WO2023245982A1 - Point cloud compression method and apparatus, electronic device, and storage medium - Google Patents
Point cloud compression method and apparatus, electronic device, and storage medium Download PDFInfo
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Definitions
- the present disclosure relates to the technical field of point cloud data processing, and specifically, to a point cloud compression method, device, electronic equipment and storage medium.
- Point cloud is an important manifestation of real-world digitization and has been widely used in fields such as autonomous driving, virtual reality and digital museums.
- point cloud is a set of geometric and attribute information (such as color and reflectivity). ) points, simulating the external surfaces of various scenes and objects.
- geometric and attribute information such as color and reflectivity
- Region-adaptive hierarchical transformation is a hierarchical sub-band transformation that uses the color of lower-level nodes to predict the color of the next-level node. Its transformation matrix is derived based on the number of points of each node; graph Fourier transform uses threshold-based distance It means setting a weight matrix and converting the geometric sparse representation into a regular optimization problem with L0 norm.
- the lifting transformation is a branch of the geometric point cloud encoder and is implemented on the basis of the multi-level detail (Level of Detail, LOD) method.
- LOD multi-level detail
- the update operator uses the prediction residual to update the attribute values of the lower level LoD.
- the transform coefficient of each point is quantized by multiplying it by the corresponding weight.
- the above method does not consider the correlation between point cloud geometry and color and texture information, and the compression performance is poor.
- Embodiments of the present disclosure at least provide a point cloud compression method, device, electronic device, and storage medium, which can fully consider the correlation between the geometric attributes, color attributes, and texture information of the point cloud, and have better compression performance.
- Embodiments of the present disclosure provide a point cloud compression method.
- the method may include:
- For each sub-point cloud block determine the correlation coefficient between the geometric attribute information and the color attribute information corresponding to the sub-point cloud block;
- an unweighted map corresponding to the sub-point cloud block is determined, and the sub-point cloud block is compressed based on the unweighted map.
- determining the correlation coefficient between the geometric attribute information and the color attribute information corresponding to the sub-point cloud block may specifically include:
- the Pearson correlation coefficient is determined as the correlation coefficient between the geometric attribute information and the color attribute information.
- the Pearson correlation coefficient corresponding to the sub-point cloud block can be determined based on the following formula:
- G is a parameter representing the geometric change information
- C is a parameter representing the color change information
- ⁇ G represents the mean value corresponding to the observed value of the geometric change information
- ⁇ G represents the observed value of the geometric change information.
- the corresponding standard deviation ⁇ C represents the mean value corresponding to the observed value of the color change information
- ⁇ C represents the standard deviation corresponding to the observed value of the color change information
- M represents the difference between the observed value of the geometric change information and the color
- G i is the i-th observation value of the geometric change information
- C i represents the i-th observation value of the color change information
- ⁇ represents the Pearson correlation coefficient.
- a distance weighted map corresponding to the sub-point cloud block is determined according to the geometric attribute information, And compressing the sub-point cloud blocks based on the distance weighted map, specifically may include:
- the sub-point cloud blocks are compressed according to the Laplacian matrix.
- a threshold-based Gaussian kernel function can be used to define the weight in the distance-weighted graph.
- the threshold-based Gaussian kernel function is as shown in the following formula:
- d i, j represents the Euclidean distance between vertex i and vertex j in the sub-point cloud block
- ⁇ represents the average absolute Euclidean distance between vertex i and vertex j in the sub-point cloud block. value deviation
- ⁇ represents the preset Euclidean distance threshold.
- calculating the texture complexity corresponding to the sub-point cloud block for which the correlation coefficient is less than the coefficient threshold may specifically include:
- the texture complexity of the sub-point cloud block can be evaluated using the quadratic statistical entropy value of the gray level co-occurrence matrix.
- the formula for calculating the quadratic statistical entropy value is as follows: Shown:
- Entropy represents the secondary statistical entropy corresponding to the gray level co-occurrence matrix
- L represents the preset gray level
- GLCM i, j represents the gray level co-occurrence matrix
- a similarity weighted map corresponding to the sub-point cloud block is determined, and based on the The similarity weighted graph is used to compress the sub-point cloud blocks, which may specifically include:
- the edge weights between the vertices in the sub-point cloud block are determined, and the similarity weighted graph is constructed based on the edge weights, where , the similarity weighted map is used to reflect the texture similarity between vertices in the sub-point cloud block by introducing color attribute information;
- the sub-point cloud blocks are compressed according to the Laplacian matrix.
- the edge weight in the similarity-weighted graph can be defined based on the following formula:
- d i, j represents the Euclidean distance between vertex i and vertex j in the sub-point cloud block; ⁇ represents the average absolute Euclidean distance between vertex i and vertex j in the sub-point cloud block. value deviation; ⁇ represents the preset Euclidean distance threshold; p i,j represents the difference between the color prediction values between vertex i and vertex j in the sub-point cloud block; ⁇ represents the sub-point In the cloud block, the average absolute value deviation between the difference in color prediction value between vertex i and vertex j; ⁇ represents the difference in color prediction value between vertex i and vertex j in the sub-point cloud block corresponding to Preset threshold.
- the color prediction value of the vertex in the sub-point cloud block can be obtained by inverse distance weighting of the color attribute values of adjacent vertices in the sub-point cloud block.
- the vertex in the sub-point cloud block The color prediction value of is obtained by the following formula:
- p i represents the color prediction value corresponding to vertex i
- r j represents the color reconstruction value corresponding to vertex j
- vertex j is the encoded node adjacent to vertex i
- d i,j represents the distance between vertex i and vertex j Euclidean distance.
- an unweighted map corresponding to the sub-point cloud block is determined, and based on the unweighted map Weight graph compression of the sub-point cloud blocks may specifically include:
- the sub-point cloud block For the sub-point cloud block whose texture complexity is less than the complexity threshold, wherein the sub-point cloud block includes a global smooth block or a plurality of local smooth blocks;
- Morton code is used to encode the global smooth block, and a linear graph corresponding to the sub-point cloud block is constructed to describe the connectivity of the global smooth block;
- For the local smooth block use a spectral clustering algorithm to perform cluster analysis based on the color differences between vertices in the local smooth block, and construct a cluster connection graph corresponding to the sub-point cloud block;
- the sub-point cloud blocks are compressed according to the Laplacian matrix.
