diagram of an electronic device 700 provided in an embodiment of the present application.
  • the electronic device 700 at least includes a processor 710 and a computer-readable storage medium 720.
  • the processor 710 and the computer-readable storage medium 720 may be connected via a bus or other means.
  • the computer-readable storage medium 720 is used to store a computer program 721, which includes computer instructions, and the processor 710 is used to execute the computer instructions stored in the computer-readable storage medium 720.
  • the processor 710 is the computing core and control core of the electronic device 700, which is suitable for implementing one or more computer instructions, and is specifically suitable for loading and executing one or more computer instructions to implement the corresponding method flow or corresponding function.
  • the processor 710 may also be referred to as a central processing unit (CPU).
  • the processor 710 may include, but is not limited to, a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, discrete hardware components, and the like.
  • DSP digital signal processor
  • ASIC application-specific integrated circuit
  • FPGA field programmable gate array
  • the computer-readable storage medium 720 may be a high-speed RAM memory, or a non-volatile memory (Non-Volatile Memory), such as at least one disk memory; optionally, it may also be at least one computer-readable storage medium located away from the aforementioned processor 710.
  • the computer-readable storage medium 720 includes, but is not limited to: a volatile memory and/or a non-volatile memory.
  • the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.
  • the volatile memory may be a random access memory (RAM), which is used as an external cache.
  • RAM random access memory
  • DRAM dynamic RAM
  • SDRAM synchronous DRAM
  • DDR SDRAM double data rate synchronous dynamic random access memory
  • ESDRAM enhanced synchronous dynamic random access memory
  • SLDRAM synchronous link DRAM
  • Direct Rambus RAM Direct Rambus RAM, DR RAM
  • the electronic device 700 may be an encoder or encoding framework involved in an embodiment of the present application; a first computer instruction is stored in the computer-readable storage medium 720; the processor 710 loads and executes the first computer instruction stored in the computer-readable storage medium 720 to implement the corresponding steps in the encoding method provided in an embodiment of the present application; in other words, the first computer instruction in the computer-readable storage medium 720 is loaded by the processor 710 and the corresponding steps are executed. To avoid repetition, it will not be repeated here.
  • the electronic device 700 may be a decoder or decoding framework involved in an embodiment of the present application; a second computer instruction is stored in the computer-readable storage medium 720; the second computer instruction stored in the computer-readable storage medium 720 is loaded and executed by the processor 710 to implement the corresponding steps in the decoding method provided in an embodiment of the present application; in other words, the second computer instruction in the computer-readable storage medium 720 is loaded by the processor 710 and the corresponding steps are executed, which will not be repeated here to avoid repetition.
  • the present application also provides a coding and decoding system, including the encoder and decoder mentioned above.
  • the present application also provides a computer-readable storage medium (Memory), which is a memory device in the electronic device 700 for storing programs and data.
  • a computer-readable storage medium 720 is a memory device in the electronic device 700 for storing programs and data.
  • a computer-readable storage medium 720 can include both the built-in storage medium in the electronic device 700 and the extended storage medium supported by the electronic device 700.
  • the computer-readable storage medium provides a storage space, which stores the operating system of the electronic device 700.
  • one or more computer instructions suitable for being loaded and executed by the processor 710 are also stored in the storage space, and these computer instructions can be one or more computer programs 721 (including program codes).
  • the present application further provides a computer program product or a computer program, which includes a computer instruction, and the computer instruction is stored in a computer-readable storage medium.
  • the data processing device 700 can be a computer, and the processor 710 reads the computer instruction from the computer-readable storage medium 720, and the processor 710 executes the computer instruction, so that the computer executes the encoding method or decoding method provided in the various optional methods mentioned above.
  • the computer program product includes one or more computer instructions.
  • the process of the embodiment of the present application is run in whole or in part or the function of the embodiment of the present application is implemented.
  • the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.
  • the computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.
  • wired e.g., coaxial cable, optical fiber, digital subscriber line (DSL)
  • wireless e.g., infrared, wireless, microwave, etc.

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Abstract

本申请实施例提供了一种点云解码方法、点云编码方法、解码器和编码器，该点云解码方法包括：解码点云的码流，确定该点云中目标点的属性信息的残差值；基于该目标点的属性信息的残差值和该目标点的属性信息的预测值，确定该目标点的属性信息的初始重建值；确定该目标点的滤波系数；基于该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，确定该目标点的属性信息的重建值。本申请利用该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，提升了解码性能并实现了对重建点云的质量的增强。

Description

点云解码方法、点云编码方法、解码器和编码器 技术领域
本申请实施例涉及编解码技术领域，并且更具体地，涉及点云解码方法、点云编码方法、解码器和编码器。
背景技术
点云已开始普及到各个领域，例如，虚拟/增强现实、机器人、地理信息系统、医学领域等。随着扫描设备的基准度和速率的不断提升，可以准确地获取物体表面的大量点云，往往一个场景下就可以对应几十万个点。数量如此庞大的点也给计算机的存储和传输带来了挑战。因此，对点的压缩也就成为一个热点问题。
对于点云的压缩来说，对点云中当前点的属性预测值会依赖已编码点的属性重建值，而这种依赖关系使得前期已编码点的失真将不可避免地传播至当前点，最终会导致误差累积，降低了编码性能，而且由于解码器预测当前点的属性信息的过程和编码器预测当前点的属性信息的过程类似，因此也会影响解码性能。
发明内容
本申请提供了一种点云解码方法、点云编码方法、解码器和编码器，能够提升解码性能并实现对重建点云的质量的增强。
第一方面，本申请实施例提供了一种点云解码方法，包括：
解码点云的码流，确定该点云中目标点的属性信息的残差值；
基于该目标点的属性信息的残差值和该目标点的属性信息的预测值，确定该目标点的属性信息的初始重建值；
确定该目标点的滤波系数；
基于该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，确定该目标点的属性信息的重建值。
第二方面，本申请实施例提供了一种点云编码方法，包括：
基于点云中目标点的属性信息的真实值和该目标点的属性信息的预测值，确定该目标点的属性信息的残差值；
基于该目标点的近邻点，确定该目标点的滤波系数；
对该目标点的属性信息的残差值和该目标点的滤波系数进行编码，得到码流。
第三方面，本申请实施例提供了一种解码器，包括：
解码单元，用于解码点云的码流，确定该点云中目标点的属性信息的残差值；
重建单元，用于基于该目标点的属性信息的残差值和该目标点的属性信息的预测值，确定该目标点的属性信息的初始重建值；
确定单元，用于确定该目标点的滤波系数；
滤波单元，用于基于该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，确定该目标点的属性信息的重建值。
第四方面，本申请实施例提供了一种编码器，包括：
残差单元，用于基于点云中目标点的属性信息的真实值和该目标点的属性信息的预测值，确定该目标点的属性信息的残差值；
确定单元，用于基于该目标点的近邻点，确定该目标点的滤波系数；
编码单元，用于对该目标点的属性信息的残差值和该目标点的滤波系数进行编码，得到码流。
第五方面，本申请实施例提供了一种解码器，包括：
处理器，适于实现计算机指令；以及，
计算机可读存储介质，计算机可读存储介质存储有计算机指令，计算机指令适于由处理器加载并执行上文涉及的第一方面或其各实现方式中的解码方法。
在一种实现方式中，该处理器为一个或多个，该存储器为一个或多个。
在一种实现方式中，该计算机可读存储介质可以与该处理器集成在一起，或者该计算机可读存储介质与处理器分离设置。
第六方面，本申请实施例提供了一种编码器，包括：
处理器，适于实现计算机指令；以及，
计算机可读存储介质，计算机可读存储介质存储有计算机指令，计算机指令适于由处理器加载并执行上文涉及的第二方面或其各实现方式中的编码方法。
在一种实现方式中，该处理器为一个或多个，该存储器为一个或多个。
在一种实现方式中，该计算机可读存储介质可以与该处理器集成在一起，或者该计算机可读存储介质与处理器分离设置。
第七方面，本申请实施例提供了一种计算机可读存储介质，该计算机可读存储介质存储有计算机指令，该计算机指令被计算机设备的处理器读取并执行时，使得计算机设备执行上文涉及的第一方面涉及的点云解码方法或上文涉及的第二方面涉及的点云编码方法。
第八方面，本申请实施例提供了一种计算机程序产品或计算机程序，该计算机程序产品或计算机程序包括计算机指令，该计算机指令存储在计算机可读存储介质中。计算机设备的处理器从计算机可读存储介质读取该计算机指令，处理器执行该计算机指令，使得该计算机设备执行上文涉及的第一方面涉及的点云解码方法或上文涉及的第二方面涉及的点云编码方法。
第九方面，本申请实施例提供了一种码流，该码流如上文涉及的第一方面所述的方法中涉及的码流或如上文涉及的第二方面所述的方法生成的码流。
基于以上技术方案，本申请解码点云的码流，确定该点云中目标点的滤波系数；并基于该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，确定该目标点的属性信息的重建值。一方面，利用该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，可以提升该目标点的属性信息的重建值的准确度，进而提升了解码性能并实现了对重建点云的质量的增强。另一方面，利用该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，有利于采用当前点的重建值对点云中后续的点的属性信息进行预测，从而减少误差累积并实现了对重建点云的质量的增强。
附图说明
图1是本申请实施例提供的点云图像的示例。
图2是图1所示的点云图像的局部放大图。
图3是本申请实施例提供的具有的六个观看角度的点云图像的示例。
图4是本申请实施例提供的编码框架的示意性框图。
图5是本申请实施例提供的包围盒的示例。
图6是本申请实施例提供的对包围盒进行八叉树划分的示例。
图7至图9示出了莫顿码在二维空间中的排列顺序。
图10示出了莫顿码在三维空间中的排列顺序。
图11是本申请实施例提供的LOD层的示意性框图。
图12是本申请实施例提供的解码框架的示意性框图。
图13是本申请实施例提供的点云解码方法的示意性流程图。
图14是本申请实施例提供的点云解码方法的另一示意性流程图。
图15是本申请实施例提供的点云解码方法的另一示意性流程图。
图16是本申请实施例提供的点云编码方法的示意性流程图。
图17是本申请实施例提供的点云编码方法的另一示意性流程图。
图18是本申请实施例提供的点云编码方法的另一示意性流程图。
图19是本申请实施例提供的解码器的示意性框图。
图20是本申请实施例提供的编码器的示意性框图。
图21是本申请实施例提供的电子设备的示意结构图。
具体实施方式
下面将结合附图，对本申请实施例中的技术方案进行描述。
点云(Point Cloud)是空间中一组无规则分布的、表达三维物体或三维场景的空间结构及表面属性的离散点集。图1和图2分别示出了三维点云图像和局部放大图，可以看到点云表面是由分布稠密的点所组成的。
二维图像在每一个像素点均有信息表达，因此不需要额外记录其位置信息；然而点云中的点在三维空间中的分布具有随机性和不规则性，因此需要记录每一个点在空间中的位置，才能完整地表达一幅点云。与二维图像类似，点云中的每一个点均有对应的属性信息，通常为RGB颜色值，颜色值反映物体的色彩；对于点云来说，每一个点所对应的属性信息除了颜色以外，还可以是反射率(reflectance)值，反射率值反映物体的表面材质。点云中每个点可以包括几何信息和属性信息，其中，点云中每个点的几 何信息是指该点的笛卡尔三维坐标数据，点云中每个点的属性信息可以包括但不限于以下至少一种：颜色信息、材质信息、激光反射强度信息。颜色信息可以是任意一种色彩空间上的信息。例如，颜色信息可以是红绿蓝(Red Green Blue，RGB)信息。再如，颜色信息还可以是亮度色度(YCbCr，YUV)信息。其中，Y表示明亮度(Luma)，Cb(U)表示蓝色色度分量，Cr(V)表示红色色度分量。点云中的每个点都具有相同数量的属性信息。例如，点云中的每个点都具有颜色信息和激光反射强度两种属性信息。再如，点云中的每个点都具有颜色信息、材质信息和激光反射强度信息三种属性信息。
点云图像可具有的多个观看角度，例如，如图3所示的点云图像可具有的六个观看角度，点云图像对应的数据存储格式由文件头信息部分和数据部分组成，头信息包含了数据格式、数据表示类型、点云总点数、以及点云表示的内容。
点云可以灵活方便地表达三维物体或场景的空间结构及表面属性，并且由于点云通过直接对真实物体采样获得，在保证精度的前提下能提供极强的真实感，因而应用广泛，其范围包括虚拟现实游戏、计算机辅助设计、地理信息系统、自动导航系统、数字文化遗产、自由视点广播、三维沉浸远程呈现、生物组织器官三维重建等。
示例性地，可以基于应用场景可以将点云划分为两大类别，即机器感知点云和人眼感知点云。机器感知点云的应用场景包括但不限于：自主导航系统、实时巡检系统、地理信息系统、视觉分拣机器人、抢险救灾机器人等应用场景。人眼感知点云的应用场景包括但不限于：数字文化遗产、自由视点广播、三维沉浸通信等应用场景。相应的，可以基于点云的获取方式，将点云划分为密集型点云和稀疏型点云；也可基于点云的获取途径将点云划分为静态点云和动态点云，更具体可划分为三种类型的点云，即第一静态点云、第二类动态点云以及第三类动态获取点云。针对第一静态点云，物体是静止的，且获取点云的设备也是静止的；针对第二类动态点云，物体是运动的，但获取点云的设备是静止的；针对第三类动态获取点云，获取点云的设备是运动的。
示例性地，点云的采集途径包括但不限于：计算机生成、3D激光扫描、3D摄影测量等。计算机可以生成虚拟三维物体及场景的点云；3D激光扫描可以获得静态现实世界三维物体或场景的点云，每秒可以获取百万级点云；3D摄影测量可以获得动态现实世界三维物体或场景的点云，每秒可以获取千万级点云。具体而言，可通过光电雷达、激光雷达、激光扫描仪、多视角相机等采集设备，可以采集得到物体表面的点云。根据激光测量原理得到的点云，其可以包括点的三维坐标信息和点的激光反射强度(reflectance)。根据摄影测量原理得到的点云，其可以可包括点的三维坐标信息和点的颜色信息。结合激光测量和摄影测量原理得到点云，其可以可包括点的三维坐标信息、点的激光反射强度(reflectance)和点的颜色信息。这些技术降低了点云数据获取成本和时间周期，提高了数据的精度。例如，在医学领域，由磁共振成像(magnetic resonance imaging，MRI)、计算机断层摄影(computed tomography，CT)、电磁定位信息，可以获得生物组织器官的点云。这些技术降低了点云的获取成本和时间周期，提高了数据的精度。点云获取方式的变革降低了点云的获取难度，然而伴随着应用需求的增长，海量3D点云的处理遭遇存储空间和传输带宽限制的瓶颈。
举例来说，对于帧率为30fps(帧每秒)的点云视频，每一帧点云的点数为70万，由于每一帧点云中的每一个点具有坐标信息xyz(float)和颜色信息RGB(uchar)，因此，其10s长度的点云视频的数据量大约为0.7(million)×(4Byte×3+1Byte×3)×30fps×10s＝3.15GB。再如，对于YUV采样格式为4:2:0、帧率为24fps(帧每秒)且分辨率为1280×720的点云视频，其10s的数据量约为1280×720×12bit×24frames×10s≈0.33GB，即10s的两视角3D视频的数据量约为0.33×2＝0.66GB。由此可见，点云视频的数据量远超过相同时长的二维视频和三维视频的数据量。因此，为更好地实现数据管理，节省服务器存储空间，降低服务器与客户端之间的传输流量及传输时间，点云压缩成为促进产业发展的关键问题。
点云压缩一般采用点云几何信息和属性信息分别压缩的方式，在编码端，首先在几何编码器中编码点云几何信息，然后将重建几何信息作为附加信息输入到属性编码器中，以辅助点云的属性压缩；在解码端，首先在几何解码器中解码点云几何信息，然后将解码后的几何信息作为附加信息输入到属性解码器中，辅助点云的属性压缩。整个编解码器由预处理/后处理、几何编码/解码、属性编码/解码几部分组成。
示例性地，点云可以通过各种类型的编码框架和解码框架分别进行编码和解码。作为示例，编解码框架可以是运动图像专家组(Moving Picture Experts Group，MPEG)提供的几何点云压缩(Geometry Point Cloud Compression，G-PCC)编解码框架或视频点云压缩(Video Point Cloud Compression，V-PCC)编解码框架，也可以是音视频编码标准(Audio Video Standard，AVS)专题组提供的AVS-PCC编解码框架或点云压缩参考平台(PCRM)框架。G-PCC编解码框架可用于针对第一静态点云和第三类动态获取点云进行压缩，V-PCC编解码框架可用于针对第二类动态点云进行压缩。G-PCC编解码框架也称为测试模 型13(TMC13)，V-PCC编解码框架也称为测试模型2(TMC2)。G-PCC及AVS-PCC为针对静态的稀疏型点云的框架，其编解码框架大致相同。
下面以G-PCC框架为例对本申请实施例可适用的编解码框架进行说明。
在G-PCC编码框架中，先将输入点云进行切片(slice)划分后，然后对划分得到的切片进行独立编码。在切片中，点云的几何信息和点云中的点所对应的属性信息是分开进行编码的。G-PCC编码框架首先对几何信息进行编码；具体地，先对几何信息进行坐标转换，使点云全都包含在一个包围盒(bounding box)中；然后再进行量化，这一步量化主要起到缩放的作用，由于量化取整，使得一部分点的几何信息相同，根据参数来决定是否移除重复点，量化和移除重复点这一过程又被称为“体素化过程”。接下来，对包围盒进行基于八叉树(octree)的划分。其中根据八叉树划分层级深度的不同，几何信息的编码又分为基于八叉树(octree)的几何信息编码框架和基于三角面片集(triangle soup，trisoup)的几何信息编码框架。
在基于八叉树(octree)的几何信息编码框架中，先将包围盒进行八等分划分并得到8个子立方体，然后记录子立方体的占位比特(1为非空，0为空)，对非空的子立方体继续进行八等分，通常划分得到的叶子节点为1x1x1的单位立方体时停止划分。在这个过程中，利用节点与周围节点的空间相关性对占位比特进行预测(prediction)，并基于预测结果选择相应的二进制算数编码器进行算数编码，以实现基于上下文模型的自适应二进制算术编码(Context-based Adaptive Binary Arithmetic Coding，CABAC)并生成二进制码流。
在基于三角面片集的几何信息编码框架中，同样也要先进行八叉树划分，但区别于基于八叉树(octree)的几何信息编码框架，基于三角面片集的几何信息编码框架不需要将点云逐级划分到边长为1x1x1的单位立方体，而是划分到块(block)边长为W时停止划分，基于每个块中点云的分布所形成的表面，得到该表面与块的十二条边所产生的至多十二个交点(vertex)，然后依次编码每个块的交点的坐标并生成二进制码流。
G-PCC编码框架在完成几何信息编码后对几何信息进行重建，并使用重建的几何信息对点云的属性信息进行编码。点云的属性编码主要是对点云中点的颜色信息进行编码。首先，G-PCC编码框架可以对点的颜色信息进行颜色空间转换，例如，当输入点云中点的颜色信息使用RGB颜色空间表示时，G-PCC编码框架可以将颜色信息从RGB颜色空间转换到YUV颜色空间。然后，G-PCC编码框架利用重建的几何信息对点云进行重新着色，使得未编码的属性信息与重建的几何信息对应起来。在颜色信息编码中，主要有两种变换方法，一种方法是依赖于细节层(Level of Detail，LOD)划分的基于距离的提升变换，另一种方法是直接进行区域自适应分层变换(Region Adaptive Hierarchal Transform，RAHT)，这两种方法都会将颜色信息从空间域变换到频域，得到高频系数和低频系数，最后对系数进行量化和编码，并生成二进制码流。
图4是本申请实施例提供的编码框架的示意性框图。
如图4所示，编码框架100可以从采集设备获取点云的几何信息和属性信息。点云的编码包括几何编码和属性编码。在一个实施例中，几何编码的过程包括：对从采集设备获取的点云进行坐标变换、量化去除重复点等预处理；基于八叉树(octree)的划分后进行编码形成几何码流。
如图4所示，编码器的位置编码过程可通过以下单元实现：
坐标变换(Tanmsform coordinates)单元101、量化和移除重复点(Quantize and remove points)单元102、八叉树分析(Analyze octree)单元103、几何重建(Reconstruct geometry)单元104和第一算术编码(Arithmetic encode)单元105。
坐标变换单元101可用于将点云中点的世界坐标变换为相对坐标。例如，点的几何坐标分别减去xyz坐标轴的最小值，相当于去直流操作，以实现将点云中的点的坐标从世界坐标变换为相对坐标，并使点云全都包含在一个包围盒(bounding box)中。量化和移除重复点单元102可通过量化减少坐标的数目；量化后原先不同的点可能被赋予相同的坐标，基于此，可通过去重操作将重复的点删除；例如，具有相同量化位置和不同属性信息的多个点可通过属性变换合并到一个点中。在本申请的一些实施例中，量化和移除重复点单元102为可选的单元模块。八叉树分析单元103可利用八叉树(octree)编码方式编码量化的点的几何信息。例如，将点云按照八叉树(octree)的形式进行规则化处理，由此，点的位置可以和八叉树(octree)的位置一一对应，通过统计八叉树中有点的位置，并将其标识(flag)记为1，以进行几何编码。第一算术编码单元105可以采用熵编码方式对八叉树分析单元103输出的几何信息进行算术编码，即将八叉树分析单元103输出的几何信息利用算术编码方式生成几何码流；几何码流也可称为几何比特流(geometry bit stream)。
下面对点云的规则化处理方法进行说明。
由于点云在空间中无规则分布的特性，给编码过程带来挑战，作为一种可能的实现方式，可以采用 递归八叉树的结构，将点云中的点规则化地表达成立方体的中心。例如如图5所示，可以将整幅点云放置在一个正方体包围盒内，此时点云中点的坐标可以表示为(x k,y k,z k),k＝0,…,K-1，其中K是点云的总点数，则点云在x轴、y轴和z轴方向上的边界值分别为：
x min＝min(x 0,x 1,…,x K-1)；
y min＝min(y 0,y 1,…,y K-1)；
z min＝min(z 0,z 1,…,z K-1)；
x max＝max(x 0,x 1,…,x K-1)；
y max＝max(y 0,y 1,…,y K-1)；
z max＝max(z 0,z 1,…,z K-1)。
此外，包围盒的原点(x origin,y origin,z origin)可以计算如下：
x origin＝int(floor(x min))；
y origin＝int(floor(y min))；
z origin＝int(floor(z min))。
其中，floor()表示向下取整运算或向下舍入运算。int()表示取整运算。
基于此，编码器可以基于边界值和原点的计算公式，计算包围盒在x轴、y轴和z轴方向上的尺寸如下：
BoudingBoxSize_x＝int(x max-x origin)+1；
BoudingBoxSize_y＝int(y max-y origin)+1；
BoudingBoxSize_z＝int(z max-z origin)+1。
如图6所示，编码器得到包围盒在x轴、y轴和z轴方向上的尺寸后，首先对包围盒进行八叉树划分，每次得到八个子块，然后对子块中的非空块(包含点的块)进行再一次的八叉树划分，如此递归划分直到某个深度，将最终大小的非空子块称作体素(voxel)，每一个voxel中包含一个或多个点，将这些点的几何位置归一化为voxel的中心点，该中心点的属性值可以为体素中全部点的属性值的平均值。将点云规则化为空间中的块，有利于描述点云中点与点之前的位置关系，进而有利于设计特定的编码顺序，基于此编码器可基于确定的编码顺序编码每一个体素，即编码每一个体素所代表的点(或称节点)。
编码器几何编码完成后对几何信息进行重建，利用重建的几何信息来对属性信息进行编码。属性编码过程包括：通过给定输入点云的几何信息的重建信息和属性信息的真实值，选择三种预测模式的一种进行点云预测，对预测后的结果进行量化，并进行算术编码形成属性码流。
如图4所示，编码器的属性编码过程可通过以下单元实现：
颜色空间变换(Transform colors)单元110、属性变换(Transfer attributes)单元111、区域自适应分层变换(Region Adaptive Hierarchical Transform，RAHT)单元112、预测变化(predicting transform)单元113以及提升变化(lifting transform)单元114、量化(Quantize)单元115以及第二算术编码单元116。
颜色空间变换单元110可用于将点云中点的RGB色彩空间变换为YCbCr格式或其他格式。属性变换单元111可用于变换点云中点的属性信息，以最小化属性失真。例如，在几何有损编码的情况下，由于几何信息在几何编码之后有所异动，因此需要属性变换单元111为几何编码后的每一个点重新分配属性值，使得重建点云和重建前的点云的属性误差最小。例如，所述属性信息可以是点的颜色信息。属性变换单元111可用于得到点的属性原始值，经过属性变换单元111变换得到点的属性原始值后，可选择任一种预测单元，对点云中的点进行预测。用于对点云中的点进行预测的单元可包括：RAHT 112、预测变化(predicting transform)单元113以及提升变化(lifting transform)单元114中的至少一项。换言之，RAHT 112、预测变化(predicting transform)单元113以及提升变化(lifting transform)单元114中的任一项可用于对点云中点的属性信息进行预测，以得到点的属性预测值，进而可基于点的属性预测值得到点的属性信息的残差值。例如，点的属性信息的残差值可以是点的属性原始值减去点的属性预测值。量化单元115可用于量化点的属性信息的残差值。例如，若所述量化单元115和所述预测变换单元113相连，则所述量化单元115可用于量化所述预测变换单元113输出的点的属性信息的残差值。例如，对预测变换单元113输出的点的属性信息的残差值使用量化步长进行量化，以实现提升系统性能。第二算术编码单元116可使用零行程编码(Zero run length coding)对点的属性信息的残差值进行熵编码，以得到属性码流。所述属性码流可以是比特流信息。
预测变换单元113可用于获取点云的原始顺序(original order)以及基于点云的原始顺序将点云划分为细节层(level of detail，LOD)，预测变换单元113获取点云的LOD后，可对LOD中点的属性信息依次进行预测，进而计算得到点的属性信息的残差值，以便后续单元基于点的属性信息的残差值进行后续的量化编码处理。对LOD中的每一个点，基于当前点所在的LOD上的邻居点搜索结果找到位于 当前点之前的3个邻居点，然后利用3个邻居点中的至少一个邻居点的属性重建值，对当前点进行预测，得到当前点的属性预测值；基于此，可基于当前点的属性预测值和当前点的属性原始值得到当前点的属性信息的残差值。
预测变换单元113获取的点云的原始顺序可以是预测变换单元113对当前点云进行莫顿排序的得到的排列顺序。换言之，编码器通过对当前点云进行莫顿排序得到当前点云的原始顺序后，可按照当前点云的原始顺序对点云中的点进行层的划分，以得到当前点云的LOD，进而基于LOD对点云中的点的属性信息进行预测。
图7至图9示出了莫顿码在二维空间中的排列顺序。
如图7所示，编码器在2*2个块所形成的二维空间中可以采用“z”字形莫顿排列顺序。如图8所示，编码器在4个2*2个块所形成的二维空间中可以采用“z”字形莫顿排列顺序，其中，每个2*2个块所形成的二维空间中也可以采用“z”字形莫顿排列顺序，最终可以得到编码器在4*4个块所形成的二维空间中采用的莫顿排列顺序。如图9所示，编码器在4个4*4个块所形成的二维空间中可以采用“z”字形莫顿排列顺序，其中，每4个2*2个块所形成的二维空间以及每个2*2个块所形成的二维空间中也可以采用“z”字形莫顿排列顺序，最终可以得到编码器在8*8个块所形成的二维空间中采用的莫顿排列顺序。
图10示出了莫顿码在三维空间中的排列顺序。
如图10所示，莫顿排列顺序不仅适用于二维空间，也可以将其扩展到三维空间中，例如图10中展示了16个点，每个“z”字内部，每个“z”与“z”之间的莫顿排列顺序都是先沿x轴方向编码，再沿y轴，最后沿z轴。
在一个实施例中，可根据点云中点的几何信息，获取点与点之间的欧式距离；然后根据点与点之间的欧式距离将点划分为不同的LOD。
示例性地，可以将点与点之间的欧式距离进行排序后，将不同范围的欧式距离划分为不同的LOD。在一种具体实现中，可以随机挑选一个点，作为第一LOD；然后计算剩余点与该点的欧式距离，并将欧式距离符合第一阈值要求的点，归为第二LOD；接着，获取第二LOD中点的质心，计算除第一LOD、第二LOD以外的点与该质心的欧式距离，并将欧式距离符合第二阈值的点，归为第三LOD；以此类推，将所有的点都归到LOD层中。进一步的，可以通过调整欧式距离的阈值，可以使得每层LOD的点的数量是递增的。
示例性地，可以基于不同的欧式距离将点云划分为细化层，再基于细化层获取LOD层。
例如，假设(R l) l＝0...L-1表示细化层(Refinement levels),L是LOD的层数。构建LOD具体步骤如下：
(1)、用户自定义L个欧式距离(d l) l＝0...L-1，划分L个细化层(R l) l＝0...L-1；
(2)、将所有的点标记为未访问，并将已访问点的集合V设为空集；
(3)、细化层R l生成过程如下：
遍历所有点，如果已经访问当前点，则忽略；否则，计算当前点距离集合V的最小距离D；若D小于d l，则忽略当前点；否则，将当前点标记为已访问并加入到R l和V中；重复此过程，直到点云中的所有点被遍历到。