- Embodiments of the present disclosure also provide a device for compressing point clouds.
- the device may include:
- a dividing module configured to obtain a point cloud to be compressed and divide the point cloud to be compressed into a plurality of sub-point cloud blocks
- a correlation determination module configured to determine, for each sub-point cloud block, a correlation coefficient between the geometric attribute information and the color attribute information corresponding to the sub-point cloud block;
- a first compression module configured to determine a distance weighted map corresponding to the sub-point cloud block based on the geometric attribute information for the sub-point cloud block whose correlation coefficient is greater than a preset coefficient threshold, and Compress the sub-point cloud blocks based on the distance-weighted map;
- a texture complexity determination module configured to calculate, for the sub-point cloud block whose correlation coefficient is less than the coefficient threshold, the texture complexity corresponding to the sub-point cloud block;
- the second compression module is configured to determine, for the sub-point cloud block whose texture complexity is greater than a preset complexity threshold, a similarity weighted map corresponding to the sub-point cloud block, and based on the The similarity weighted graph compresses the sub-point cloud blocks;
- a third compression module configured to determine, for the sub-point cloud block whose texture complexity is less than the complexity threshold, an unweighted map corresponding to the sub-point cloud block, and based on the unweighted Figure compresses the sub-point cloud blocks.
- the correlation determination module may be specifically configured to:
- the Pearson correlation coefficient is determined as the correlation coefficient between the geometric attribute information and the color attribute information.
- the correlation determination module is further configured to determine the Pearson correlation coefficient corresponding to the sub-point cloud block based on the following formula:
- G is a parameter representing the geometric change information
- C is a parameter representing the color change information
- ⁇ G represents the mean value corresponding to the observed value of the geometric change information
- ⁇ G represents the observed value of the geometric change information.
- the corresponding standard deviation ⁇ C represents the mean value corresponding to the observed value of the color change information
- ⁇ C represents the standard deviation corresponding to the observed value of the color change information
- M represents the difference between the observed value of the geometric change information and the color
- G i is the i-th observation value of the geometric change information
- C i represents the i-th observation value of the color change information
- ⁇ represents the Pearson correlation coefficient.
- the first compression module may be specifically configured to:
- the sub-point cloud blocks are compressed according to the Laplacian matrix.
- the texture complexity determination module may be specifically configured to:
- the second compression module may be specifically configured to:
- the edge weights between the vertices in the sub-point cloud block are determined, and the similarity weighted graph is constructed based on the edge weights, where , the similarity weighted map is used to reflect the texture similarity between vertices in the sub-point cloud block by introducing color attribute information;
- the sub-point cloud blocks are compressed according to the Laplacian matrix.
- the third compression module may be specifically configured to:
- the sub-point cloud block For the sub-point cloud block whose texture complexity is less than the complexity threshold, wherein the sub-point cloud block includes a global smooth block or a plurality of local smooth blocks;
- Morton code is used to encode the global smooth block, and a linear graph corresponding to the sub-point cloud block is constructed to describe the connectivity of the global smooth block;
- For the local smooth block use a spectral clustering algorithm to perform cluster analysis based on the color differences between vertices in the local smooth block, and construct a cluster connection graph corresponding to the sub-point cloud block;
- the sub-point cloud blocks are compressed according to the Laplacian matrix.
- Embodiments of the present disclosure also provide an electronic device.
- the electronic device may include: a processor, a memory, and a bus.
- the memory stores machine-readable instructions executable by the processor.
- the processing There is communication between the processor and the memory through a bus.
- the machine-readable instructions are executed by the processor, the above-mentioned point cloud compression method or the steps in any possible implementation of the above-mentioned point cloud compression method are performed. .
- Embodiments of the present disclosure also provide a computer-readable storage medium.
- a computer program can be stored on the computer-readable storage medium.
- the computer program When the computer program is run by a processor, it can execute the above-mentioned point cloud compression method, or the above-mentioned point cloud compression. steps in any possible implementation of the method.
- Embodiments of the present disclosure provide a point cloud compression method, device, electronic device, and storage medium.
- the point cloud to be compressed is divided into multiple sub-point cloud blocks; for each sub-point cloud block, determine The correlation coefficient between the geometric attribute information and the color attribute information corresponding to the sub-point cloud block; for the sub-point cloud block whose correlation coefficient is greater than the preset coefficient threshold, the distance weighted map corresponding to the sub-point cloud block is determined based on the geometric attribute information , and compress the sub-point cloud blocks based on the distance weighted map; for sub-point cloud blocks whose correlation coefficient is less than the coefficient threshold, calculate the texture complexity corresponding to the sub-point cloud block; for sub-points whose texture complexity is greater than the preset complexity threshold Cloud block, determine the similarity weighted map corresponding to the sub-point cloud block, and compress the sub-point cloud block based on the similarity weighted map; for sub-point cloud blocks whose texture complexity is less than the complexity threshold, determine the
- Figure 1 shows a flow chart of a point cloud compression method provided by an embodiment of the present disclosure
- Figure 2 shows a flow chart of a compression method for textured smooth sub-point cloud blocks provided by an embodiment of the present disclosure
- Figure 3 shows a schematic diagram of a point cloud compression device provided by an embodiment of the present disclosure
- FIG. 4 shows a schematic diagram of an electronic device provided by an embodiment of the present disclosure.
- 300-compression device 310-division module; 320-correlation determination module; 330-first compression module; 340-texture complexity determination module; 350-second compression module; 360-third compression module; 400 - Electronic equipment; 41-processor; 42-storage; 421-memory; 422-external memory; 43-bus.
- a and/or B can mean: A alone exists, A and B exist simultaneously, and B alone exists. situation.
- at least one herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, and C, which can mean including from A, Any one or more elements selected from the set composed of B and C.
- transformation technologies for point cloud attribute compression can be roughly divided into three categories: region-adaptive hierarchical transformation, graph Fourier transform, and lifting transform.
- region-adaptive hierarchical transformation graph Fourier transform
- lifting transform lifting transform
- the present disclosure provides a point cloud compression method, device, electronic device and storage medium.