(4)、取细化层R 0,R 1,...,R l的并集作为细节层LOD l(即
Figure PCTCN2022125110-appb-000001
)；
(5)、重复此过程，直到点云中的所有点均已被访问。
应理解，LOD层划分的方式还可以采用其它方式，本申请对此不进行限制。需要说明的是，可以直接将点云划分为一个或多个LOD层，也可以先将点云划分为多个点云切块(slice)，再将每一个点云切块划分为一个或多个LOD层。例如，可将点云划分为多个点云切块，每个点云切块的点的个数可以在55万-110万之间。每个点云切块可看成单独的点云。每个点云切块又可以划分为多个LOD层，每个LOD层包括多个点。
图11是本申请实施例提供的LOD层的示意性框图。
如图11所示，点云可以包括按照原始顺序(original order)排列的多个点，即P0，P1，P2，P3，P4，P5，P6，P7，P8以及P9，假设基于点与点之间的欧式距离将点云划分为3个LOD层，即LOD0、LOD1以及LOD2。其中，LOD0可包括P0，P5，P4以及P2，LOD1可包括LOD0中的点，P1，P6以及P3，LOD2可包括LOD0中的点，LOD1中的点，P9，P8以及P7。此时，LOD0、LOD1以及LOD2可用于形成该点云的基于LOD的顺序(LOD-based order)，即P0，P5，P4，P2，P1，P6，P3，P9，P8以及P7。其中，基于LOD的顺序可作为该点云的编码顺序。
示例性地，编码器在对当前点的属性信息进行预测时，首先基于点云的原始顺序生成一个或多个细化层，随后根据细化层的生成顺序从前期已编码的细化层中寻找当前点的最近邻居，并基于寻找到的最近邻居对当前点的属性信息进行预测，以得到当前点的属性预测值。例如，编码器在对当前点的属性信 息进行预测时，可先基于当前点的邻居点的搜索结果，创建多个候选预测值，然后利用率失真优化(Rate distortion optimization，RDO)技术从多个候选预测值中选择针对当前点预测性能最佳的属性预测值，然后将最佳的属性预测值作为当前点的属性预测值，并与属性原始值进行做差的方式来编码属性信息以得到点云的属性码流。
示例性地，编码器可以先利用多个预测模式(predMode)，基于当前点的邻居点的搜索结果创建多个候选预测值。
其中，该多个预测模式(predMode)的索引的取值可以为0～3，编码器对当前点的属性信息进行编码时，编码器先基于当前点所在的LOD上的邻居点搜索结果找到位于当前点之前的3个邻居点，然后基于索引取值为0～3的预测模式获取多个候选预测值。其中，索引为0的预测模式指基于3个邻居点与当前点之间的距离将3个邻居点的重建属性值的加权平均值确定为当前点的候选预测值；索引为1的预测模式指：将3个邻居点中最近邻居点的属性重建值，作为当前点的候选预测值；索引为2的预测模式指：将次近邻居点的属性重建值，作为当前点的候选预测值；索引为3的预测模式指：将3个邻居点中除了最近邻居点和次近邻居点之外的邻居点的属性重建值，作为当前点的候选预测值；在基于上述各种预测模式得到当前点的多个候选预测值后，可以利用率失真优化(Rate distortion optimization，RDO)技术选择最佳的属性预测值，然后将所选的最佳的属性预测值作为当前点的属性预测值进行后续编码。
下面结合表1对创建多个候选预测值的原理进行说明。
表1
Figure PCTCN2022125110-appb-000002
如表1所示，对当前点P2的属性信息进行编码时，索引为0的预测模式指：将邻居点P0、P5以及P4的距离将邻居点P0、P5以及P4的重建属性值的加权平均值，确定为当前点P2的候选预测值；索引为1的预测模式指：将最近邻居点P4的属性重建值，确定为当前点P2的候选预测值；索引为2的预测模式指：将次近邻点P5的属性重建值，确定为当前点P2的候选预测值；索引为3的预测模式指：将三近邻点P0的属性重建值，确定为当前点P2的候选预测值；最后，利用RDO选择针对当前点P2的最佳的属性预测值。
值得注意的是，码流中还可以写入当前点使用的预测模型的索引，也可以不写入当前点使用的预测模型的索引。例如，当前点使用的预测模型的索引为0时，码流中不需要对当前点使用的预测模型的索引进行编码，但是，当前点使用的预测模型的索引为通过RDO选择的预测模式的索引为1，2或3时，码流中需要对当前点使用的预测模型的索引进行编码，即需要将所选的预测模式的索引编码到属性码流。
示例性地，编码器利用RDO技术选择最佳的属性预测值时，可以先对当前点的至少一个邻居点计算其属性的最大差异maxDiff，将maxDiff与设定的阈值进行比较，如果小于设定的阈值则使用邻居点属性值加权平均的预测模式；否则对该点使用RDO技术选择最优预测模式。具体地，编码器计算当前点的至少一个邻居点的属性最大差异maxDiff，例如首先计算当前点的至少一个邻居点在R分量上的最大差异，即max(R1,R2,R3)-min(R1,R2,R3)；类似的，编码器计算当前点的至少一个邻居点在G以及B分量上的最大差异，即max(G1,G2,G3)-min(G1,G2,G3)以及max(B1,B2,B3)-min(B1,B2,B3)，然后选择R、G、B分量中的最大差异值作为maxDiff，即maxDiff＝max(max(R1,R2,R3)-min(R1,R2,R3),max(G1,G2,G3)-min(G1,G2,G3),max(B1,B2,B3)-min(B1,B2,B3))；编码器将得到的maxDiff与设定的阈值比较，若小于设定的阈值则当前点的预测模式设为0，即predMode＝0；若大于或等于设定的阈值，则编码器对当前点可以使用RDO技术确定当前点使用的预测模式。
对于RDO技术，编码器可以对当前点的每种预测模式计算得到对应的率失真代价，然后选取率失真代价最小的预测模式，即最优预测模式作为当前点的属性预测模式。例如，编码器可通过以下公式计算索引为1、2或3的预测模式的率失真代价：J indx_i＝D indx_i+λ×R indx_i；其中，J indx_i表示当前点采用索引为i的预测模式时的率失真代价，D为attrResidualQuant三个分量的和，即D＝attrResidualQuant[0]+attrResidualQuant[1]+attrResidualQuant[2]。λ根据所述当前点的量化参数确定，R indx_i表示当前点采用索引为i的预测模式时得到的量化残差值在码流中所需的比特数。
示例性地，编码器确定出针对当前点的最佳的属性预测值attrPred后，可以将当前点的属性原始值attrValue与当前点的属性预测值attrPred相减并对其结果进行量化，得到当前点的量化残差值 attrResidualQuant。例如，attrResidualQuant＝(attrValue-attrPred)/Qs；其中，attrResidualQuant表示当前点的量化残差值，attrValue表示当前点的属性原始值，attrPred表示当前点的属性预测值，Qstep表示量化步长。其中，Qstep由量化参数(Quantization Parameter，Qp)计算得到。
示例性地，当前点可以作为后续点的邻居点，即可以利用当前点的属性重建值，对后续点的属性信息进行预测。例如，编码器可通过以下公式基于当前点的量化残差值和属性预测值，来确定当前点的属性重建值：Recon＝attrResidualQuant×Qstep+attrPred；其中，Recon表示基于当前点的量化残差值确定的当前点的属性重建值，attrResidualQuant表示当前点的量化残差值，Qstep表示量化步长，attrPred表示当前点的属性预测值。其中，Qstep由量化参数(Quantization Parameter，Qp)计算得到。
需要说明的是，本申请中涉及的术语也可称为其他名称。例如，当前点的属性预测值(predictedvalue)也可称为属性信息的预测值或颜色预测值(predictedColor)。当前点的属性原始值也可称为当前点的属性信息的原始值或颜色原始值。当前点的残差值也可称为当前点的属性原始值与当前点的属性预测值的差值，也可称为当前点的颜色残差值(residualColor)。当前点的属性重建值(reconstructedvalue)也可称为当前点的属性信息的重建值，或称为颜色重建值(reconstructedColor)。
图12是本申请实施例提供的解码框架200的示意性框图。
在解码过程中，解码框架200从编码设备获取点云的码流后，可通过解析码流得到点云中的点的几何信息和属性信息。其中点云的解码包括位置解码和属性解码。位置解码的过程包括：对几何码流进行算术解码；然后按照和几何编码相同的方式构建出八叉树结构，并基于构建出的八叉树结构得到几何信息的重建信息；接着，对点的几何信息的重建信息进行坐标变换，得到变换后的几何信息。点的几何信息也可称为点的位置信息。属性解码过程包括：通过解析属性码流，获取点云中点的属性残差值；对点的属性残差值进行反量化，得到反量化后的点的属性残差值；基于位置解码过程中获取的点的几何信息的重建信息，选择三种预测模式的一种进行点云预测，得到点的属性预测值，并基于反量化后的点的属性残差值和点的属性预测值，确定点的属性重建值；然后经过颜色空间反变换得到解码后的属性信息。
如图12所示，位置解码可通过以下单元实现：第一算数解码单元201、八叉树(octree)合成单元202、几何重建(Reconstruct geometry)单元203以及坐标反变化(inverse transform coordinates)单元204。属性编码可通过以下单元实现：第二算数解码单元210、反量化(inverse quantize)单元211、RAHT单元212、预测变化(predicting transform)单元213、提升变化(lifting transform)单元214以及颜色空间反变换(inverse transform colors)单元215。需要说明的是，解压缩是压缩的逆过程，类似的，解码框架200中的各个单元的功能可参见编码框架100中相应的单元的功能。例如，预测变化单元213可根据点云中点与点之间的欧式距离将点云划分为多个LOD；然后，依次对LOD中点的属性信息进行解码。例如，第二算数解码单元210可用于解析零行程编码技术中零的数量(zero_cnt)，以基于零的数量对残差进行解码。为避免重复，此处不再赘述。
值得注意的是，编码器在对当前点的属性信息进行预测时，首先基于点云的原始顺序生成一个或多个细化层，随后根据细化层的生成顺序从前期已编码的细化层中寻找当前点的最近邻居，并基于寻找到的最近邻居对当前点的属性信息进行预测，以得到当前点的属性预测值。然而，在上述过程中，当前细化层中点的属性预测值会依赖已编码细化层中点的属性重建值，而这种依赖关系使得前期已编码点的失真将不可避免地传播至当前点，最终会导致误差累积，降低了编码性能，而且由于解码器预测当前点的属性信息的过程和编码器预测当前点的属性信息的过程类似，因此也会影响解码性能。有鉴于此，本申请提供了一种点云解码方法，能够提升解码性能并实现对重建点云的质量的增强。
图13是本申请的实施例提供的点云解码方法310的示意性流程图。应理解，该点云解码方法310可由解码器或解码框架执行。例如应用于图12所示的解码框架200。为便于描述，下面以解码器为例进行说明。
如图13所示，该点云解码方法310可包括：
S311，解码器解码点云的码流，确定该点云中目标点的属性信息的残差值；
S312，该解码器基于该目标点的属性信息的残差值和该目标点的属性信息的预测值，确定该目标点的属性信息的初始重建值；
S313，该解码器确定该目标点的滤波系数；
S314，该解码器基于该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，确定该目标点的属性信息的重建值。
换言之，该解码器基于该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波得到滤波后的重建值，并利用滤波后的重建值覆盖或替换该目标点的属性信息的初始重建值。
此外，该解码器可采用更新后的重建值对后续点的属性信息进行预测。
示例性地，该目标点的滤波系数可以是该码流中携带的信息，也可以是解码器确定的参数，本申请 对此不作限定。
本实施例中，该解码器解码点云的码流，确定该点云中目标点的滤波系数；并基于该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，确定该目标点的属性信息的重建值。一方面，利用该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，可以提升该目标点的属性信息的重建值的准确度，进而提升了解码性能并实现了对重建点云的质量的增强。另一方面，利用该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，有利于采用当前点的重建值对点云中后续的点的属性信息进行预测，从而减少误差累积并实现了对重建点云的质量的增强。
本申请提供的方案在G-PCC参考软件TMC13V14.0上进行了测试并得到了如表2所示的测试结果。
其中，表2示出了在几何无损、属性近无损编码方式(lossless geometry,near lossless attribute)编码方式下的率失真(Bit distortion，BD-rate)，BD-Rate反映的是两种情况下(有无滤波)PSNR曲线的差异，BD-Rate减少时，表示在PSNR相等的情况下，码率减少，性能提高；反之性能下降。即BD-Rate下降越多则压缩效果越好。
此外，Cat1-A表示仅包括点的反射率信息的点的点云，Cat1-A average表示在几何无损、属性近无损编码方式下Cat1A的各个分量的平均BD-rate；Cat1-B表示仅包括点的颜色信息的点的点云，Cat1-B average表示在几何无损、属性近无损编码方式下Cat1-B的各个分量的平均BD-rate；Cat3-fused表示包括点的颜色信息和其他属性信息的点的点云。Cat3-fused average表示在几何无损、属性近无损编码方式下Cat3-fused的各个分量的平均BD-rate；总平均值(Overall average)表示Cat1-A至Cat3-fused在几何无损、属性近无损编码方式下的平均BD-rate。L、Cb和Cr(也叫称为Y，U，V)表示点云颜色的亮度和色度三种分量的性能。
表2
Figure PCTCN2022125110-appb-000003
如表2所示，本申请提供的方案对Cat1-A、Cat1-B和Cat3-fused均具有明显的性能提升。具体地，与改进前的原方案相比本申请提供的方案在Y,Cb,Cr分量上分别获得了-0.0％,-4.4％,以及-6.5％的增益。
值得注意的是，本申请实施例中涉及的该目标点的属性信息的重建值可以是该目标点最终的重建值。换言之，该解码器基于该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，确定的该目标点的属性信息的重建值为该目标点最终的重建值。该目标点最终的重建值可以理解为用于解码点云中该目标点的属性信息的值。
在一些实施例中，该S313可包括：
该解码器基于该目标点所在的目标细化层，确定至少一个细化层；该细化层包括一个或多个点；确定该至少一个细化层对应的滤波系数；该解码器基于该至少一个细化层对应的滤波系数，确定该至少一个细化层中的任意点的滤波系数。
示例性地，该码流的结构可如下所示：
Flagbits_0，coefbits_0[k]，...，Flagbits_m-1，coefbits_m-1[k]，bits_0，...，bits_n-2，bits_n-1。
其中，n表示点云中点的总个数，m表示点云中进行了维纳滤波的细化层的数量，k表示各个细化层对应的滤波系数的长度(也称为维纳滤波器的阶数，或维纳滤波器的长度)。k＝16。bits,flagbits,coefbits分别是点云中点的属性信息的残差值、各个细化层对应的滤波标识、各个细化层对应的滤波系数。
在一些实施例中，该解码器先解码该码流，确定该点云的几何信息的重建值；接着，该解码器基于该点云的几何信息的重建值，将该点云划分为L个细化层，该细化层包括一个或多个点，L为大于0的整数；然后，该解码器基于该目标点的索引，确定该目标细化层。
示例性地，解码器可以基于不同的欧式距离将点云划分为L个细化层，再基于细化层获取L个LOD层。
例如，假设(R l) l＝0...L-1表示细化层(Refinement levels),L是LOD的层数。构建LOD具体步骤如下：
(1)、用户自定义L个欧式距离(d l) l＝0...L-1，划分L个细化层(R l) l＝0...L-1；
(2)、将所有的点标记为未访问，并将已访问点的集合V设为空集；
(3)、细化层R l生成过程如下：
遍历所有点，如果已经访问当前点，则忽略；否则，计算当前点距离集合V的最小距离D；若D小于d l，则忽略当前点；否则，将当前点标记为已访问并加入到R l和V中；重复此过程，直到点云中的所有点被遍历到。
(4)、取细化层R 0,R 1,...,R l的并集作为细节层LOD l(即
Figure PCTCN2022125110-appb-000004
)；
(5)、重复此过程，直到点云中的所有点均已被访问。
应理解，LOD层划分的方式还可以采用其它方式，本申请对此不进行限制。需要说明的是，可以直接将点云划分为一个或多个LOD层，也可以先将点云划分为多个点云切块(slice)，再将每一个点云切块划分为一个或多个LOD层。例如，可将点云划分为多个点云切块，每个点云切块的点的个数可以在55万-110万之间。每个点云切块可看成单独的点云。每个点云切块又可以划分为多个LOD层，每个LOD层包括多个点。
示例性地，假设该点云中点的顺序为按照细化层的生成顺序，则该解码器可基于该目标点的索引和各个细化层中点的数量，基于该目标点所在的目标细化层，确定至少一个细化层。
在一些实施例中，该解码器解码该码流，确定该至少一个细化层对应的滤波标识；该至少一个细化层对应的滤波标识指示该至少一个细化层对应的滤波系数在该码流中的位置；然后，该解码器基于该至少一个细化层对应的滤波标识解码该码流，确定该至少一个细化层对应的滤波系数。
示例性地，该至少一个细化层对应的滤波标识通过指示该至少一个细化层的标识，来指示该至少一个细化层对应的滤波系数在该码流中的位置。
例如，假设该码流的结构可如下所示：Flagbits_0，coefbits_0[k]，...，Flagbits_m-1，coefbits_m-1[k]，bits_0，...，bits_n-2，bits_n-1。其中，n表示点云中点的总个数，m表示点云中进行了维纳滤波的细化层的数量，k表示各个细化层对应的滤波系数的长度(也称为维纳滤波器的阶数，或维纳滤波器的长度)。k＝16。bits,flagbits,coefbits分别是点云中点的属性信息的残差值、各个细化层对应的滤波标识、各个细化层对应的滤波系数；这种情况下，该至少一个细化层对应的滤波标识指示该至少一个细化层的标识，相当于指示了该至少一个细化层对应的滤波系数为该码流中位于该至少一个细化层对应的滤波标识之后的滤波系数。例如，Flagbits_0指示该至少一个细化层的标识，Flagbits_0后面的coefbits_0[k]可以默认为Flagbits_0指示的细化层对应的滤波系数。
当然，在其他可替代实施例中，该至少一个细化层对应的滤波标识指示是否携带有该至少一个细化层对应的滤波系数，例如，若该码流中携带有该至少一个细化层对应的滤波标识，则说明该码流携带有该至少一个细化层对应的滤波系数。若该码流中未携带该至少一个细化层对应的滤波标识，则说明该码流中未携带该至少一个细化层对应的滤波系数。甚至于，该至少一个细化层对应的滤波标识可以指示是否对该至少一个细化层是否进行滤波；例如，该解码器可以采用类似编码器确定该至少一个细化层对应的滤波系数的方式，确定该至少一个细化层对应的滤波系数时，该至少一个细化层对应的滤波标识可以指示是否对该至少一个细化层是否进行滤波。本申请对此不作具体限定。
在一些实施例中，该解码器将该至少一个细化层中的任意点的滤波系数，设置为等于该至少一个细化层对应的滤波系数。
换言之，该至少一个细化层中的所有点的滤波系数为相同的滤波系数。
当然，在其他可替代实施例中，也可以在该码流中携带该至少一个细化层中的各个点的滤波系数，本申请对此不作具体限定。
在一些实施例中，该至少一个细化层包括按照细化层的生成顺序排列的前N个细化层，或该至少一个细化层包括按照细化层的生成顺序位于该前N个细化层之后的一个或多个细化层，N为大于0的整数。
由于该前N个细化层中的点较少，本实施例对该前N个细化层进行合并滤波，并将滤波后的值覆盖初始重建值得到更新后的重建值，能够提高滤波准确度和解码效率。对编码器来说，降低了滤波系数的计算量，并提升了编码效率。
示例性地，N的取值可以为6或其他数值，本申请对此不作具体限定。
在一些实施例中，该至少一个细化层包括按照细化层的生成顺序位于最后一个细化层之前、且位于该前N个细化层之后的单个细化层。
由于该按照细化层的生成顺序位于最后一个细化层之前、且位于该前N个细化层之后的单个细化层中的点较多，本实施例将该单个细化层进行独立滤波，能够提高滤波准确度。
图14是本申请实施例提供的点云解码方法320的示意性流程图。
如图14所示，该点云解码方法320可包括：
S321，解码器解码点云的码流，确定该点云中目标点的属性信息的残差值，并对目标点的属性信息进行预测得到该目标点的属性信息的预测值。
示例性地，解码器在对目标点的属性信息进行预测时，首先基于点云的原始顺序生成一个或多个细化层，随后根据细化层的生成顺序从前期已编码的细化层中寻找目标点的最近邻居，并基于寻找到的最近邻居对目标点的属性信息进行预测，以得到目标点的属性预测值。例如，解码器在对目标点的属性信息进行预测时，可先基于目标点的邻居点的搜索结果，创建多个候选预测值，然后利用率失真优化(Rate distortion optimization，RDO)技术从多个候选预测值中选择针对目标点预测性能最佳的属性预测值，然后将最佳的属性预测值作为目标点的属性预测值，并与属性原始值进行做差的方式来编码属性信息以得到点云的属性码流。
示例性地，解码器可以先利用多个预测模式(predMode)，基于目标点的邻居点的搜索结果创建多个候选预测值。
其中，该多个预测模式(predMode)的索引的取值可以为0～3，解码器对目标点的属性信息进行编码时，解码器先基于目标点所在的LOD上的邻居点搜索结果找到位于目标点之前的3个邻居点，然后基于索引取值为0～3的预测模式获取多个候选预测值。其中，索引为0的预测模式指基于3个邻居点与目标点之间的距离将3个邻居点的重建属性值的加权平均值确定为目标点的候选预测值；索引为1的预测模式指：将3个邻居点中最近邻居点的属性重建值，作为目标点的候选预测值；索引为2的预测模式指：将次近邻居点的属性重建值，作为目标点的候选预测值；索引为3的预测模式指：将3个邻居点中除了最近邻居点和次近邻居点之外的邻居点的属性重建值，作为目标点的候选预测值；在基于上述各种预测模式得到目标点的多个候选预测值后，可以利用率失真优化(Rate distortion optimization，RDO)技术选择最佳的属性预测值，然后将所选的最佳的属性预测值作为目标点的属性预测值进行后续编码。
值得注意的是，解码器预测目标点的实现方式可参考上文涉及的相关内容，为避免重复，此处不再赘述。
S322，该解码器基于该目标点的属性信息的残差值和该目标点的属性信息的预测值，确定该目标点的属性信息的初始重建值。
示例性地，例如，该解码器可通过以下公式基于该目标点的属性信息的残差值和该目标点的属性信息的预测值，确定该目标点的属性信息的初始重建值：Recon＝attrResidualQuant×Qstep+attrPred；其中，Recon表示基于该目标点的属性信息的残差值确定的该目标点的属性信息的初始重建值，attrResidualQuant表示该目标点的属性信息的残差值，Qstep表示量化步长，attrPred表示该目标点的属性信息的预测值。其中，Qstep由量化参数(Quantization Parameter，Qp)计算得到。
S323，该目标点的索引指示该目标点为R 7中的点时，该解码器解码该码流，确定R 7对应的滤波系数，并将该目标点的滤波系数，设置为等于R 7对应的滤波系数。
S324，该解码器基于该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，确定该目标点的属性信息的重建值，用于后续点的属性信息的预测。
示例性地，解码器解码出R 7对应的滤波系数，并利用R 7对应的滤波系数对R 7中点的属性信息的初始重建值进行滤波后，得到滤波后的重建值覆盖初始重建值得到更新后的重建值，这种情况下，用LOD m中点的重建值对R 7之后的细化层中点的属性信息进行预测并得到R 7之后的细化层中点的属性信息的预测值。
进一步的，解码器可重复上述过程，直到对倒数第二层细化层也完成滤波并用滤波后得到的值覆盖倒数第二层细化层中点的初始重建值，至此，对点云中的点完成滤波。
本申请实施例中，该解码器解码点云的码流，确定该点云中目标点的滤波系数；并基于该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，确定该目标点的属性信息的重建值。一方面，利用该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，可以提升该目标点的属性信息的重建值的准确度，进而提升了解码性能并实现了对重建点云的质量的增强。另一方面，利用该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，有利于采用当前点的重建值对点云中后续的点的属性信息进行预测，从而减少误差累积并实现了对重建点云的质量的增强。
此外，由于该前N个细化层中的点较少，本实施例对该前N个细化层进行合并滤波，并将滤波后的值覆盖初始重建值得到更新后的重建值，能够提高滤波准确度和解码效率。
图15是本申请实施例提供的点云解码方法330的示意性流程图。
如图15所示，该点云解码方法330可包括：
S331，解码器获取点云的码流。
S332，该解码器解码该码流得到R m中当前点的属性信息的残差值。
S333，该解码器解码该码流得到R m对应的滤波系数。
S334，该解码器对当前点的属性信息进行预测，并得到其预测值。
示例性地，解码器在对当前点的属性信息进行预测时，首先基于点云的原始顺序生成一个或多个细 化层，随后根据细化层的生成顺序从前期已编码的细化层中寻找当前点的最近邻居，并基于寻找到的最近邻居对当前点的属性信息进行预测，以得到当前点的属性预测值。例如，解码器在对当前点的属性信息进行预测时，可先基于当前点的邻居点的搜索结果，创建多个候选预测值，然后利用率失真优化(Rate distortion optimization，RDO)技术从多个候选预测值中选择针对当前点预测性能最佳的属性预测值，然后将最佳的属性预测值作为当前点的属性预测值，并与属性原始值进行做差的方式来编码属性信息以得到点云的属性码流。
示例性地，解码器可以先利用多个预测模式(predMode)，基于当前点的邻居点的搜索结果创建多个候选预测值。
其中，该多个预测模式(predMode)的索引的取值可以为0～3，解码器对当前点的属性信息进行编码时，解码器先基于当前点所在的LOD上的邻居点搜索结果找到位于当前点之前的3个邻居点，然后基于索引取值为0～3的预测模式获取多个候选预测值。其中，索引为0的预测模式指基于3个邻居点与当前点之间的距离将3个邻居点的重建属性值的加权平均值确定为当前点的候选预测值；索引为1的预测模式指：将3个邻居点中最近邻居点的属性重建值，作为当前点的候选预测值；索引为2的预测模式指：将次近邻居点的属性重建值，作为当前点的候选预测值；索引为3的预测模式指：将3个邻居点中除了最近邻居点和次近邻居点之外的邻居点的属性重建值，作为当前点的候选预测值；在基于上述各种预测模式得到当前点的多个候选预测值后，可以利用率失真优化(Rate distortion optimization，RDO)技术选择最佳的属性预测值，然后将所选的最佳的属性预测值作为当前点的属性预测值进行后续编码。
值得注意的是，解码器预测当前点的实现方式可参考上文涉及的相关内容，为避免重复，此处不再赘述。
S335，该解码器确定该当前点的属性信息的初始重建值。
示例性地，例如，该解码器可通过以下公式基于该当前点的属性信息的残差值和该当前点的属性信息的预测值，确定该当前点的属性信息的初始重建值：Recon＝attrResidualQuant×Qstep+attrPred；其中，Recon表示基于该当前点的属性信息的残差值确定的该当前点的属性信息的初始重建值，attrResidualQuant表示该当前点的属性信息的残差值，Qstep表示量化步长，attrPred表示该当前点的属性信息的预测值。其中，Qstep由量化参数(Quantization Parameter，Qp)计算得到。
S336，该解码器确定当前点为该R m中的最后一个点。
S337，该解码器基于R m对应的滤波系数对该R m中点的属性信息的初始重建值进行滤波，该R m中当前点的属性信息的初始重建值。
本申请实施例中，该解码器解码点云的码流，确定该点云中目标点的滤波系数；并基于该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，确定该目标点的属性信息的重建值。一方面，利用该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，可以提升该目标点的属性信息的重建值的准确度，进而提升了解码性能并实现了对重建点云的质量的增强。另一方面，利用该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，有利于采用当前点的重建值对点云中后续的点的属性信息进行预测，从而减少误差累积并实现了对重建点云的质量的增强。此外，由于该前N个细化层中的点较少，本实施例对该前N个细化层进行合并滤波，并将滤波后的值覆盖初始重建值得到更新后的重建值，能够提高滤波准确度和解码效率。
在一些实施例中，该S313可包括：
该解码器先确定该目标点所在的目标细节层；该目标细节层包括一个或多个细化层；接着，该解码器确定该目标细节层对应的滤波系数；然后，该解码器基于该目标细节层对应的滤波系数，确定该目标细节层中的任意点的滤波系数。
示例性地，该码流的结构可如下所示：
Flagbits_LOD0，coefbits_LOD0[k]，...，Flagbits_LODm-1，coefbits_LODm-1[k]，bits_0，...，bits_n-2，bits_n-1。
其中，n表示点云中点的总个数，m表示点云中进行了维纳滤波的细节层的数量，k表示各个细节层对应的滤波系数的长度(也称为维纳滤波器的阶数，或维纳滤波器的长度)。k＝16。bits,flagbits LOD,coefbits LOD分别是点云中点的属性信息的残差值、各个细节层对应的滤波标识、各个细节层对应的滤波系数。
在一些实施例中，该解码器先解码该码流，确定该点云的几何信息的重建值；接着，该解码器基于该点云的几何信息的重建值，将该点云划分为L个细化层，该细化层包括一个或多个点，L为大于0的整数；接着，该解码器基于该L个细化层，确定该点云的L个细节层；其中，该L个细节层中序号为i的细节层包括：按照细化层的生成顺序排列的序号为0到i的一个或多个细化层，i为大于0的整数；然后，该解码器基于该目标点的索引，确定该目标细节层。
示例性地，解码器可以基于不同的欧式距离将点云划分为L个细化层，再基于细化层获取L个LOD层。
例如，假设(R l) l＝0...L-1表示细化层(Refinement levels),L是LOD的层数。构建LOD具体步骤如下：
(1)、用户自定义L个欧式距离(d l) l＝0...L-1，划分L个细化层(R l) l＝0...L-1；
(2)、将所有的点标记为未访问，并将已访问点的集合V设为空集；
(3)、细化层R l生成过程如下：
遍历所有点，如果已经访问当前点，则忽略；否则，计算当前点距离集合V的最小距离D；若D小于d l，则忽略当前点；否则，将当前点标记为已访问并加入到R l和V中；重复此过程，直到点云中的所有点被遍历到。
(4)、取细化层R 0,R 1,...,R l的并集作为细节层LOD l(即
Figure PCTCN2022125110-appb-000005
)；
(5)、重复此过程，直到点云中的所有点均已被访问。
应理解，LOD层划分的方式还可以采用其它方式，本申请对此不进行限制。需要说明的是，可以直接将点云划分为一个或多个LOD层，也可以先将点云划分为多个点云切块(slice)，再将每一个点云切块划分为一个或多个LOD层。例如，可将点云划分为多个点云切块，每个点云切块的点的个数可以在55万-110万之间。每个点云切块可看成单独的点云。每个点云切块又可以划分为多个LOD层，每个LOD层包括多个点。
示例性地，假设该点云中点的顺序为按照细化层的生成顺序，则该解码器可基于该目标点的索引和各个细化层中点的数量，确定该目标细节层。
在一些实施例中，该解码器先解码该码流，确定该目标细节层对应的滤波标识；该目标细节层对应的滤波标识指示该目标细节层对应的滤波系数在该码流中的位置；然后，该解码器基于该目标细节层对应的滤波标识解码点云的码流，确定该目标细节层对应的滤波系数。
示例性地，该目标细节层对应的滤波标识通过指示该目标细节层的标识，来指示该目标细节层对应的滤波系数在该码流中的位置。
例如，假设该码流的结构可如下所示：Flagbits_LOD0，coefbits_LOD0[k]，...，Flagbits_LODm-1，coefbits_LODm-1[k]，bits_0，...，bits_n-2，bits_n-1。其中，n表示点云中点的总个数，m表示点云中进行了维纳滤波的细节层的数量，k表示各个细节层对应的滤波系数的长度(也称为维纳滤波器的阶数，或维纳滤波器的长度)。k＝16。bits,flagbits LOD,coefbits LOD分别是点云中点的属性信息的残差值、各个细节层对应的滤波标识、各个细节层对应的滤波系数。这种情况下，该目标细节层对应的滤波标识指示该目标细节层的标识，相当于指示了该目标细节层对应的滤波系数为该码流中位于该目标细节层对应的滤波标识之后的滤波系数。例如，Flagbits_LOD 0指示该目标细节层的标识，Flagbits_LOD 0后面的coefbits_LOD 0[k]可以默认为Flagbits_LOD 0指示的该目标细节层对应的滤波系数。
当然，在其他可替代实施例中，该目标细节层对应的滤波标识指示是否携带有该目标细节层对应的滤波系数，例如，若该码流中携带有该目标细节层对应的滤波标识，则说明该码流携带有该目标细节层对应的滤波系数。若该码流中未携带该目标细节层对应的滤波标识，则说明该码流中未携带该目标细节层对应的滤波系数。甚至于，该目标细节层对应的滤波标识可以指示是否对该目标细节层是否进行滤波；例如，该解码器可以采用类似编码器确定该目标细节层对应的滤波系数的方式，确定该目标细节层对应的滤波系数时，该目标细节层对应的滤波标识可以指示是否对该目标细节层是否进行滤波。本申请对此不作具体限定。
在一些实施例中，该解码器将该目标细节层中的任意点的滤波系数，设置为等于该目标细节层对应的滤波系数。
换言之，该目标细节层中的所有点的滤波系数为相同的滤波系数。
当然，在其他可替代实施例中，也可以在该码流中携带该目标细节层中的各个点的滤波系数，本申请对此不作具体限定。
在一些实施例中，该S314可包括：
该解码器将该目标点的近邻点的属性信息的初始重建值或重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性信息的重建值。
示例性地，该解码器将该目标点的k个近邻点的属性信息的初始重建值或重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性信息的重建值。其中，k表示各个细节层对应的滤波系数的长度(也称为维纳滤波器的阶数，或维纳滤波器的长度)。k＝16。当然，k也可以为其他数值，本申请对此不作具体限定。
当然，在其他可替代实施例中，该解码器可以将该目标点的属性信息的初始重建值与该目标点的k-1个近邻点的属性信息的初始重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性 信息的重建值，或者，该解码器也可以将该目标点的属性信息的初始重建值与该目标点的k-1个近邻点的属性信息的重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性信息的重建值，本申请对此不作具体限定。
值得注意的是，本申请对解码器查找该目标点的近邻点的实现方式不作具体限定。例如，可以采用KNN算法或其他方式查找该目标点的近邻点。
在一些实施例中，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则该解码器在该前N个细化层中选择该目标点的近邻点，并将该目标点的近邻点的属性信息的初始重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性信息的重建值。
示例性地，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则该解码器在该前N个细化层中选择该目标点的k个近邻点，并将该目标点的k个近邻点的属性信息的初始重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性信息的重建值。
示例性地，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则该解码器在该前N个细化层中选择该目标点的k-1个近邻点，并将该目标点的属性信息的初始重建值与该目标点的k-1个近邻点的属性信息的初始重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性信息的重建值。
在一些实施例中，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则该解码器在已滤波的细化层中选择该目标点的k个近邻点，并将该目标点的k个近邻点的属性信息的重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性信息的重建值。
示例性地，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则该解码器在已滤波的细化层中选择该目标点的k个近邻点，并将该目标点的k个近邻点的属性信息的初始重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性信息的重建值。
示例性地，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则该解码器在已滤波的细化层中选择该目标点的k-1个近邻点，并将该目标点的属性信息的初始重建值与该目标点的属性信息的初始重建值与该目标点的k-1个近邻点的属性信息的初始重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性信息的重建值。