- the point cloud to be compressed is divided into multiple sub-point cloud blocks; for each sub-point cloud block , determine the correlation coefficient between the geometric attribute information and color attribute information corresponding to the sub-point cloud block; for the sub-point cloud block whose correlation coefficient is greater than the preset coefficient threshold, determine the distance corresponding to the sub-point cloud block based on the geometric attribute information Weighted map, and compress the sub-point cloud blocks based on the distance-weighted map; for the sub-point cloud blocks whose correlation coefficient is less than the coefficient threshold, calculate the texture complexity corresponding to the sub-point cloud block; for the sub-point cloud blocks whose texture complexity is greater than the preset complexity threshold For sub-point cloud blocks, determine the similarity weighted map corresponding to the sub-point cloud block, and compress the sub-point cloud block based on the similarity weighted map; for sub-point cloud blocks whose texture complexity is less than the
- the execution subject of the point cloud compression method provided by the embodiment of the disclosure is generally a computer with certain computing capabilities.
- Equipment the computer equipment includes, for example: terminal equipment or servers or other processing equipment.
- the terminal equipment can be user equipment (User Equipment, UE), mobile equipment, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (Personal Digital Assistant) Assistant, PDA), handheld devices, computing devices, vehicle-mounted devices, wearable devices, etc.
- the point cloud compression method can be implemented by the processor calling computer-readable instructions stored in the memory.
- a flow chart of a point cloud compression method is provided according to an embodiment of the present disclosure.
- the method includes steps S101 to S106, wherein:
- the three-dimensional KD tree structure can be used to divide the point cloud to be compressed into blocks. For each non-leaf node in the point cloud to be compressed, it can be divided into two subspaces by a hyperplane, and each corresponding subspace It can be divided recursively in the same way. After the division is completed, each leaf node is a child point cloud block.
- the segmentation of the KD tree is performed along the coordinate axes, and all hyperplanes are perpendicular to the corresponding coordinate axes. For example, when dividing along the x-axis, you only need to give a certain x value to determine the position of the hyperplane.
- the hyperplane divides the original node space into two subspaces, and the x values of all points in one subspace are smaller than the other. x-values for all points in space.
- the geometric change information and color change information generated by the signal differences between vertices in the sub-point cloud block can be obtained; the geometric change information and the The color change information is used as an evaluation index for the correlation between the geometric attribute information and the color attribute information, and a plurality of observation values corresponding to the geometric change information and a plurality of observation values corresponding to the color change information are obtained; according to The observed values corresponding to the geometric change information and the observed values corresponding to the color change information are determined to determine the Pearson correlation coefficient corresponding to the sub-point cloud block; the Pearson correlation coefficient is determined as the geometric attribute information and the The correlation coefficient between the color attribute information.
- the Pearson correlation coefficient corresponding to the sub-point cloud block can be determined based on the following formula:
- G is a parameter representing the geometric change information
- C is a parameter representing the color change information
- ⁇ G represents the mean value corresponding to the observed value of the geometric change information
- ⁇ G represents the observed value of the geometric change information.
- the corresponding standard deviation ⁇ C represents the mean value corresponding to the observed value of the color change information
- ⁇ C represents the standard deviation corresponding to the observed value of the color change information
- M represents the difference between the observed value of the geometric change information and the color
- G i is the i-th observation value of the geometric change information
- C i represents the i-th observation value of the color change information
- ⁇ represents the Pearson correlation coefficient.
- the geometric change information and color change information have N ⁇ (N-1)/2 observation values, where N is the number of vertices in the sub-point cloud block.
- the absolute value of the correlation coefficient can be binarized with a preset threshold to reflect the sub-point cloud.
- the strength of the correlation between geometric attribute information and color attribute information in the block can be binarized with a preset threshold to reflect the sub-point cloud.
- the correlation coefficient is greater than the preset coefficient threshold, it means that there is a strong correlation between the geometric attribute information and the color attribute information of the sub-point cloud block.
- the correlation coefficient is greater than the preset coefficient threshold, it means that there is a strong correlation between the geometric attribute information and the color attribute information of the sub-point cloud block.
- only geometric information is used to construct the relationship between vertex signals in the sub-point cloud block and determine the distance weighting corresponding to the sub-point cloud block. graph, and compress the sub-point cloud blocks based on the distance-weighted graph.
- the distance weighted map corresponding to the sub-point cloud block can be constructed based on the following method: for the sub-point cloud block whose correlation coefficient is greater than the preset coefficient threshold, determine the number of vertices of every two vertices in the sub-point cloud block. the vertex distance between them; determine the edge weight between the vertices in the sub-point cloud block according to the vertex distance and the preset distance threshold, and construct the distance-weighted graph based on the edge weight.
- preset coefficient threshold can be selected according to actual needs, and there is no specific restriction here.
- a threshold-based Gaussian kernel function can be used to define the weights in the distance-weighted graph, as shown in the following formula:
- d i, j represents the Euclidean distance between vertex i and vertex j in the sub-point cloud block; ⁇ represents the average absolute value deviation of the Euclidean distance between vertex i and vertex j in the sub-point cloud block; ⁇ Represents the preset Euclidean distance threshold.
- the ⁇ value can be set according to actual needs, and there are no specific restrictions here.
- the weight matrix corresponding to the distance-weighted graph can be determined, and the Laplan corresponding to the sub-point cloud block is determined according to the weight matrix.
- Laplacian matrix compress the sub-point cloud block according to the Laplacian matrix.
- the corresponding degree matrix can be determined based on the weight matrix, where the degree matrix is a diagonal matrix, and the elements on the diagonal are the sum of the elements in each row of the weight matrix
- the Laplacian matrix corresponding to the sub-point cloud block can be determined based on the difference between the degree matrix and the weight matrix.
- the eigenvector corresponding to the Laplacian matrix can reflect the attribute information corresponding to the sub-point cloud block. After encoding and compressing the eigenvector corresponding to the Laplacian matrix using the preset compressor, the sub-point cloud block can be completed. compression.
- the correlation coefficient is less than the coefficient threshold, it means that there is a weak correlation between the geometric attribute information and the color attribute information of the sub-point cloud block.
- texture complexity analysis needs to be performed to determine the texture complexity corresponding to the sub-point cloud block.
- the following method can be used to calculate the texture complexity corresponding to the sub-point cloud block: for the sub-point cloud block whose correlation coefficient is less than the coefficient threshold, determine the corresponding texture complexity of the sub-point cloud block.