在一些实施例中，该方法310还可包括：
该解码器基于该目标点的属性信息的重建值，得到解码点云。
示例性地，该解码器基于该点云中所有点的属性信息的重建值，得到该解码点云。
上文中从解码器的角度详细描述了根据本申请实施例的解码方法，下面将结合图16至18，从编码器的角度描述根据本申请实施例的编码方法。
图16是本申请实施例提供的点云编码方法410的示意性流程图。应理解，该点云编码方法410可由编码器或编码框架执行。例如应用于图1所示的编码框架100。为便于描述，下面以编码器为例进行说明。
如图16所示，该点云编码方法410可包括：
S411，编码器基于点云中目标点的属性信息的真实值和该目标点的属性信息的预测值，确定该目标点的属性信息的残差值；
S412，该编码器基于该目标点的近邻点，确定该目标点的滤波系数；
S413，该编码器对该目标点的属性信息的残差值和该目标点的滤波系数进行编码，得到码流。
换言之，该编码器基于该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波得到滤波后的重建值，并利用滤波后的重建值覆盖或替换该目标点的属性信息的初始重建值。
此外，该编码器可采用更新后的重建值对后续点的属性信息进行预测。
本申请实施例中，该编码器对该目标点的属性信息的残差值和该目标点的滤波系数进行编码，有利于解码器解码点云的码流，确定该点云中目标点的滤波系数；进而有利于解码器基于该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，确定该目标点的属性信息的重建值。一方面，利用该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，可以提升该目标点的属性信息的重建值的准确度，进而提升了解码性能并实现了对重建点云的质量的增强。另一方面，利用该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，有利于采用当前点的重建值对点云中后续的点的属性信息进行预测，从而减少误差累积并实现了对重建点云的质量的增强。
示例性地，该目标点的滤波系数可以是维纳编码器输出的滤波系数。
其中，维纳滤波器(Wiener filter)是由数学家维纳(Norbert Wiener)提出的一种以最小平方作为最优准则的线性滤波器。在一定的约束条件下，其输出与一给定函数(通常称为期望输出)的差的平方达到最小，通过数学运算最终可变为一个托布利兹方程的求解问题。维纳滤波器又被称为最小二乘滤波 器或最小平方滤波器。
假设滤波器的阶数(也称为滤波器的长度)为k时，其输出为：
y(n)＝H(k)×X(n)。
其中，y(n)表示滤波后的输出信号，k表示滤波器的阶数，H(k)表示滤波器的系数，X(n)表示滤波器的输入信号，即混有噪声的输入信号。
基于此，可以计算输出信号与期望信号之间的误差：e(n)＝d(n)-y(n)＝d(n)-H(k)×X(n),m＝0,1,...M；其中，e(n)表示期望信号和输出信号之间的误差，d(n)表示期望信号，y(n)表示输出信号。由于维纳滤波器以最小均方误差为目标函数，故令目标函数为：Min E(e(n) 2)＝E[(d(n)-H(k)×X(n)) 2]；当滤波器系数为最优时，目标函数对系数的导数应该为0，即：d(E(e(n) 2))/dH＝0；即：2E[(d(n)-H(k)×X(n))]×X(n)＝0；即：E[d(n)X(n)]-H(k)E[X(n)X(n)]＝0；对E[d(n)X(n)]-H(k)E[X(n)X(n)]＝0进行简化可得到：R xd-H(k)×R xx＝0。其中，R xd表示输入信号与期望信号的互相关向量，R xx表示输入信号与输入信号的自相关矩阵；最终得到：H(k)＝R xx -1×R xd。结合维纳滤波的工作原理，本申请将待滤波的点的属性信息的初始重建值作为上文涉及的混有噪声的输入信号，将待滤波点的属性信息的真实值作为上文涉及的期望信号。
换言之，可以用每个点的k近邻点(包括该点)去计算最优滤波系数。假设滤波器的阶数(也称为滤波器的长度)为k，则对于点云中的n个点，则可以构建矩阵P(n,k)来表示n个点中各个点的k个近邻点的属性重建值(即输入信号)，并通过构建向量S(n)来表示n个点中各个点的属性信息的真实值(即期望信号)，由此可知，输入信号与期望信号的互相关向量B(k)为：B(k)＝P(n,k) T×S(n)；输入信号与输入信号的自相关矩阵A(k,k)为：A(k,k)＝P(n,k) T×P(n,k)；即k阶滤波器的最优滤波系数H(k)为：H(k)＝A(k,k) -1×B(k)。同理，将H(k)作用在n个点中各个点的k个近邻点的属性重建值(即矩阵P(n,k))上，则可以最大限度恢复n个点中各个点的属性信息，并得到对n个点中各个点的属性信息进行为纳滤波后的最终重建值R(n)，即R(n)＝P(n,k)×H(k)。
值得注意的是，本申请对k的具体取值不作限定，例如k＝16或其他数值。
在一些实施例中，该S412可包括：
该编码器先基于该目标点所在的目标细化层，确定至少一个细化层；该细化层包括一个或多个点；接着，该编码器确定第一矩阵和第一向量，该第一矩阵为该至少一个细化层中点的近邻点的属性信息的初始重建值或重建值形成的矩阵，该第一向量为该至少一个细化层中点的属性信息的真实值形成的向量；该编码器将该第一矩阵和该第一向量相乘，得到该至少一个细化层的互相关矩阵；将由该第一矩阵的转置矩阵和该第一矩阵相乘，得到该至少一个细化层的自相关矩阵；基于该至少一个细化层的互相关矩阵和该至少一个细化层的自相关矩阵，确定该至少一个细化层对应的滤波系数；然后，该编码器基于该至少一个细化层对应的滤波系数，确定该至少一个细化层中的任意点的滤波系数；其中，该S413可包括：该编码器对该至少一个细化层中的点的属性信息的残差值、该至少一个细化层对应的滤波标识以及该至少一个细化层对应的滤波系数进行编码，得到该码流；该至少一个细化层对应的滤波标识指示该至少一个细化层对应的滤波系数在该码流中的位置。
示例性地，该第一矩阵为该至少一个细化层中各个点的k个近邻点的属性信息的初始重建值或重建值形成的矩阵，该第一向量为该至少一个细化层中各个点的属性信息的真实值形成的向量。其中，k表示各个细节层对应的滤波系数的长度(也称为维纳滤波器的阶数，或维纳滤波器的长度)。k＝16。当然，k也可以为其他数值，本申请对此不作具体限定。
示例性地，该至少一个细化层对应的滤波标识通过指示该至少一个细化层的标识，来指示该至少一个细化层对应的滤波系数在该码流中的位置。
例如，假设该码流的结构可如下所示：Flagbits_0，coefbits_0[k]，...，Flagbits_m-1，coefbits_m-1[k]，bits_0，...，bits_n-2，bits_n-1。其中，n表示点云中点的总个数，m表示点云中进行了维纳滤波的细化层的数量，k表示各个细化层对应的滤波系数的长度(也称为维纳滤波器的阶数，或维纳滤波器的长度)。k＝16。bits,flagbits,coefbits分别是点云中点的属性信息的残差值、各个细化层对应的滤波标识、各个细化层对应的滤波系数；这种情况下，该至少一个细化层对应的滤波标识指示该至少一个细化层的标识，相当于指示了该至少一个细化层对应的滤波系数为该码流中位于该至少一个细化层对应的滤波标识之后的滤波系数。例如，Flagbits_0指示该至少一个细化层的标识，Flagbits_0后面的coefbits_0[k]可以默认为Flagbits_0指示的细化层对应的滤波系数。
当然，在其他可替代实施例中，该至少一个细化层对应的滤波标识指示是否携带有该至少一个细化层对应的滤波系数，例如，若该码流中携带有该至少一个细化层对应的滤波标识，则说明该码流携带有该至少一个细化层对应的滤波系数。若该码流中未携带该至少一个细化层对应的滤波标识，则说明该码流中未携带该至少一个细化层对应的滤波系数。甚至于，该至少一个细化层对应的滤波标识可以指示是 否对该至少一个细化层是否进行滤波；例如，该解码器可以采用类似编码器确定该至少一个细化层对应的滤波系数的方式，确定该至少一个细化层对应的滤波系数时，该至少一个细化层对应的滤波标识可以指示是否对该至少一个细化层是否进行滤波。本申请对此不作具体限定。
在一些实施例中，该编码器先确定该点云的几何信息的重建值；接着，该编码器基于该点云的几何信息的重建值，将该点云划分为L个细化层，该细化层包括一个或多个点，L为大于0的整数；然后，该编码器基于该目标点的索引，确定该目标细化层。
示例性地，编码器可以基于不同的欧式距离将点云划分为L个细化层，再基于细化层获取L个LOD层。
例如，假设(R l) l＝0...L-1表示细化层(Refinement levels),L是LOD的层数。构建LOD具体步骤如下：
(1)、用户自定义L个欧式距离(d l) l＝0...L-1，划分L个细化层(R l) l＝0...L-1；
(2)、将所有的点标记为未访问，并将已访问点的集合V设为空集；
(3)、细化层R l生成过程如下：
遍历所有点，如果已经访问当前点，则忽略；否则，计算当前点距离集合V的最小距离D；若D小于d l，则忽略当前点；否则，将当前点标记为已访问并加入到R l和V中；重复此过程，直到点云中的所有点被遍历到。
(4)、取细化层R 0,R 1,...,R l的并集作为细节层LOD l(即
Figure PCTCN2022125110-appb-000006
)；
(5)、重复此过程，直到点云中的所有点均已被访问。
应理解，LOD层划分的方式还可以采用其它方式，本申请对此不进行限制。需要说明的是，可以直接将点云划分为一个或多个LOD层，也可以先将点云划分为多个点云切块(slice)，再将每一个点云切块划分为一个或多个LOD层。例如，可将点云划分为多个点云切块，每个点云切块的点的个数可以在55万-110万之间。每个点云切块可看成单独的点云。每个点云切块又可以划分为多个LOD层，每个LOD层包括多个点。
示例性地，假设该点云中点的顺序为按照细化层的生成顺序，则该编码器可基于该目标点的索引和各个细化层中点的数量，基于该目标点所在的目标细化层，确定至少一个细化层。
在一些实施例中，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则该编码器在该前N个细化层中选择该至少一个细化层中点的近邻点，并将该至少一个细化层中点的近邻点的属性信息的初始重建值形成的矩阵，确定为该第一矩阵。
示例性地，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则该编码器在该前N个细化层中选择该目标点的k个近邻点，并将该目标点的k个近邻点的属性信息的初始重建值形成的矩阵，确定为该第一矩阵。
示例性地，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则该编码器在该前N个细化层中选择该目标点的k-1个近邻点，并将该目标点的属性信息的初始重建值与该目标点的k-1个近邻点的属性信息的初始重建值形成的矩阵，确定为该第一矩阵。
在一些实施例中，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则该编码器在已滤波的细化层中选择该至少一个细化层中点的近邻点，并将该至少一个细化层中点的近邻点的属性信息的重建值形成的矩阵，确定为该第一矩阵。
示例性地，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则该编码器在已滤波的细化层中选择该目标点的k个近邻点，并将该目标点的k个近邻点的属性信息的初始重建值形成的矩阵，确定为该第一矩阵。
示例性地，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则该编码器在已滤波的细化层中选择该目标点的k-1个近邻点，并将该目标点的属性信息的初始重建值与该目标点的属性信息的初始重建值与该目标点的k-1个近邻点的属性信息的初始重建值形成的矩阵，确定为该第一矩阵。
在一些实施例中，该编码器将该至少一个细化层中的任意点的滤波系数，设置为等于该至少一个细化层对应的滤波系数。
换言之，该至少一个细化层中的所有点的滤波系数为相同的滤波系数。
当然，在其他可替代实施例中，也可以在该码流中携带该至少一个细化层中的各个点的滤波系数，本申请对此不作具体限定。
在一些实施例中，该至少一个细化层包括按照细化层的生成顺序排列的前N个细化层，或该至少一个细化层包括按照细化层的生成顺序位于该前N个细化层之后的一个或多个细化层，N为大于0的整数。
以该至少一个细化层为该前N个细化层为例，首先用KNN算法找到该前N个细化层中所有点的k 个近邻点，其次将该前N个细化层中所有点的属性信息的真实值和找到的所有点的k个近邻点的属性初始重建值(或重建值)作为维纳滤波器的输入，推导出前N个细化层对应的滤波系数并对前N个细化层中所有点的初始重建值进行滤波，将滤波后的值覆盖初始重建值得到更新后的重建值，用更新后的重建值对后续细化层中的点进行预测，并将前N个细化层对应的滤波系数写入码流。
由于该前N个细化层中的点较少，本实施例对该前N个细化层进行合并滤波，并将滤波后的值覆盖初始重建值得到更新后的重建值，能够提高滤波准确度和解码效率。对编码器来说，降低了滤波系数的计算量，并提升了编码效率。
在一些实施例中，该至少一个细化层包括按照细化层的生成顺序位于最后一个细化层之前、且位于该前N个细化层之后的单个细化层。
由于该按照细化层的生成顺序位于最后一个细化层之前、且位于该前N个细化层之后的单个细化层中的点较多，本实施例将该单个细化层进行独立滤波，能够提高滤波准确度。
图17是本申请实施例提供的点云编码方法420的示意性流程图。
如图17所示，该点云编码方法420可包括：
S421，编码器基于点云中目标点的属性信息的真实值和该目标点的属性信息的预测值，确定该目标点的属性信息的残差值。
示例性地，编码器在对目标点的属性信息进行预测时，首先基于点云的原始顺序生成一个或多个细化层，随后根据细化层的生成顺序从前期已编码的细化层中寻找目标点的最近邻居，并基于寻找到的最近邻居对目标点的属性信息进行预测，以得到目标点的属性预测值。例如，编码器在对目标点的属性信息进行预测时，可先基于目标点的邻居点的搜索结果，创建多个候选预测值，然后利用率失真优化(Rate distortion optimization，RDO)技术从多个候选预测值中选择针对目标点预测性能最佳的属性预测值，然后将最佳的属性预测值作为目标点的属性预测值，并与属性原始值进行做差的方式来编码属性信息以得到点云的属性码流。
示例性地，编码器可以先利用多个预测模式(predMode)，基于目标点的邻居点的搜索结果创建多个候选预测值。
其中，该多个预测模式(predMode)的索引的取值可以为0～3，编码器对目标点的属性信息进行编码时，编码器先基于目标点所在的LOD上的邻居点搜索结果找到位于目标点之前的3个邻居点，然后基于索引取值为0～3的预测模式获取多个候选预测值。其中，索引为0的预测模式指基于3个邻居点与目标点之间的距离将3个邻居点的重建属性值的加权平均值确定为目标点的候选预测值；索引为1的预测模式指：将3个邻居点中最近邻居点的属性重建值，作为目标点的候选预测值；索引为2的预测模式指：将次近邻居点的属性重建值，作为目标点的候选预测值；索引为3的预测模式指：将3个邻居点中除了最近邻居点和次近邻居点之外的邻居点的属性重建值，作为目标点的候选预测值；在基于上述各种预测模式得到目标点的多个候选预测值后，可以利用率失真优化(Rate distortion optimization，RDO)技术选择最佳的属性预测值，然后将所选的最佳的属性预测值作为目标点的属性预测值进行后续编码。
值得注意的是，编码器预测目标点的实现方式可参考上文涉及的相关内容，为避免重复，此处不再赘述。
S422，该编码器将该目标点的属性信息的残差值量化反量化后与该目标点的属性信息的预测值相加，得到该目标点的属性信息的初始重建值。
示例性地，编码器可以将该目标点的属性信息的真实值attrValue与该目标点的属性信息的预测值attrPred相减并对其结果进行量化，得到该目标点的属性信息的残差值attrResidualQuant。例如，attrResidualQuant＝(attrValue-attrPred)/Qs；其中，attrResidualQuant表示该目标点的属性信息的残差值，attrValue表示目标点的属性信息的真实值，attrPred表示该目标点的属性信息的预测值，Qstep表示量化步长。其中，Qstep由量化参数(Quantization Parameter，Qp)计算得到。
S423，该目标点的索引指示该目标点为R7中的点时，该编码器基于R 7中的点的邻居点确定R 7对应的滤波系数，并将该目标点的滤波系数，设置为等于R 7对应的滤波系数。
S424，该编码器对R 7中的点的属性信息的残差值、R 7对应的滤波标识以及R 7对应的滤波系数进行编码，得到该点云的码流。
示例性地，编码器确定R 7对应的滤波系数，并利用R 7对应的滤波系数对R 7中点的属性信息的初始重建值进行滤波后，得到滤波后的重建值覆盖初始重建值得到更新后的重建值，这种情况下，用LOD m中点的重建值对R 7之后的细化层中点的属性信息进行预测并得到R 7之后的细化层中点的属性信息的预测值。
进一步的，编码可重复上述过程，直到对倒数第二层细化层也完成滤波并用滤波后得到的值覆盖倒数第二层细化层中点的初始重建值，至此，对点云中的点完成滤波。
本申请实施例中，该编码器对该目标点的属性信息的残差值和该目标点的滤波系数进行编码，有利于解码器解码点云的码流，确定该点云中目标点的滤波系数；进而有利于解码器基于该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，确定该目标点的属性信息的重建值。一方面，利用该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，可以提升该目标点的属性信息的重建值的准确度，进而提升了解码性能并实现了对重建点云的质量的增强。另一方面，利用该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，有利于采用当前点的重建值对点云中后续的点的属性信息进行预测，从而减少误差累积并实现了对重建点云的质量的增强。
此外，由于该前N个细化层中的点较少，本实施例对该前N个细化层进行合并滤波，并将滤波后的值覆盖初始重建值得到更新后的重建值，能够提高滤波准确度和解码效率。
图18是本申请实施例提供的点云编码方法430的示意性流程图。
如图18所示，该点云编码方法430可包括：
S431，编码器获取点云的码流。
S432，该编码器将点云划分为L个细化层，并对R m中当前点的属性信息进行预测，并得到其预测值。
示例性地，编码器在对当前点的属性信息进行预测时，首先基于点云的原始顺序生成一个或多个细化层，随后根据细化层的生成顺序从前期已编码的细化层中寻找当前点的最近邻居，并基于寻找到的最近邻居对当前点的属性信息进行预测，以得到当前点的属性预测值。例如，编码器在对当前点的属性信息进行预测时，可先基于当前点的邻居点的搜索结果，创建多个候选预测值，然后利用率失真优化(Rate distortion optimization，RDO)技术从多个候选预测值中选择针对当前点预测性能最佳的属性预测值，然后将最佳的属性预测值作为当前点的属性预测值，并与属性原始值进行做差的方式来编码属性信息以得到点云的属性码流。
示例性地，编码器可以先利用多个预测模式(predMode)，基于当前点的邻居点的搜索结果创建多个候选预测值。