- the gray level co-occurrence matrix calculates the secondary statistical entropy value corresponding to the gray level co-occurrence matrix, and determine the secondary statistical entropy value as the texture complexity.
- the Gray-Level Co-occurrence Matrix can be used as a texture complexity measure to calculate the number of occurrences of gray-level pairs in sub-point cloud blocks, where the gray-level pairs are sub-point cloud blocks.
- the quadratic statistical entropy value for GLCM can be used to evaluate the texture complexity of the sub-point cloud block.
- the formula for calculating the quadratic statistical entropy value is as follows:
- Entropy represents the secondary statistical entropy corresponding to the gray-level co-occurrence matrix
- L represents the preset gray level, which can be set according to actual needs, and there are no specific restrictions here
- GLCM i, j represents the gray-level co-occurrence matrix.
- the texture complexity is greater than the preset complexity threshold, it means that the texture of the sub-point cloud block is relatively complex.
- a color space needs to be introduced to provide more information, so a similarity weighted map is used for compression.
- preset complexity threshold can be selected according to actual needs, and there is no specific restriction here.
- the following method can be used to determine a similarity weighted map that reflects the texture similarity between vertices in the sub-point cloud block: determine the color attribute value corresponding to each vertex in the sub-point cloud block, and for each a vertex, perform an inverse distance weighted calculation on the color attribute value corresponding to the vertex and the color attribute value corresponding to its adjacent vertex, and determine the color prediction value corresponding to the vertex; according to the vertex distance and each two The difference between the color prediction values between vertices is used to determine the edge weights between vertices in the sub-point cloud block, and the similarity weighted graph is constructed based on the edge weights, where the similarity weighted graph is used to The texture similarity between vertices in the sub-point cloud blocks is reflected by introducing color attribute information.
- edge weights in the similarity-weighted graph can be defined based on the following formula:
- d i, j represents the Euclidean distance between vertex i and vertex j in the sub-point cloud block; ⁇ represents the average absolute value deviation of the Euclidean distance between vertex i and vertex j in the sub-point cloud block; ⁇ Represents the preset Euclidean distance threshold.
- the ⁇ value can be set according to actual needs, and there are no specific restrictions here;
- p i, j represents the difference between the color prediction values between vertex i and vertex j in the sub-point cloud block.
- the difference of The preset threshold corresponding to the difference in values, and the ⁇ value can be set according to actual needs, and there are no specific restrictions here.
- the color prediction value of the vertex in the sub-point cloud block can be obtained by the inverse distance weighting of the color attribute values of adjacent vertices in the sub-point cloud block.
- the specific formula is as follows:
- p i represents the color prediction value corresponding to vertex i
- r j represents the color reconstruction value corresponding to vertex j
- vertex j is the encoded node adjacent to vertex i
- d i,j represents the distance between vertex i and vertex j Euclidean distance.
- the weight matrix corresponding to the similarity weighted graph can be determined, and the pull corresponding to the sub-point cloud block can be determined according to the weight matrix.
- Laplacian matrix compress the sub-point cloud blocks according to the Laplacian matrix.
- the weight matrix corresponding to the similarity weighted graph is a graph Fourier transform weight matrix that considers both the geometric information and attribute information of the sub-point cloud blocks.
- the corresponding degree matrix can be determined based on the weight matrix, where the degree matrix is a diagonal matrix, and the elements on the diagonal are the sum of the elements in each row of the weight matrix.
- the Laplacian matrix corresponding to the sub-point cloud block can be determined based on the difference between the degree matrix and the weight matrix.
- the feature vector corresponding to the Laplacian matrix can reflect the attribute information corresponding to the sub-point cloud block.
- the texture complexity is less than the preset complexity threshold, it means that the texture of the sub-point cloud block is relatively smooth.
- unweighted graphs are used for compression.
- Figure 2 is a flow chart of a compression method for sub-point cloud blocks with smooth texture provided by an embodiment of the present disclosure.
- the method includes steps S1061 to S1064, wherein the sub-point cloud blocks whose texture complexity is less than the complexity threshold include one similar data cluster, that is, a global smooth block or multiple similar data blocks, that is, a local smooth block.
- the unweighted graph used to compress the texture smooth sub-point cloud block includes a line graph based on Morton coding.
- a line graph based on Morton coding is used to describe the connectivity of the global smooth block in the texture smooth sub-point cloud block. sex.
- For the local smooth block use a spectral clustering algorithm to perform cluster analysis based on the color differences between vertices in the local smooth block, and construct a cluster connection graph corresponding to the sub-point cloud block.
- the unweighted graph used to compress the texture smooth sub-point cloud block also includes a connection graph based on cluster analysis.
- a connection graph based on cluster analysis is used to describe the local smooth block in the texture smooth sub-point cloud block. Connectivity.
- cluster analysis can be performed to cluster multiple local smooth areas in sub-point cloud blocks with smooth textures to better analyze the correlation between vertices in the local smooth areas.
- cluster analysis can adopt spectral clustering
- graph clustering weight used can adopt Gaussian kernel function, which is defined by the following formula:
- W i,j represents the weight of the edge connecting vertices i and j
- c(i,j) represents the color difference between vertices i and j
- ⁇ represents the average absolute deviation corresponding to c, which is used to control the similarity measure pair Differential sensitivity.
- the adjacency matrix can be used to describe the cluster connection graph.
- the cluster connection is determined.
- the corresponding degree matrix can be determined based on the adjacency matrix.
- the degree matrix is a diagonal matrix. The elements on the diagonal are the sum of the elements in each row of the adjacency matrix. According to the difference between the degree matrix and the adjacency matrix The Laplacian matrix corresponding to the sub-point cloud block can be determined.
- the feature vector corresponding to the Laplacian matrix can reflect the attribute information corresponding to the sub-point cloud block. After encoding and compressing the feature vector corresponding to the Laplacian matrix using a preset compressor, the comparison can be completed. Compression of this sub-point cloud block.
- a point cloud compression method by obtaining a point cloud to be compressed, dividing the point cloud to be compressed into a plurality of sub-point cloud blocks; for each sub-point cloud block, determining the geometry corresponding to the sub-point cloud block Correlation coefficient between attribute information and color attribute information; for sub-point cloud blocks whose correlation coefficient is greater than the preset coefficient threshold, determine the distance-weighted map corresponding to the sub-point cloud block based on the geometric attribute information, and compress the sub-point cloud block based on the distance-weighted map Point cloud blocks; for sub-point cloud blocks whose correlation coefficient is less than the coefficient threshold, calculate the texture complexity corresponding to the sub-point cloud block; for sub-point cloud blocks whose texture complexity is greater than the preset complexity threshold, determine the sub-point cloud block similarity weighted map corresponding to the block, and compress the sub-point cloud block based on the similarity weighted map; for sub-point cloud blocks whose texture complexity is less than the complexity threshold, determine the unweighted
- the writing order of each step does not mean a strict execution order and does not constitute any limitation on the implementation process.
- the specific execution order of each step should be based on its function and possible The internal logic is determined.
- the embodiments of the present disclosure also provide a point cloud compression device corresponding to the point cloud compression method. Since the problem-solving principle of the device in the embodiments of the present disclosure is consistent with the above-mentioned point cloud compression method in the embodiments of the present disclosure, are similar, so the implementation of the device can refer to the implementation of the method, and repeated details will not be repeated.
- FIG. 3 is a schematic diagram of a point cloud compression device provided by an embodiment of the present disclosure.
- a point cloud compression device 300 provided by an embodiment of the present disclosure includes:
- the dividing module 310 is used to obtain the point cloud to be compressed and divide the point cloud to be compressed into multiple sub-point cloud blocks;
- the correlation determination module 320 is configured to determine, for each sub-point cloud block, the correlation coefficient between the geometric attribute information and the color attribute information corresponding to the sub-point cloud block;
- the first compression module 330 is configured to determine the distance weighted map corresponding to the sub-point cloud block according to the geometric attribute information for the sub-point cloud block whose correlation coefficient is greater than the preset coefficient threshold, and based on the The distance-weighted graph compresses the sub-point cloud blocks;
- the texture complexity determination module 340 is configured to calculate the texture complexity corresponding to the sub-point cloud block for which the correlation coefficient is less than the coefficient threshold;
- the second compression module 350 is configured to determine, for the sub-point cloud block whose texture complexity is greater than a preset complexity threshold, a similarity weighted map corresponding to the sub-point cloud block, and based on the similarity Weighted graph compresses the sub-point cloud blocks;
- the third compression module 360 is configured to determine the unweighted map corresponding to the sub-point cloud block whose texture complexity is less than the complexity threshold, and compress the sub-point cloud block based on the unweighted map.
- the sub-point cloud block is configured to determine the unweighted map corresponding to the sub-point cloud block whose texture complexity is less than the complexity threshold, and compress the sub-point cloud block based on the unweighted map. The sub-point cloud block.
- the correlation determination module 320 is specifically used to:
- the Pearson correlation coefficient is determined as the correlation coefficient between the geometric attribute information and the color attribute information.
- the correlation determination module 320 is further configured to determine the Pearson correlation coefficient corresponding to the sub-point cloud block based on the following formula:
- G is a parameter representing the geometric change information
- C is a parameter representing the color change information
- ⁇ G represents the mean value corresponding to the observed value of the geometric change information
- ⁇ G represents the observed value of the geometric change information.
- the corresponding standard deviation ⁇ C represents the mean value corresponding to the observed value of the color change information
- ⁇ C represents the standard deviation corresponding to the observed value of the color change information
- M represents the difference between the observed value of the geometric change information and the color
- G i is the i-th observation value of the geometric change information
- C i represents the i-th observation value of the color change information
- ⁇ represents the Pearson correlation coefficient.
- the first compression module 330 is specifically used to:
- the sub-point cloud blocks are compressed according to the Laplacian matrix.
- the texture complexity determination module 340 is specifically used to:
- the second compression module 350 is specifically used to:
- the edge weights between the vertices in the sub-point cloud block are determined, and the similarity weighted graph is constructed based on the edge weights, where , the similarity weighted map is used to reflect the texture similarity between vertices in the sub-point cloud block by introducing color attribute information;
- the sub-point cloud blocks are compressed according to the Laplacian matrix.
- the third compression module 360 is specifically used to:
- the sub-point cloud block For the sub-point cloud block whose texture complexity is less than the complexity threshold, wherein the sub-point cloud block includes a global smooth block or a plurality of local smooth blocks;
- Morton code is used to encode the global smooth block, and a linear graph corresponding to the sub-point cloud block is constructed to describe the connectivity of the global smooth block;
- For the local smooth block use a spectral clustering algorithm to perform cluster analysis based on the color differences between vertices in the local smooth block, and construct a cluster connection graph corresponding to the sub-point cloud block;
- the sub-point cloud blocks are compressed according to the Laplacian matrix.
- a point cloud compression device divides the point cloud to be compressed into multiple sub-point cloud blocks by acquiring the point cloud to be compressed; for each sub-point cloud block, the geometry corresponding to the sub-point cloud block is determined. Correlation coefficient between attribute information and color attribute information; for sub-point cloud blocks whose correlation coefficient is greater than the preset coefficient threshold, determine the distance-weighted map corresponding to the sub-point cloud block based on the geometric attribute information, and compress the sub-point cloud block based on the distance-weighted map Point cloud blocks; for sub-point cloud blocks whose correlation coefficient is less than the coefficient threshold, calculate the texture complexity corresponding to the sub-point cloud block; for sub-point cloud blocks whose texture complexity is greater than the preset complexity threshold, determine the sub-point cloud block similarity weighted map corresponding to the block, and compress the sub-point cloud block based on the similarity weighted map; for sub-point cloud blocks whose texture complexity is less than the complexity threshold, determine the unweighted map corresponding to the sub
- an embodiment of the present disclosure also provides an electronic device 400.
- a schematic structural diagram of the electronic device 400 provided by an embodiment of the present disclosure includes:
- the processor 41 and the memory 42 communicate through the bus 43, so that The processor 41 executes the steps of the point cloud compression method in FIG. 1 .
- Embodiments of the present disclosure also provide a computer-readable storage medium.
- a computer program is stored on the computer-readable storage medium.
- the storage medium may be a volatile or non-volatile computer-readable storage medium.
- An embodiment of the present disclosure also provides a computer program product.
- the computer program product includes computer instructions.
- the steps of the point cloud compression method described in the above method embodiment can be performed. Specifically, Please refer to the above method embodiments, which will not be described again here.