其中，该多个预测模式(predMode)的索引的取值可以为0～3，编码器对当前点的属性信息进行编码时，编码器先基于当前点所在的LOD上的邻居点搜索结果找到位于当前点之前的3个邻居点，然后基于索引取值为0～3的预测模式获取多个候选预测值。其中，索引为0的预测模式指基于3个邻居点与当前点之间的距离将3个邻居点的重建属性值的加权平均值确定为当前点的候选预测值；索引为1的预测模式指：将3个邻居点中最近邻居点的属性重建值，作为当前点的候选预测值；索引为2的预测模式指：将次近邻居点的属性重建值，作为当前点的候选预测值；索引为3的预测模式指：将3个邻居点中除了最近邻居点和次近邻居点之外的邻居点的属性重建值，作为当前点的候选预测值；在基于上述各种预测模式得到当前点的多个候选预测值后，可以利用率失真优化(Rate distortion optimization，RDO)技术选择最佳的属性预测值，然后将所选的最佳的属性预测值作为当前点的属性预测值进行后续编码。
值得注意的是，编码器预测当前点的实现方式可参考上文涉及的相关内容，为避免重复，此处不再赘述。
S433，该编码器将点云划分为L个细化层，并对R m中当前点的属性信息进行处理并得到其真实值。
S434，该编码器确定当前点的属性信息的残差值。
S435，该编码器确定该当前点的属性信息的初始重建值。
示例性地，编码器可以将该当前点的属性信息的真实值attrValue与该当前点的属性信息的预测值attrPred相减并对其结果进行量化，得到该当前点的属性信息的残差值attrResidualQuant。例如，attrResidualQuant＝(attrValue-attrPred)/Qs；其中，attrResidualQuant表示该当前点的属性信息的残差值，attrValue表示当前点的属性信息的真实值，attrPred表示该当前点的属性信息的预测值，Qstep表示量化步长。其中，Qstep由量化参数(Quantization Parameter，Qp)计算得到。
S436，该编码器确定当前点为该R m中的最后一个点。
S437，该编码器确定R m对应的滤波系数，并基于R m对应的滤波系数对该R m中点的属性信息的初始重建值进行滤波，该R m中当前点的属性信息的初始重建值。
S438，该编码器确定点云的码流。
本申请实施例中，该编码器对该目标点的属性信息的残差值和该目标点的滤波系数进行编码，有利于解码器解码点云的码流，确定该点云中目标点的滤波系数；进而有利于解码器基于该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，确定该目标点的属性信息的重建值。一方面，利用该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，可以提升该目标点的属性信息的重建值的准确度，进而提升了解码性能并实现了对重建点云的质量的增强。另一方面，利用该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，有利于采用当前点的重建值对点云中后续的点的属 性信息进行预测，从而减少误差累积并实现了对重建点云的质量的增强。
此外，由于该前N个细化层中的点较少，本实施例对该前N个细化层进行合并滤波，并将滤波后的值覆盖初始重建值得到更新后的重建值，能够提高滤波准确度和解码效率。
在一些实施例中，该S412可包括：
该编码器先确定该目标点所在的目标细节层；该目标细节层包括一个或多个细化层；接着，该编码器获取第二矩阵和第二向量，该第二矩阵为该目标细节层中点的近邻点的属性信息的初始重建值或重建值形成的矩阵，该第二向量为该目标细节层中点的属性信息的真实值形成的向量；将该第二矩阵和该第二向量相乘，得到该目标细节层的互相关矩阵；将该第二矩阵的转置矩阵和该第二矩阵相乘，得到该目标细节层的自相关矩阵；基于该目标细节层的互相关矩阵和该目标细节层的自相关矩阵，确定该目标细节层对应的滤波系数；然后，该编码器基于该目标细节层对应的滤波系数，确定该目标细节层中任意点的滤波系数；其中，该S413可包括：该编码器对该目标细节层中点的属性信息的残差值、该目标细节层对应的滤波标识以及该目标细节层对应的滤波系数进行编码，得到该码流；该目标细节层对应的滤波标识指示该目标细节层对应的滤波系数在该码流中的位置。
示例性地，该第二矩阵为该目标细节层中各个点的k个近邻点的属性信息的初始重建值或重建值形成的矩阵，该第二向量为该目标细节层中各个点的属性信息的真实值形成的向量。其中，k表示各个细节层对应的滤波系数的长度(也称为维纳滤波器的阶数，或维纳滤波器的长度)。k＝16。当然，k也可以为其他数值，本申请对此不作具体限定。
示例性地，该目标细节层对应的滤波标识通过指示该目标细节层的标识，来指示该目标细节层对应的滤波系数在该码流中的位置。
例如，假设该码流的结构可如下所示：Flagbits_LOD0，coefbits_LOD0[k]，...，Flagbits_LODm-1，coefbits_LODm-1[k]，bits_0，...，bits_n-2，bits_n-1。其中，n表示点云中点的总个数，m表示点云中进行了维纳滤波的细节层的数量，k表示各个细节层对应的滤波系数的长度(也称为维纳滤波器的阶数，或维纳滤波器的长度)。k＝16。bits,flagbits LOD,coefbits LOD分别是点云中点的属性信息的残差值、各个细节层对应的滤波标识、各个细节层对应的滤波系数。这种情况下，该目标细节层对应的滤波标识指示该目标细节层的标识，相当于指示了该目标细节层对应的滤波系数为该码流中位于该目标细节层对应的滤波标识之后的滤波系数。例如，Flagbits_LOD 0指示该目标细节层的标识，Flagbits_LOD 0后面的coefbits_LOD 0[k]可以默认为Flagbits_LOD 0指示的该目标细节层对应的滤波系数。
当然，在其他可替代实施例中，该目标细节层对应的滤波标识指示是否携带有该目标细节层对应的滤波系数，例如，若该码流中携带有该目标细节层对应的滤波标识，则说明该码流携带有该目标细节层对应的滤波系数。若该码流中未携带该目标细节层对应的滤波标识，则说明该码流中未携带该目标细节层对应的滤波系数。甚至于，该目标细节层对应的滤波标识可以指示是否对该目标细节层是否进行滤波；例如，该解码器可以采用类似编码器确定该目标细节层对应的滤波系数的方式，确定该目标细节层对应的滤波系数时，该目标细节层对应的滤波标识可以指示是否对该目标细节层是否进行滤波。本申请对此不作具体限定。
在一些实施例中，该编码器先确定该点云的几何信息的重建值；接着，该编码器基于该点云的几何信息的重建值，将该点云划分为L个细化层，该细化层包括一个或多个点，L为大于0的整数；基于该L个细化层，得到该点云的L个细节层；其中，该L个细节层中序号为i的细节层包括：按照细化层的生成顺序排列的序号为0到i的一个或多个细化层，i为大于0的整数；然后，该编码器基于该目标点的索引，确定该目标细节层。
示例性地，编码器可以基于不同的欧式距离将点云划分为L个细化层，再基于细化层获取L个LOD层。
例如，假设(R l) l＝0...L-1表示细化层(Refinement levels),L是LOD的层数。构建LOD具体步骤如下：
(1)、用户自定义L个欧式距离(d l) l＝0...L-1，划分L个细化层(R l) l＝0...L-1；
(2)、将所有的点标记为未访问，并将已访问点的集合V设为空集；
(3)、细化层R l生成过程如下：
遍历所有点，如果已经访问当前点，则忽略；否则，计算当前点距离集合V的最小距离D；若D小于d l，则忽略当前点；否则，将当前点标记为已访问并加入到R l和V中；重复此过程，直到点云中的所有点被遍历到。
(4)、取细化层R 0,R 1,...,R l的并集作为细节层LOD l(即
Figure PCTCN2022125110-appb-000007
)；
(5)、重复此过程，直到点云中的所有点均已被访问。
应理解，LOD层划分的方式还可以采用其它方式，本申请对此不进行限制。需要说明的是，可以直接将点云划分为一个或多个LOD层，也可以先将点云划分为多个点云切块(slice)，再将每一个点 云切块划分为一个或多个LOD层。例如，可将点云划分为多个点云切块，每个点云切块的点的个数可以在55万-110万之间。每个点云切块可看成单独的点云。每个点云切块又可以划分为多个LOD层，每个LOD层包括多个点。
示例性地，假设该点云中点的顺序为按照细化层的生成顺序，则该编码器可基于该目标点的索引和各个细化层中点的数量，确定该目标细节层。
在一些实施例中，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则该编码器在该前N个细化层中选择该目标细节层中点的近邻点，并将该目标细节层中点的近邻点的属性信息的初始重建值形成的矩阵，确定为该第二矩阵。
示例性地，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则该编码器在该前N个细化层中选择该目标点的k个近邻点，并将该目标点的k个近邻点的属性信息的初始重建值形成的矩阵，确定为该第二矩阵。
示例性地，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则该编码器在该前N个细化层中选择该目标点的k-1个近邻点，并将该目标点的属性信息的初始重建值与该目标点的k-1个近邻点的属性信息的初始重建值形成的矩阵，确定为该第二矩阵。
在一些实施例中，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则该编码器在已滤波的细化层中选择该目标细节层中点的近邻点，并将该目标细节层中点的近邻点的属性信息的重建值形成的矩阵，确定为该第二矩阵。
示例性地，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则该编码器在已滤波的细化层中选择该目标点的k个近邻点，并将该目标点的k个近邻点的属性信息的初始重建值形成的矩阵，确定为该第二矩阵。
示例性地，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则该编码器在已滤波的细化层中选择该目标点的k-1个近邻点，并将该目标点的属性信息的初始重建值与该目标点的属性信息的初始重建值与该目标点的k-1个近邻点的属性信息的初始重建值形成的矩阵，确定为该第二矩阵。
在一些实施例中，该编码器将该目标细节层中的任意点的滤波系数，设置为等于该目标细节层对应的滤波系数。
换言之，该至少一个细化层中的所有点的滤波系数为相同的滤波系数。
当然，在其他可替代实施例中，也可以在该码流中携带该至少一个细化层中的各个点的滤波系数，本申请对此不作具体限定。
在一些实施例中，该编码器基于该目标点的属性信息的残差值和该目标点的属性信息的预测值，确定该目标点的属性信息的初始重建值；基于该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，确定该目标点的属性信息的重建值。
在一些实施例中，该编码器将该目标点的近邻点的属性信息的初始重建值或重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性信息的重建值。
在一些实施例中，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则该编码器在该前N个细化层中选择该目标点的近邻点，并将该目标点的近邻点的属性信息的初始重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性信息的重建值。
在一些实施例中，若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则该编码器在已滤波的细化层中选择该目标点的近邻点，并将该目标点的近邻点的属性信息的重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性信息的重建值。
值得注意的是，编码器对目标点的初始重建值进行滤波的方案可参考解码器对目标点的初始重建值滤波的方案，为避免重复，此处不作赘述。
以上结合附图详细描述了本申请的优选实施方式，但是，本申请并不限于上文涉及的实施方式中的具体细节，在本申请的技术构思范围内，可以对本申请的技术方案进行多种简单变型，这些简单变型均属于本申请的保护范围。例如，在上文涉及的具体实施方式中所描述的各个具体技术特征，在不矛盾的情况下，可以通过任何合适的方式进行组合，为了避免不必要的重复，本申请对各种可能的组合方式不再另行说明。又例如，本申请的各种不同的实施方式之间也可以进行任意组合，只要其不违背本申请的思想，其同样应当视为本申请所公开的内容。还应理解，在本申请的各种方法实施例中，上文涉及的各过程的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。
上文详细描述了本申请的方法实施例，下文结合图19至图21，详细描述本申请的装置实施例。
图19是本申请实施例的解码器500的示意性框图。
如图19所示，该解码器500可包括：
解码单元510，用于解码点云的码流，确定该点云中目标点的属性信息的残差值；
重建单元520，用于基于该目标点的属性信息的残差值和该目标点的属性信息的预测值，确定该目标点的属性信息的初始重建值；
确定单元530，用于确定该目标点的滤波系数；
滤波单元540，用于基于该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，确定该目标点的属性信息的重建值。
在一些实施例中，该确定单元530具体用于：
基于该目标点所在的目标细化层，确定至少一个细化层；该细化层包括一个或多个点；
确定该至少一个细化层对应的滤波系数；
基于该至少一个细化层对应的滤波系数，确定该至少一个细化层中的任意点的滤波系数。
在一些实施例中，该确定单元530具体用于：
解码该码流，确定该点云的几何信息的重建值；
基于该点云的几何信息的重建值，将该点云划分为L个细化层，该细化层包括一个或多个点，L为大于0的整数；
基于该目标点的索引，确定该目标细化层。
在一些实施例中，该确定单元530具体用于：
解码该码流，确定该至少一个细化层对应的滤波标识；该至少一个细化层对应的滤波标识指示该至少一个细化层对应的滤波系数在该码流中的位置；
基于该至少一个细化层对应的滤波标识解码该码流，确定该至少一个细化层对应的滤波系数。
在一些实施例中，该确定单元530具体用于：
将该至少一个细化层中的任意点的滤波系数，设置为等于该至少一个细化层对应的滤波系数。
在一些实施例中，该至少一个细化层包括按照细化层的生成顺序排列的前N个细化层，或该至少一个细化层包括按照细化层的生成顺序位于该前N个细化层之后的一个或多个细化层，N为大于0的整数。
在一些实施例中，该至少一个细化层包括按照细化层的生成顺序位于最后一个细化层之前、且位于该前N个细化层之后的单个细化层。
在一些实施例中，该确定单元530具体用于：
确定该目标点所在的目标细节层；该目标细节层包括一个或多个细化层；
确定该目标细节层对应的滤波系数；
基于该目标细节层对应的滤波系数，确定该目标细节层中的任意点的滤波系数。
在一些实施例中，该确定单元530具体用于：
解码该码流，确定该点云的几何信息的重建值；
基于该点云的几何信息的重建值，将该点云划分为L个细化层，该细化层包括一个或多个点，L为大于0的整数；
基于该L个细化层，确定该点云的L个细节层；
其中，该L个细节层中序号为i的细节层包括：按照细化层的生成顺序排列的序号为0到i的一个或多个细化层，i为大于0的整数；
基于该目标点的索引，确定该目标细节层。
在一些实施例中，该确定单元530具体用于：
解码该码流，确定该目标细节层对应的滤波标识；该目标细节层对应的滤波标识指示该目标细节层对应的滤波系数在该码流中的位置；
基于该目标细节层对应的滤波标识解码点云的码流，确定该目标细节层对应的滤波系数。
在一些实施例中，该确定单元530具体用于：
将该目标细节层中的任意点的滤波系数，设置为等于该目标细节层对应的滤波系数。
在一些实施例中，该滤波单元540具体用于：
将该目标点的近邻点的属性信息的初始重建值或重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性信息的重建值。
在一些实施例中，该滤波单元540具体用于：
若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则在该前N个细化层中选择该目标点的近邻点，并将该目标点的近邻点的属性信息的初始重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性信息的重建值。
在一些实施例中，该滤波单元540具体用于：
若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则在已滤波的细化层中选择该目标点的近邻点，并将该目标点的近邻点的属性信息的重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性信息的重建值。
在一些实施例中，该重建单元520还用于：
基于该目标点的属性信息的重建值，得到解码点云。
图20是本申请实施例的编码器600的示意性框图。
如图20所示，该编码器600可包括：
残差单元610，用于基于点云中目标点的属性信息的真实值和该目标点的属性信息的预测值，确定该目标点的属性信息的残差值；
确定单元620，用于基于该目标点的近邻点，确定该目标点的滤波系数；
编码单元630，用于对该目标点的属性信息的残差值和该目标点的滤波系数进行编码，得到码流。
在一些实施例中，该确定单元620具体用于：
基于该目标点所在的目标细化层，确定至少一个细化层；该细化层包括一个或多个点；
确定第一矩阵和第一向量，该第一矩阵为该至少一个细化层中点的近邻点的属性信息的初始重建值或重建值形成的矩阵，该第一向量为该至少一个细化层中点的属性信息的真实值形成的向量；
将该第一矩阵和该第一向量相乘，得到该至少一个细化层的互相关矩阵；
将由该第一矩阵的转置矩阵和该第一矩阵相乘，得到该至少一个细化层的自相关矩阵；
基于该至少一个细化层的互相关矩阵和该至少一个细化层的自相关矩阵，确定该至少一个细化层对应的滤波系数；
基于该至少一个细化层对应的滤波系数，确定该至少一个细化层中的任意点的滤波系数；
其中，该编码单元630具体用于：
对该至少一个细化层中的点的属性信息的残差值、该至少一个细化层对应的滤波标识以及该至少一个细化层对应的滤波系数进行编码，得到该码流；该至少一个细化层对应的滤波标识指示该至少一个细化层对应的滤波系数在该码流中的位置。
在一些实施例中，该确定单元620具体用于：
确定该点云的几何信息的重建值；
基于该点云的几何信息的重建值，将该点云划分为L个细化层，该细化层包括一个或多个点，L为大于0的整数；
基于该目标点的索引，确定该目标细化层。
在一些实施例中，该确定单元620具体用于：
若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则在该前N个细化层中选择该至少一个细化层中点的近邻点，并将该至少一个细化层中点的近邻点的属性信息的初始重建值形成的矩阵，确定为该第一矩阵。
在一些实施例中，该确定单元620具体用于：
若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则在已滤波的细化层中选择该至少一个细化层中点的近邻点，并将该至少一个细化层中点的近邻点的属性信息的重建值形成的矩阵，确定为该第一矩阵。