- the above-mentioned computer program product can be specifically implemented by hardware, software or a combination thereof.
- the computer program product is embodied as a computer storage medium.
- the computer program product is embodied as a software product, such as a Software Development Kit (SDK), etc. wait.
- SDK Software Development Kit
- the disclosed devices and methods can be implemented in other ways.
- the device embodiments described above are only illustrative.
- the division of the units is only a logical function division.
- multiple units or components may be combined or can be integrated into another system, or some features can be ignored, or not implemented.
- the coupling or direct coupling or communication connection between each other shown or discussed may be through some communication interfaces, and the indirect coupling or communication connection of the devices or units may be in electrical, mechanical or other forms.
- the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
- each functional unit in various embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
- the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor.
- the technical solution of the present disclosure is essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product.
- the computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present disclosure.
- the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program code. .
- Embodiments of the present disclosure provide a point cloud compression method, device, electronic device, and storage medium.
- the point cloud to be compressed is divided into multiple sub-point cloud blocks; for each sub-point cloud block, determine The correlation coefficient between the geometric attribute information and the color attribute information corresponding to the sub-point cloud block; for the sub-point cloud block whose correlation coefficient is greater than the preset coefficient threshold, the distance weighted map corresponding to the sub-point cloud block is determined based on the geometric attribute information , and compress the sub-point cloud blocks based on the distance weighted map; for sub-point cloud blocks whose correlation coefficient is less than the coefficient threshold, calculate the texture complexity corresponding to the sub-point cloud block; for sub-points whose texture complexity is greater than the preset complexity threshold Cloud block, determine the similarity weighted map corresponding to the sub-point cloud block, and compress the sub-point cloud block based on the similarity weighted map; for sub-point cloud blocks whose texture complexity is less than the complexity threshold, determine the
- the point cloud compression method, device, electronic device and storage medium of the present application are reproducible and can be used in a variety of industrial applications.
- the point cloud compression method, device, electronic device and storage medium of this application can be used in the technical field of point cloud data processing.
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Abstract
The present disclosure relates to the technical field of point cloud data processing, and particularly provides a point cloud compression method and apparatus, an electronic device, and a storage medium. The method comprises: dividing a point cloud to be compressed into a plurality of sub-point cloud blocks; determining a correlation coefficient between the geometric attribute information and the color attribute information corresponding to each sub-point cloud block, and comparing the correlation coefficient with a preset coefficient threshold value; if the former is larger than the latter, according to the geometric attribute information, determining a distance weighted graph corresponding to the sub-point cloud block, and compressing the sub-point cloud block on the basis of the distance weighted graph; and if the former is smaller than the latter, calculating the texture complexity corresponding to the sub-point cloud block and comparing the texture complexity with a preset complexity threshold value; if the former is larger than the latter, determining a similarity weighted graph corresponding to the sub-point cloud block, and compressing the sub-point cloud block on the basis of the similarity weighted graph; and if the former is smaller than the latter, determining an unweighted graph corresponding to the sub-point cloud block, and compressing the sub-point cloud block on the basis of the unweighted graph. The present disclosure achieves better compression performance with full consideration of the correlation between the geometric attribute, the color attribute and the texture information of the point cloud.
Description
相关申请的交叉引用Cross-references to related applications
本申请要求于2022年06月21日提交中国国家知识产权局的申请号为202210700940.6、名称为“一种点云的压缩方法、装置、电子设备及存储介质”的中国专利申请的优先权以及于2022年06月20日提交中国国家知识产权局的申请号为202210694204.4、名称为“一种点云的压缩方法、装置、电子设备及存储介质”的中国专利申请的优先权,其全部内容通过引用结合在本申请中。This application requires the priority of the Chinese patent application with application number 202210700940.6 and titled "A point cloud compression method, device, electronic equipment and storage medium" submitted to the State Intellectual Property Office of China on June 21, 2022, as well as the The priority of the Chinese patent application with application number 202210694204.4 and titled "A point cloud compression method, device, electronic equipment and storage medium" submitted to the State Intellectual Property Office of China on June 20, 2022, the entire content of which is incorporated by reference incorporated in this application.
本公开涉及点云数据处理技术领域,具体而言,涉及一种点云的压缩方法、装置、电子设备及存储介质。The present disclosure relates to the technical field of point cloud data processing, and specifically, to a point cloud compression method, device, electronic equipment and storage medium.
三维点云是现实世界数字化的重要表现形式,目前已经被广泛应用于自动驾驶、虚拟现实和数字博物馆等领域,在这些领域中,点云是一组包含几何和属性信息(如颜色和反射率)的点,模拟了各种场景和物体的外部表面。随着点云采集设备的发展,点云的分辨率正在急剧提高,数据量的增加使得3D应用程序的部署难以实现,如何高效地压缩点云数据已经成为一个重要的问题。Three-dimensional point cloud is an important manifestation of real-world digitization and has been widely used in fields such as autonomous driving, virtual reality and digital museums. In these fields, point cloud is a set of geometric and attribute information (such as color and reflectivity). ) points, simulating the external surfaces of various scenes and objects. With the development of point cloud acquisition equipment, the resolution of point clouds is increasing dramatically. The increase in data volume makes the deployment of 3D applications difficult to achieve. How to efficiently compress point cloud data has become an important issue.
目前,各种对于点云属性信息的压缩方法已经被提出,变换技术是其中的一个重要分支。目前,点云属性压缩的变换技术可以大致分为三大类:区域自适应层次变换、图傅里叶变换和提升变换。区域自适应层次变换是一种层次子带变换,利用较低层次节点的颜色来预测下一层次节点的颜色,其变换矩阵根据每个节点的点数导出;图傅里叶变换通过基于阈值的距离表示设置权重矩阵,将其中几何上的稀疏表示转化为一个L0范数的正则优化问题。或者通过引入基于块的帧内预测算法,利用邻接点之间的空间相关性;提升变换是几何点云编码器的一个分支,是在多层次细节(Level of Detail,LOD)方法的基础上实现的,它还引入了更新算子和自适应量化策略。更新算子使用预测残差来更新更低层LoD的属性值。接着,每个点的变换系数通过乘上对应权重从而被量化。但是上述方法并未考虑点云几何与颜色、纹理信息之间的相关性,压缩性能较差。At present, various compression methods for point cloud attribute information have been proposed, and transformation technology is an important branch among them. At present, transformation technologies for point cloud attribute compression can be roughly divided into three categories: region-adaptive hierarchical transformation, graph Fourier transform, and lifting transform. Region-adaptive hierarchical transformation is a hierarchical sub-band transformation that uses the color of lower-level nodes to predict the color of the next-level node. Its transformation matrix is derived based on the number of points of each node; graph Fourier transform uses threshold-based distance It means setting a weight matrix and converting the geometric sparse representation into a regular optimization problem with L0 norm. Or by introducing a block-based intra prediction algorithm to utilize the spatial correlation between adjacent points; the lifting transformation is a branch of the geometric point cloud encoder and is implemented on the basis of the multi-level detail (Level of Detail, LOD) method. , it also introduces update operators and adaptive quantization strategies. The update operator uses the prediction residual to update the attribute values of the lower level LoD. Next, the transform coefficient of each point is quantized by multiplying it by the corresponding weight. However, the above method does not consider the correlation between point cloud geometry and color and texture information, and the compression performance is poor.
发明内容Contents of the invention
本公开实施例至少提供一种点云的压缩方法、装置、电子设备及存储介质,可以充分考虑点云的几何属性、颜色属性、纹理信息之间的相关性,并具有较优的压缩性能。Embodiments of the present disclosure at least provide a point cloud compression method, device, electronic device, and storage medium, which can fully consider the correlation between the geometric attributes, color attributes, and texture information of the point cloud, and have better compression performance.
本公开实施例提供了一种点云的压缩方法,所述方法可以包括:Embodiments of the present disclosure provide a point cloud compression method. The method may include:
获取待压缩点云,将所述待压缩点云划分为多个子点云块;Obtain the point cloud to be compressed and divide the point cloud to be compressed into multiple sub-point cloud blocks;
针对每个所述子点云块,确定该所述子点云块对应的几何属性信息与颜色属性信息之间的相关系数;For each sub-point cloud block, determine the correlation coefficient between the geometric attribute information and the color attribute information corresponding to the sub-point cloud block;
针对所述相关系数大于预设的系数阈值的所述子点云块,根据所述几何属性信息确定该所述子点云块对应的距离加权图,并基于所述距离加权图压缩所述子点云块;For the sub-point cloud block whose correlation coefficient is greater than a preset coefficient threshold, determine the distance weighted map corresponding to the sub-point cloud block based on the geometric attribute information, and compress the sub-point cloud block based on the distance weighted map. point cloud block;
针对所述相关系数小于所述系数阈值的所述子点云块,计算该所述子点云块对应的纹理复杂度;For the sub-point cloud block whose correlation coefficient is less than the coefficient threshold, calculate the texture complexity corresponding to the sub-point cloud block;
针对所述纹理复杂度大于预设的复杂度阈值的所述子点云块,确定该所述子点云块对应的相似度加权图,并基于所述相似度加权图压缩所述子点云块;For the sub-point cloud block whose texture complexity is greater than a preset complexity threshold, determine the similarity weighted map corresponding to the sub-point cloud block, and compress the sub-point cloud based on the similarity weighted map piece;
针对所述纹理复杂度小于所述复杂度阈值的所述子点云块,确定该所述子点云块对应的无权图,并基于所述无权图压缩所述子点云块。For the sub-point cloud block whose texture complexity is less than the complexity threshold, an unweighted map corresponding to the sub-point cloud block is determined, and the sub-point cloud block is compressed based on the unweighted map.
一种可选的实施方式中,所述针对每个所述子点云块,确定该所述子点云块对应的几何属性信息与颜色属性信息之间的相关系数,具体可以包括:In an optional implementation, for each sub-point cloud block, determining the correlation coefficient between the geometric attribute information and the color attribute information corresponding to the sub-point cloud block may specifically include:
针对每个所述子点云块,获取该所述子点云块内,各个顶点间的信号差值产生的几何变化信息与颜色变化信息;For each sub-point cloud block, obtain geometric change information and color change information generated by signal differences between vertices in the sub-point cloud block;
将所述几何变化信息与所述颜色变化信息作为所述几何属性信息与所述颜色属性信息之间相关性的评价指标,获取多个所述几何变化信息对应的观测值以及多个所述颜色变化信息对应的观测值;Using the geometric change information and the color change information as evaluation indicators for the correlation between the geometric attribute information and the color attribute information, obtain a plurality of observation values corresponding to the geometric change information and a plurality of the colors Observed values corresponding to change information;
根据所述几何变化信息对应的观测值以及所述颜色变化信息对应的观测值,确定所述子点云块对应的皮尔森相关系数;According to the observation value corresponding to the geometric change information and the observation value corresponding to the color change information, determine the Pearson correlation coefficient corresponding to the sub-point cloud block;
将所述皮尔森相关系数确定为所述几何属性信息与所述颜色属性信息之间的相关系数。The Pearson correlation coefficient is determined as the correlation coefficient between the geometric attribute information and the color attribute information.
一种可选的实施方式中,可以基于以下公式确定所述子点云块对应的皮尔森相关系数:In an optional implementation, the Pearson correlation coefficient corresponding to the sub-point cloud block can be determined based on the following formula:
其中,G为代表所述几何变化信息的参数、C为代表所述颜色变化信息的参数、μ
G代表所述几何变化信息的观测值对应的均值、σ
G代表所述几何变化信息的观测值对应的标准差、μ
C代表所述颜色变化信息的观测值对应的均值、σ
C代表所述颜色变化信息的观测值对应的标准差、M代表所述几何变化信息的观测值与所述颜色变化信息的观测值的数量、G
i所述几何变化信息的第i个观测值、C
i代表所述颜色变化信息的第i个观测值、ρ代表所述皮尔森相关系数。
Wherein, G is a parameter representing the geometric change information, C is a parameter representing the color change information, μ G represents the mean value corresponding to the observed value of the geometric change information, and σ G represents the observed value of the geometric change information. The corresponding standard deviation, μ C represents the mean value corresponding to the observed value of the color change information, σ C represents the standard deviation corresponding to the observed value of the color change information, M represents the difference between the observed value of the geometric change information and the color The number of observation values of the change information, G i is the i-th observation value of the geometric change information, C i represents the i-th observation value of the color change information, and ρ represents the Pearson correlation coefficient.