在一些实施例中，该确定单元620具体用于：
将该至少一个细化层中的任意点的滤波系数，设置为等于该至少一个细化层对应的滤波系数。
在一些实施例中，该至少一个细化层包括按照细化层的生成顺序排列的前N个细化层，或该至少一个细化层包括按照细化层的生成顺序位于该前N个细化层之后的一个或多个细化层，N为大于0的整数。
在一些实施例中，该至少一个细化层包括按照细化层的生成顺序位于最后一个细化层之前、且位于该前N个细化层之后的单个细化层。
在一些实施例中，该确定单元620具体用于：
确定该目标点所在的目标细节层；该目标细节层包括一个或多个细化层；
获取第二矩阵和第二向量，该第二矩阵为该目标细节层中点的近邻点的属性信息的初始重建值或重建值形成的矩阵，该第二向量为该目标细节层中点的属性信息的真实值形成的向量；
将该第二矩阵和该第二向量相乘，得到该目标细节层的互相关矩阵；
将该第二矩阵的转置矩阵和该第二矩阵相乘，得到该目标细节层的自相关矩阵；
基于该目标细节层的互相关矩阵和该目标细节层的自相关矩阵，确定该目标细节层对应的滤波系 数；
基于该目标细节层对应的滤波系数，确定该目标细节层中任意点的滤波系数；
其中，该编码单元630具体用于：
对该目标细节层中点的属性信息的残差值、该目标细节层对应的滤波标识以及该目标细节层对应的滤波系数进行编码，得到该码流；该目标细节层对应的滤波标识指示该目标细节层对应的滤波系数在该码流中的位置。
在一些实施例中，该确定单元620具体用于：
确定该点云的几何信息的重建值；
基于该点云的几何信息的重建值，将该点云划分为L个细化层，该细化层包括一个或多个点，L为大于0的整数；
基于该L个细化层，得到该点云的L个细节层；
其中，该L个细节层中序号为i的细节层包括：按照细化层的生成顺序排列的序号为0到i的一个或多个细化层，i为大于0的整数；
基于该目标点的索引，确定该目标细节层。
在一些实施例中，该确定单元620具体用于：
若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则在该前N个细化层中选择该目标细节层中点的近邻点，并将该目标细节层中点的近邻点的属性信息的初始重建值形成的矩阵，确定为该第二矩阵。
在一些实施例中，该确定单元620具体用于：
若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则在已滤波的细化层中选择该目标细节层中点的近邻点，并将该目标细节层中点的近邻点的属性信息的重建值形成的矩阵，确定为该第二矩阵。
在一些实施例中，该确定单元620具体用于：
将该目标细节层中的任意点的滤波系数，设置为等于该目标细节层对应的滤波系数。
在一些实施例中，该确定单元620还用于：
基于该目标点的属性信息的残差值和该目标点的属性信息的预测值，确定该目标点的属性信息的初始重建值；
基于该目标点的滤波系数对该目标点的属性信息的初始重建值进行滤波，确定该目标点的属性信息的重建值。
在一些实施例中，该确定单元620具体用于：
将该目标点的近邻点的属性信息的初始重建值或重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性信息的重建值。
在一些实施例中，该确定单元620具体用于：
若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则在该前N个细化层中选择该目标点的近邻点，并将该目标点的近邻点的属性信息的初始重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性信息的重建值。
在一些实施例中，该确定单元620具体用于：
若该目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则在已滤波的细化层中选择该目标点的近邻点，并将该目标点的近邻点的属性信息的重建值形成的向量，与该目标点的滤波系数相乘，得到该目标点的属性信息的重建值。
应理解，装置实施例与方法实施例可以相互对应，类似的描述可以参照方法实施例。为避免重复，此处不再赘述。具体地，图19所示的解码器500可以对应于执行本申请实施例的方法310中的相应主体，并且解码器500中的各个单元的前述和其它操作和/或功能分别为了实现方法310等各个方法中的相应流程。图20所示的编码器600可以对应于执行本申请实施例的方法410中的相应主体，即编码器600中的各个单元的前述和其它操作和/或功能分别为了实现方法410等各个方法中的相应流程。
还应当理解，本申请实施例涉及的解码器500或编码器600中的各个单元可以分别或全部合并为一个或若干个另外的单元来构成，或者其中的某个(些)单元还可以再拆分为功能上更小的多个单元来构成，这可以实现同样的操作，而不影响本申请的实施例的技术效果的实现。上文涉及的单元是基于逻辑功能划分的，在实际应用中，一个单元的功能也可以由多个单元来实现，或者多个单元的功能由一个单元实现。在本申请的其它实施例中，该解码器500或编码器600也可以包括其它单元，在实际应用中，这些功能也可以由其它单元协助实现，并且可以由多个单元协作实现。根据本申请的另一个实施例，可以通过在包括例如中央处理单元(CPU)、随机存取存储介质(RAM)、只读存储介质(ROM)等处 理元件和存储元件的通用计算机的通用计算设备上运行能够执行相应方法所涉及的各步骤的计算机程序(包括程序代码)，来构造本申请实施例涉及的解码器500或编码器600，以及来实现本申请实施例的编码方法或解码方法。计算机程序可以记载于例如计算机可读存储介质上，并通过计算机可读存储介质装载于电子设备中，并在其中运行，来实现本申请实施例的相应方法。
换言之，上文涉及的单元可以通过硬件形式实现，也可以通过软件形式的指令实现，还可以通过软硬件结合的形式实现。具体地，本申请实施例中的方法实施例的各步骤可以通过处理器中的硬件的集成逻辑电路和/或软件形式的指令完成，结合本申请实施例公开的方法的步骤可以直接体现为硬件译码处理器执行完成，或者用译码处理器中的硬件及软件组合执行完成。可选地，软件可以位于随机存储器，闪存、只读存储器、可编程只读存储器、电可擦写可编程存储器、寄存器等本领域的成熟的存储介质中。该存储介质位于存储器，处理器读取存储器中的信息，结合其硬件完成上文涉及的方法实施例中的步骤。
图21是本申请实施例提供的电子设备700的示意结构图。
如图21所示，该电子设备700至少包括处理器710以及计算机可读存储介质720。其中，处理器710以及计算机可读存储介质720可通过总线或者其它方式连接。计算机可读存储介质720用于存储计算机程序721，计算机程序721包括计算机指令，处理器710用于执行计算机可读存储介质720存储的计算机指令。处理器710是电子设备700的计算核心以及控制核心，其适于实现一条或多条计算机指令，具体适于加载并执行一条或多条计算机指令从而实现相应方法流程或相应功能。
示例性地，处理器710也可称为中央处理器(Central Processing Unit，CPU)。处理器710可以包括但不限于：通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现场可编程门阵列(Field Programmable Gate Array，FPGA)或者其他可编程逻辑器件、晶体管逻辑器件、分立硬件组件等等。
示例性地，计算机可读存储介质720可以是高速RAM存储器，也可以是非不稳定的存储器(Non-VolatileMemory)，例如至少一个磁盘存储器；可选的，还可以是至少一个位于远离前述处理器710的计算机可读存储介质。具体而言，计算机可读存储介质720包括但不限于：易失性存储器和/或非易失性存储器。其中，非易失性存储器可以是只读存储器(Read-Only Memory，ROM)、可编程只读存储器(Programmable ROM，PROM)、可擦除可编程只读存储器(Erasable PROM，EPROM)、电可擦除可编程只读存储器(Electrically EPROM，EEPROM)或闪存。易失性存储器可以是随机存取存储器(Random Access Memory，RAM)，其用作外部高速缓存。通过示例性但不是限制性说明，许多形式的RAM可用，例如静态随机存取存储器(Static RAM，SRAM)、动态随机存取存储器(Dynamic RAM，DRAM)、同步动态随机存取存储器(Synchronous DRAM，SDRAM)、双倍数据速率同步动态随机存取存储器(Double Data Rate SDRAM，DDR SDRAM)、增强型同步动态随机存取存储器(Enhanced SDRAM，ESDRAM)、同步连接动态随机存取存储器(synch link DRAM，SLDRAM)和直接内存总线随机存取存储器(Direct Rambus RAM，DR RAM)。
示例性地，该电子设备700可以是本申请实施例涉及的编码器或编码框架；该计算机可读存储介质720中存储有第一计算机指令；由处理器710加载并执行计算机可读存储介质720中存放的第一计算机指令，以实现本申请实施例提供的编码方法中的相应步骤；换言之，计算机可读存储介质720中的第一计算机指令由处理器710加载并执行相应步骤，为避免重复，此处不再赘述。
示例性地，该电子设备700可以是本申请实施例涉及的解码器或解码框架；该计算机可读存储介质720中存储有第二计算机指令；由处理器710加载并执行计算机可读存储介质720中存放的第二计算机指令，以实现本申请实施例提供的解码方法中的相应步骤；换言之，计算机可读存储介质720中的第二计算机指令由处理器710加载并执行相应步骤，为避免重复，此处不再赘述。
根据本申请的另一方面，本申请还提供了一种编解码系统，包括上文涉及的编码器和解码器。
根据本申请的另一方面，本申请还提供了一种计算机可读存储介质(Memory)，计算机可读存储介质是电子设备700中的记忆设备，用于存放程序和数据。例如，计算机可读存储介质720。可以理解的是，此处的计算机可读存储介质720既可以包括电子设备700中的内置存储介质，当然也可以包括电子设备700所支持的扩展存储介质。计算机可读存储介质提供存储空间，该存储空间存储了电子设备700的操作系统。并且，在该存储空间中还存放了适于被处理器710加载并执行的一条或多条的计算机指令，这些计算机指令可以是一个或多个的计算机程序721(包括程序代码)。
根据本申请的另一方面，本申请还提供了一种计算机程序产品或计算机程序，该计算机程序产品或计算机程序包括计算机指令，该计算机指令存储在计算机可读存储介质中。例如，计算机程序721。此时，数据处理设备700可以是计算机，处理器710从计算机可读存储介质720读取该计算机指令，处理器710执行该计算机指令，使得该计算机执行上文涉及的各种可选方式中提供的编码方法或解码方法。
换言之，当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。该计算机程序产品 包括一个或多个计算机指令。在计算机上加载和执行该计算机程序指令时，全部或部分地运行本申请实施例的流程或实现本申请实施例的功能。该计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。该计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质进行传输，例如，该计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户线(digital subscriber line，DSL))或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元以及流程步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
最后需要说明的是，以上内容，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。

Claims (68)

  1. 一种点云解码方法，其特征在于，包括：
    解码点云的码流，确定所述点云中目标点的属性信息的残差值；
    基于所述目标点的属性信息的残差值和所述目标点的属性信息的预测值，确定所述目标点的属性信息的初始重建值；
    确定所述目标点的滤波系数；
    基于所述目标点的滤波系数对所述目标点的属性信息的初始重建值进行滤波，确定所述目标点的属性信息的重建值。
  2. 根据权利要求1所述的方法，其特征在于，所述确定所述目标点的滤波系数，包括：
    基于所述目标点所在的目标细化层，确定至少一个细化层；所述细化层包括一个或多个点；
    确定所述至少一个细化层对应的滤波系数；
    基于所述至少一个细化层对应的滤波系数，确定所述至少一个细化层中的任意点的滤波系数。
  3. 根据权利要求2所述的方法，其特征在于，所述基于所述目标点所在的目标细化层，确定至少一个细化层，包括：
    解码所述码流，确定所述点云的几何信息的重建值；
    基于所述点云的几何信息的重建值，将所述点云划分为L个细化层，所述细化层包括一个或多个点，L为大于0的整数；
    基于所述目标点的索引，确定所述目标细化层。
  4. 根据权利要求2所述的方法，其特征在于，所述确定所述至少一个细化层对应的滤波系数，包括：
    解码所述码流，确定所述至少一个细化层对应的滤波标识；所述至少一个细化层对应的滤波标识指示所述至少一个细化层对应的滤波系数在所述码流中的位置；
    基于所述至少一个细化层对应的滤波标识解码所述码流，确定所述至少一个细化层对应的滤波系数。
  5. 根据权利要求2所述的方法，其特征在于，所述基于所述至少一个细化层对应的滤波系数，确定所述至少一个细化层中的任意点的滤波系数，包括：
    将所述至少一个细化层中的任意点的滤波系数，设置为等于所述至少一个细化层对应的滤波系数。
  6. 根据权利要求2所述的方法，其特征在于，所述至少一个细化层包括按照细化层的生成顺序排列的前N个细化层，或所述至少一个细化层包括按照细化层的生成顺序位于所述前N个细化层之后的一个或多个细化层，N为大于0的整数。
  7. 根据权利要求6所述的方法，其特征在于，所述至少一个细化层包括按照细化层的生成顺序位于最后一个细化层之前、且位于所述前N个细化层之后的单个细化层。
  8. 根据权利要求1所述的方法，其特征在于，所述确定所述目标点的滤波系数，包括：
    确定所述目标点所在的目标细节层；所述目标细节层包括一个或多个细化层；
    确定所述目标细节层对应的滤波系数；
    基于所述目标细节层对应的滤波系数，确定所述目标细节层中的任意点的滤波系数。
  9. 根据权利要求8所述的方法，其特征在于，所述确定所述目标点所在的目标细节层，包括：
    解码所述码流，确定所述点云的几何信息的重建值；
    基于所述点云的几何信息的重建值，将所述点云划分为L个细化层，所述细化层包括一个或多个点，L为大于0的整数；
    基于所述L个细化层，确定所述点云的L个细节层；
    其中，所述L个细节层中序号为i的细节层包括：按照细化层的生成顺序排列的序号为0到i的一个或多个细化层，i为大于0的整数；
    基于所述目标点的索引，确定所述目标细节层。
  10. 根据权利要求8所述的方法，其特征在于，所述确定所述目标细节层对应的滤波系数，包括：
    解码所述码流，确定所述目标细节层对应的滤波标识；所述目标细节层对应的滤波标识指示所述目标细节层对应的滤波系数在所述码流中的位置；
    基于所述目标细节层对应的滤波标识解码点云的码流，确定所述目标细节层对应的滤波系数。
  11. 根据权利要求8所述的方法，其特征在于，所述基于所述目标细节层对应的滤波系数，确定所述目标细节层中的任意点的滤波系数，包括：
    将所述目标细节层中的任意点的滤波系数，设置为等于所述目标细节层对应的滤波系数。
  12. 根据权利要求1至11中任一项所述的方法，其特征在于，所述基于所述目标点的滤波系数对所述目标点的属性信息的初始重建值进行滤波，确定所述目标点的属性信息的重建值，包括：
    将所述目标点的近邻点的属性信息的初始重建值或重建值形成的向量，与所述目标点的滤波系数相乘，得到所述目标点的属性信息的重建值。
  13. 根据权利要求12所述的方法，其特征在于，所述将所述目标点的近邻点的属性信息的初始重建值或重建值形成的向量，与所述目标点的滤波系数相乘，得到所述目标点的属性信息的重建值，包括：
    若所述目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则在所述前N个细化层中选择所述目标点的近邻点，并将所述目标点的近邻点的属性信息的初始重建值形成的向量，与所述目标点的滤波系数相乘，得到所述目标点的属性信息的重建值。
  14. 根据权利要求12所述的方法，其特征在于，所述将所述目标点的近邻点的属性信息的初始重建值或重建值形成的向量，与所述目标点的滤波系数相乘，得到所述目标点的属性信息的重建值，包括：
    若所述目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则在已滤波的细化层中选择所述目标点的近邻点，并将所述目标点的近邻点的属性信息的重建值形成的向量，与所述目标点的滤波系数相乘，得到所述目标点的属性信息的重建值。
  15. 根据权利要求1至14中任一项所述的方法，其特征在于，所述方法还包括：
    基于所述目标点的属性信息的重建值，得到解码点云。
  16. 一种点云编码方法，其特征在于，包括：
    基于点云中目标点的属性信息的真实值和所述目标点的属性信息的预测值，确定所述目标点的属性信息的残差值；
    基于所述目标点的近邻点，确定所述目标点的滤波系数；
    对所述目标点的属性信息的残差值和所述目标点的滤波系数进行编码，得到码流。
  17. 根据权利要求16所述的方法，其特征在于，所述基于所述目标点的近邻点，确定所述目标点的滤波系数，包括：
    基于所述目标点所在的目标细化层，确定至少一个细化层；所述细化层包括一个或多个点；
    确定第一矩阵和第一向量，所述第一矩阵为所述至少一个细化层中点的近邻点的属性信息的初始重建值或重建值形成的矩阵，所述第一向量为所述至少一个细化层中点的属性信息的真实值形成的向量；
    将所述第一矩阵和所述第一向量相乘，得到所述至少一个细化层的互相关矩阵；
    将由所述第一矩阵的转置矩阵和所述第一矩阵相乘，得到所述至少一个细化层的自相关矩阵；
    基于所述至少一个细化层的互相关矩阵和所述至少一个细化层的自相关矩阵，确定所述至少一个细化层对应的滤波系数；
    基于所述至少一个细化层对应的滤波系数，确定所述至少一个细化层中的任意点的滤波系数；
    其中，所述对所述目标点的属性信息的残差值和所述目标点的滤波系数进行编码，得到码流，包括：
    对所述至少一个细化层中的点的属性信息的残差值、所述至少一个细化层对应的滤波标识以及所述至少一个细化层对应的滤波系数进行编码，得到所述码流；所述至少一个细化层对应的滤波标识指示所述至少一个细化层对应的滤波系数在所述码流中的位置。
  18. 根据权利要求17所述的方法，其特征在于，所述基于所述目标点所在的目标细化层，确定至少一个细化层，包括：
    确定所述点云的几何信息的重建值；
    基于所述点云的几何信息的重建值，将所述点云划分为L个细化层，所述细化层包括一个或多个点，L为大于0的整数；
    基于所述目标点的索引，确定所述目标细化层。
  19. 根据权利要求17所述的方法，其特征在于，所述确定第一矩阵和第一向量，包括：
    若所述目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则在所述前N个细化层中选择所述至少一个细化层中点的近邻点，并将所述至少一个细化层中点的近邻点的属性信息的初始重建值形成的矩阵，确定为所述第一矩阵。
  20. 根据权利要求17所述的方法，其特征在于，所述确定第一矩阵和第一向量，包括：
    若所述目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则在已滤波的细化层中选择所述至少一个细化层中点的近邻点，并将所述至少一个细化层中点的近邻点的属性信息的重建值形成的矩阵，确定为所述第一矩阵。
  21. 根据权利要求17所述的方法，其特征在于，所述基于所述至少一个细化层对应的滤波系数，确定所述至少一个细化层中的任意点的滤波系数，包括：
    将所述至少一个细化层中的任意点的滤波系数，设置为等于所述至少一个细化层对应的滤波系数。
  22. 根据权利要求17所述的方法，其特征在于，所述至少一个细化层包括按照细化层的生成顺序排列的前N个细化层，或所述至少一个细化层包括按照细化层的生成顺序位于所述前N个细化层之后的一个或多个细化层，N为大于0的整数。
  23. 根据权利要求22所述的方法，其特征在于，所述至少一个细化层包括按照细化层的生成顺序位于最后一个细化层之前、且位于所述前N个细化层之后的单个细化层。
  24. 根据权利要求16所述的方法，其特征在于，所述基于所述目标点的近邻点，确定所述目标点的滤波系数，包括：
    确定所述目标点所在的目标细节层；所述目标细节层包括一个或多个细化层；
    获取第二矩阵和第二向量，所述第二矩阵为所述目标细节层中点的近邻点的属性信息的初始重建值或重建值形成的矩阵，所述第二向量为所述目标细节层中点的属性信息的真实值形成的向量；
    将所述第二矩阵和所述第二向量相乘，得到所述目标细节层的互相关矩阵；
    将所述第二矩阵的转置矩阵和所述第二矩阵相乘，得到所述目标细节层的自相关矩阵；
    基于所述目标细节层的互相关矩阵和所述目标细节层的自相关矩阵，确定所述目标细节层对应的滤波系数；
    基于所述目标细节层对应的滤波系数，确定所述目标细节层中任意点的滤波系数；
    其中，所述对所述目标点的属性信息的残差值和所述目标点的滤波系数进行编码，得到码流，包括：
    对所述目标细节层中点的属性信息的残差值、所述目标细节层对应的滤波标识以及所述目标细节层对应的滤波系数进行编码，得到所述码流；所述目标细节层对应的滤波标识指示所述目标细节层对应的滤波系数在所述码流中的位置。
  25. 根据权利要求24所述的方法，其特征在于，所述确定所述目标点所在的目标细节层，包括：
    确定所述点云的几何信息的重建值；
    基于所述点云的几何信息的重建值，将所述点云划分为L个细化层，所述细化层包括一个或多个点，L为大于0的整数；
    基于所述L个细化层，得到所述点云的L个细节层；
    其中，所述L个细节层中序号为i的细节层包括：按照细化层的生成顺序排列的序号为0到i的一个或多个细化层，i为大于0的整数；
    基于所述目标点的索引，确定所述目标细节层。
  26. 根据权利要求24所述的方法，其特征在于，所述获取第二矩阵和第二向量，包括：
    若所述目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则在所述前N个细化层中选择所述目标细节层中点的近邻点，并将所述目标细节层中点的近邻点的属性信息的初始重建值形成的矩阵，确定为所述第二矩阵。
  27. 根据权利要求24所述的方法，其特征在于，所述获取第二矩阵和第二向量，包括：
    若所述目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则在已滤波的细化层中选择所述目标细节层中点的近邻点，并将所述目标细节层中点的近邻点的属性信息的重建值形成的矩阵，确定为所述第二矩阵。
  28. 根据权利要求24所述的方法，其特征在于，所述基于所述目标细节层对应的滤波系数，确定所述目标细节层中任意点的滤波系数，包括：
    将所述目标细节层中的任意点的滤波系数，设置为等于所述目标细节层对应的滤波系数。
  29. 根据权利要求16至28中任一项所述的方法，其特征在于，所述方法还包括：
    基于所述目标点的属性信息的残差值和所述目标点的属性信息的预测值，确定所述目标点的属性信息的初始重建值；
    基于所述目标点的滤波系数对所述目标点的属性信息的初始重建值进行滤波，确定所述目标点的属性信息的重建值。
  30. 根据权利要求29所述的方法，其特征在于，所述基于所述目标点的滤波系数对所述目标点的属性信息的初始重建值进行滤波，确定所述目标点的属性信息的重建值，包括：
    将所述目标点的近邻点的属性信息的初始重建值或重建值形成的向量，与所述目标点的滤波系数相乘，得到所述目标点的属性信息的重建值。
  31. 根据权利要求30所述的方法，其特征在于，所述将所述目标点的近邻点的属性信息的初始重建值或重建值形成的向量，与所述目标点的滤波系数相乘，得到所述目标点的属性信息的重建值，包括：
    若所述目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则在所述前N个细化层中选择所述目标点的近邻点，并将所述目标点的近邻点的属性信息的初始重建值形成的向量，与所 述目标点的滤波系数相乘，得到所述目标点的属性信息的重建值。
  32. 根据权利要求30所述的方法，其特征在于，所述将所述目标点的近邻点的属性信息的初始重建值或重建值形成的向量，与所述目标点的滤波系数相乘，得到所述目标点的属性信息的重建值，包括：
    若所述目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则在已滤波的细化层中选择所述目标点的近邻点，并将所述目标点的近邻点的属性信息的重建值形成的向量，与所述目标点的滤波系数相乘，得到所述目标点的属性信息的重建值。
  33. 一种解码器，其特征在于，包括：
    解码单元，用于解码点云的码流，确定所述点云中目标点的属性信息的残差值；
    重建单元，用于基于所述目标点的属性信息的残差值和所述目标点的属性信息的预测值，确定所述目标点的属性信息的初始重建值；
    确定单元，用于确定所述目标点的滤波系数；
    滤波单元，用于基于所述目标点的滤波系数对所述目标点的属性信息的初始重建值进行滤波，确定所述目标点的属性信息的重建值。
  34. 根据权利要求33所述的解码器，其特征在于，所述确定单元具体用于：
    基于所述目标点所在的目标细化层，确定至少一个细化层；所述细化层包括一个或多个点；
    确定所述至少一个细化层对应的滤波系数；
    基于所述至少一个细化层对应的滤波系数，确定所述至少一个细化层中的任意点的滤波系数。
  35. 根据权利要求34所述的解码器，其特征在于，所述确定单元具体用于：
    解码所述码流，确定所述点云的几何信息的重建值；
    基于所述点云的几何信息的重建值，将所述点云划分为L个细化层，所述细化层包括一个或多个点，L为大于0的整数；
    基于所述目标点的索引，确定所述目标细化层。
  36. 根据权利要求34所述的解码器，其特征在于，所述确定单元具体用于：
    解码所述码流，确定所述至少一个细化层对应的滤波标识；所述至少一个细化层对应的滤波标识指示所述至少一个细化层对应的滤波系数在所述码流中的位置；
    基于所述至少一个细化层对应的滤波标识解码所述码流，确定所述至少一个细化层对应的滤波系数。
  37. 根据权利要求34所述的解码器，其特征在于，所述确定单元具体用于：
    将所述至少一个细化层中的任意点的滤波系数，设置为等于所述至少一个细化层对应的滤波系数。
  38. 根据权利要求34所述的解码器，其特征在于，所述至少一个细化层包括按照细化层的生成顺序排列的前N个细化层，或所述至少一个细化层包括按照细化层的生成顺序位于所述前N个细化层之后的一个或多个细化层，N为大于0的整数。
  39. 根据权利要求38所述的解码器，其特征在于，所述至少一个细化层包括按照细化层的生成顺序位于最后一个细化层之前、且位于所述前N个细化层之后的单个细化层。
  40. 根据权利要求33所述的解码器，其特征在于，所述确定单元具体用于：
    确定所述目标点所在的目标细节层；所述目标细节层包括一个或多个细化层；
    确定所述目标细节层对应的滤波系数；
    基于所述目标细节层对应的滤波系数，确定所述目标细节层中的任意点的滤波系数。
  41. 根据权利要求40所述的解码器，其特征在于，所述确定单元具体用于：
    解码所述码流，确定所述点云的几何信息的重建值；
    基于所述点云的几何信息的重建值，将所述点云划分为L个细化层，所述细化层包括一个或多个点，L为大于0的整数；
    基于所述L个细化层，确定所述点云的L个细节层；
    其中，所述L个细节层中序号为i的细节层包括：按照细化层的生成顺序排列的序号为0到i的一个或多个细化层，i为大于0的整数；
    基于所述目标点的索引，确定所述目标细节层。
  42. 根据权利要求40所述的解码器，其特征在于，所述确定单元具体用于：
    解码所述码流，确定所述目标细节层对应的滤波标识；所述目标细节层对应的滤波标识指示所述目标细节层对应的滤波系数在所述码流中的位置；
    基于所述目标细节层对应的滤波标识解码点云的码流，确定所述目标细节层对应的滤波系数。
  43. 根据权利要求40所述的解码器，其特征在于，所述确定单元具体用于：
    将所述目标细节层中的任意点的滤波系数，设置为等于所述目标细节层对应的滤波系数。
  44. 根据权利要求33至43中任一项所述的解码器，其特征在于，所述滤波单元具体用于：
    将所述目标点的近邻点的属性信息的初始重建值或重建值形成的向量，与所述目标点的滤波系数相乘，得到所述目标点的属性信息的重建值。
  45. 根据权利要求44所述的解码器，其特征在于，所述滤波单元具体用于：
    若所述目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则在所述前N个细化层中选择所述目标点的近邻点，并将所述目标点的近邻点的属性信息的初始重建值形成的向量，与所述目标点的滤波系数相乘，得到所述目标点的属性信息的重建值。
  46. 根据权利要求44所述的解码器，其特征在于，所述滤波单元具体用于：
    若所述目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则在已滤波的细化层中选择所述目标点的近邻点，并将所述目标点的近邻点的属性信息的重建值形成的向量，与所述目标点的滤波系数相乘，得到所述目标点的属性信息的重建值。
  47. 根据权利要求33至46中任一项所述的解码器，其特征在于，所述解码器还包括：
    基于所述目标点的属性信息的重建值，得到解码点云。
  48. 一种编码器，其特征在于，包括：
    残差单元，用于基于点云中目标点的属性信息的真实值和所述目标点的属性信息的预测值，确定所述目标点的属性信息的残差值；
    确定单元，用于基于所述目标点的近邻点，确定所述目标点的滤波系数；
    编码单元，用于对所述目标点的属性信息的残差值和所述目标点的滤波系数进行编码，得到码流。
  49. 根据权利要求48所述的编码器，其特征在于，所述确定单元具体用于：
    基于所述目标点所在的目标细化层，确定至少一个细化层；所述细化层包括一个或多个点；
    确定第一矩阵和第一向量，所述第一矩阵为所述至少一个细化层中点的近邻点的属性信息的初始重建值或重建值形成的矩阵，所述第一向量为所述至少一个细化层中点的属性信息的真实值形成的向量；
    将所述第一矩阵和所述第一向量相乘，得到所述至少一个细化层的互相关矩阵；
    将由所述第一矩阵的转置矩阵和所述第一矩阵相乘，得到所述至少一个细化层的自相关矩阵；
    基于所述至少一个细化层的互相关矩阵和所述至少一个细化层的自相关矩阵，确定所述至少一个细化层对应的滤波系数；
    基于所述至少一个细化层对应的滤波系数，确定所述至少一个细化层中的任意点的滤波系数；
    其中，所述对所述目标点的属性信息的残差值和所述目标点的滤波系数进行编码，得到码流，包括：
    对所述至少一个细化层中的点的属性信息的残差值、所述至少一个细化层对应的滤波标识以及所述至少一个细化层对应的滤波系数进行编码，得到所述码流；所述至少一个细化层对应的滤波标识指示所述至少一个细化层对应的滤波系数在所述码流中的位置。
  50. 根据权利要求49所述的编码器，其特征在于，所述确定单元具体用于：
    确定所述点云的几何信息的重建值；
    基于所述点云的几何信息的重建值，将所述点云划分为L个细化层，所述细化层包括一个或多个点，L为大于0的整数；
    基于所述目标点的索引，确定所述目标细化层。
  51. 根据权利要求49所述的编码器，其特征在于，所述确定单元具体用于：
    若所述目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则在所述前N个细化层中选择所述至少一个细化层中点的近邻点，并将所述至少一个细化层中点的近邻点的属性信息的初始重建值形成的矩阵，确定为所述第一矩阵。
  52. 根据权利要求49所述的编码器，其特征在于，所述确定单元具体用于：
    若所述目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则在已滤波的细化层中选择所述至少一个细化层中点的近邻点，并将所述至少一个细化层中点的近邻点的属性信息的重建值形成的矩阵，确定为所述第一矩阵。
  53. 根据权利要求49所述的编码器，其特征在于，所述确定单元具体用于：
    将所述至少一个细化层中的任意点的滤波系数，设置为等于所述至少一个细化层对应的滤波系数。
  54. 根据权利要求49所述的编码器，其特征在于，所述至少一个细化层包括按照细化层的生成顺序排列的前N个细化层，或所述至少一个细化层包括按照细化层的生成顺序位于所述前N个细化层之后的一个或多个细化层，N为大于0的整数。
  55. 根据权利要求54所述的编码器，其特征在于，所述至少一个细化层包括按照细化层的生成顺序位于最后一个细化层之前、且位于所述前N个细化层之后的单个细化层。
  56. 根据权利要求48所述的编码器，其特征在于，所述确定单元具体用于：
    确定所述目标点所在的目标细节层；所述目标细节层包括一个或多个细化层；
    获取第二矩阵和第二向量，所述第二矩阵为所述目标细节层中点的近邻点的属性信息的初始重建值或重建值形成的矩阵，所述第二向量为所述目标细节层中点的属性信息的真实值形成的向量；
    将所述第二矩阵和所述第二向量相乘，得到所述目标细节层的互相关矩阵；
    将所述第二矩阵的转置矩阵和所述第二矩阵相乘，得到所述目标细节层的自相关矩阵；
    基于所述目标细节层的互相关矩阵和所述目标细节层的自相关矩阵，确定所述目标细节层对应的滤波系数；
    基于所述目标细节层对应的滤波系数，确定所述目标细节层中任意点的滤波系数；
    其中，所述对所述目标点的属性信息的残差值和所述目标点的滤波系数进行编码，得到码流，包括：
    对所述目标细节层中点的属性信息的残差值、所述目标细节层对应的滤波标识以及所述目标细节层对应的滤波系数进行编码，得到所述码流；所述目标细节层对应的滤波标识指示所述目标细节层对应的滤波系数在所述码流中的位置。
  57. 根据权利要求56所述的编码器，其特征在于，所述确定单元具体用于：
    确定所述点云的几何信息的重建值；
    基于所述点云的几何信息的重建值，将所述点云划分为L个细化层，所述细化层包括一个或多个点，L为大于0的整数；
    基于所述L个细化层，得到所述点云的L个细节层；
    其中，所述L个细节层中序号为i的细节层包括：按照细化层的生成顺序排列的序号为0到i的一个或多个细化层，i为大于0的整数；
    基于所述目标点的索引，确定所述目标细节层。
  58. 根据权利要求56所述的编码器，其特征在于，所述获取第二矩阵和第二向量，包括：
    若所述目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则在所述前N个细化层中选择所述目标细节层中点的近邻点，并将所述目标细节层中点的近邻点的属性信息的初始重建值形成的矩阵，确定为所述第二矩阵。
  59. 根据权利要求56所述的编码器，其特征在于，所述获取第二矩阵和第二向量，包括：
    若所述目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则在已滤波的细化层中选择所述目标细节层中点的近邻点，并将所述目标细节层中点的近邻点的属性信息的重建值形成的矩阵，确定为所述第二矩阵。
  60. 根据权利要求56所述的编码器，其特征在于，所述基于所述目标细节层对应的滤波系数，确定所述目标细节层中任意点的滤波系数，包括：
    将所述目标细节层中的任意点的滤波系数，设置为等于所述目标细节层对应的滤波系数。
  61. 根据权利要求48至60中任一项所述的编码器，其特征在于，所述确定单元还用于：
    基于所述目标点的属性信息的残差值和所述目标点的属性信息的预测值，确定所述目标点的属性信息的初始重建值；
    基于所述目标点的滤波系数对所述目标点的属性信息的初始重建值进行滤波，确定所述目标点的属性信息的重建值。
  62. 根据权利要求61所述的编码器，其特征在于，所述确定单元具体用于：
    将所述目标点的近邻点的属性信息的初始重建值或重建值形成的向量，与所述目标点的滤波系数相乘，得到所述目标点的属性信息的重建值。
  63. 根据权利要求62所述的编码器，其特征在于，所述确定单元具体用于：
    若所述目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层，则在所述前N个细化层中选择所述目标点的近邻点，并将所述目标点的近邻点的属性信息的初始重建值形成的向量，与所述目标点的滤波系数相乘，得到所述目标点的属性信息的重建值。
  64. 根据权利要求62所述的编码器，其特征在于，所述确定单元具体用于：
    若所述目标点所在的细化层属于按照细化层的生成顺序排列的前N个细化层之后的细化层，则在已滤波的细化层中选择所述目标点的近邻点，并将所述目标点的近邻点的属性信息的重建值形成的向量，与所述目标点的滤波系数相乘，得到所述目标点的属性信息的重建值。
  65. 一种电子设备，其特征在于，包括：
    处理器，适于执行计算机程序；
    计算机可读存储介质，所述计算机可读存储介质中存储有计算机程序，所述计算机程序被所述处理器执行时，实现根据权利要求1至15中任一项所述的方法或根据权利要求16至32中任一项所述的方 法。
  66. 一种计算机可读存储介质，其特征在于，用于存储计算机程序，所述计算机程序使得计算机执行根据权利要求1至15中任一项所述的方法或根据权利要求16至32中任一项所述的方法。
  67. 一种计算机程序产品，包括计算机程序/指令，其特征在于，所述计算机程序/指令被处理器执行时实现根据权利要求1至15中任一项所述的方法或根据权利要求16至32中任一项所述的方法。
  68. 一种码流，其特征在于，所述码流为根据权利要求1至15中任一项所述的方法中的码流或为根据权利要求16至32中任一项所述的方法生成的码流。
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