一种可选的实施方式中,所述针对所述相关系数大于预设的系数阈值的所述子点云块,根据所述几何属性信息确定该所述子点云块对应的距离加权图,并基于所述距离加权图压缩所述子点云块,具体可以包括:In an optional implementation, for the sub-point cloud block whose correlation coefficient is greater than a preset coefficient threshold, a distance weighted map corresponding to the sub-point cloud block is determined according to the geometric attribute information, And compressing the sub-point cloud blocks based on the distance weighted map, specifically may include:
针对所述相关系数大于预设的系数阈值的所述子点云块,确定该所述子点云块中,每两个顶点之间的顶点距离;For the sub-point cloud block whose correlation coefficient is greater than a preset coefficient threshold, determine the vertex distance between every two vertices in the sub-point cloud block;
根据所述顶点距离以及预设的距离阈值,确定所述子点云块中顶点间的边权重,并根据所述边权重构建所述距离加权图;Determine edge weights between vertices in the sub-point cloud block based on the vertex distance and a preset distance threshold, and construct the distance-weighted graph based on the edge weights;
确定所述距离加权图对应的权重矩阵,根据所述权重矩阵确定所述子点云块对应的拉普拉斯矩阵;Determine the weight matrix corresponding to the distance weighted map, and determine the Laplacian matrix corresponding to the sub-point cloud block according to the weight matrix;
根据所述拉普拉斯矩阵压缩所述子点云块。The sub-point cloud blocks are compressed according to the Laplacian matrix.
一种可选的实施方式中,可以使用基于阈值的高斯核函数来定义所述距离加权图中的权重,所述基于阈值的高斯核函数如下公式所示:In an optional implementation, a threshold-based Gaussian kernel function can be used to define the weight in the distance-weighted graph. The threshold-based Gaussian kernel function is as shown in the following formula:
其中,d
i,j代表所述子点云块中,顶点i与顶点j之间的欧氏距离;δ代表所述子点云块中,顶点i与顶点j之间欧氏距离的平均绝对值偏差;τ代表预设的欧氏距离的阈值。
Among them, d i, j represents the Euclidean distance between vertex i and vertex j in the sub-point cloud block; δ represents the average absolute Euclidean distance between vertex i and vertex j in the sub-point cloud block. value deviation; τ represents the preset Euclidean distance threshold.
一种可选的实施方式中,所述针对所述相关系数小于所述系数阈值的所述子点云块,计算该所述子点云块对应的纹理复杂度,具体可以包括:In an optional implementation, calculating the texture complexity corresponding to the sub-point cloud block for which the correlation coefficient is less than the coefficient threshold may specifically include:
针对所述相关系数小于所述系数阈值的所述子点云块,确定该所述子点云块对应的灰度共生矩阵;For the sub-point cloud block whose correlation coefficient is less than the coefficient threshold, determine the gray level co-occurrence matrix corresponding to the sub-point cloud block;
计算所述灰度共生矩阵对应的二次统计熵值,将所述二次统计熵值确定为所述纹理复杂度。Calculate the quadratic statistical entropy value corresponding to the gray level co-occurrence matrix, and determine the quadratic statistical entropy value as the texture complexity.
一种可选的实施方式中,可以使用对于所述灰度共生矩阵的二次统计熵值来评估所述子点云块的所述纹理复杂度,计算所述二次统计熵值的公式如下所示:In an optional implementation, the texture complexity of the sub-point cloud block can be evaluated using the quadratic statistical entropy value of the gray level co-occurrence matrix. The formula for calculating the quadratic statistical entropy value is as follows: Shown:
其中,Entropy代表所述灰度共生矩阵对应的二次统计熵;L代表预设的灰度级别;GLCM
i,j代表所述灰度共生矩阵。
Wherein, Entropy represents the secondary statistical entropy corresponding to the gray level co-occurrence matrix; L represents the preset gray level; GLCM i, j represents the gray level co-occurrence matrix.
一种可选的实施方式中,所述针对所述纹理复杂度大于预设的复杂度阈值的所述子点云块,确定该所述子点云块对应的相似度加权图,并基于所述相似度加权图压缩所述子点云块,具体可以包括:In an optional implementation, for the sub-point cloud block whose texture complexity is greater than a preset complexity threshold, a similarity weighted map corresponding to the sub-point cloud block is determined, and based on the The similarity weighted graph is used to compress the sub-point cloud blocks, which may specifically include:
针对所述纹理复杂度大于预设的复杂度阈值的所述子点云块,确定该所述子点云块中,每两个顶点之间的顶点距离;For the sub-point cloud block whose texture complexity is greater than a preset complexity threshold, determine the vertex distance between every two vertices in the sub-point cloud block;
确定所述子点云块中每个顶点对应的颜色属性值,针对每个顶点,将该顶点对应的所述颜色属性值以及与其相邻的顶点对应的所述颜色属性值进行反距离加权计算,确定该顶点对应的颜色预测值;Determine the color attribute value corresponding to each vertex in the sub-point cloud block, and for each vertex, perform an inverse distance weighted calculation on the color attribute value corresponding to the vertex and the color attribute value corresponding to its adjacent vertex , determine the color prediction value corresponding to the vertex;
根据所述顶点距离以及每两个顶点之间所述颜色预测值的差值,确定所述子点云块中顶点间的边权重,并根据所述边权重构建所述相似度加权图,其中,所述相似度加权图用以通过引入颜色属性信息来反映所述子点云块中顶点间的纹理相似性;According to the vertex distance and the difference of the color prediction values between each two vertices, the edge weights between the vertices in the sub-point cloud block are determined, and the similarity weighted graph is constructed based on the edge weights, where , the similarity weighted map is used to reflect the texture similarity between vertices in the sub-point cloud block by introducing color attribute information;
确定所述相似度加权图对应的权重矩阵,根据所述权重矩阵确定所述子点云块对应的拉普拉斯矩阵;Determine the weight matrix corresponding to the similarity weighted map, and determine the Laplacian matrix corresponding to the sub-point cloud block according to the weight matrix;
根据所述拉普拉斯矩阵压缩所述子点云块。The sub-point cloud blocks are compressed according to the Laplacian matrix.
一种可选的实施方式中,可以基于以下公式定义所述相似度加权图中的所述边权重:In an optional implementation, the edge weight in the similarity-weighted graph can be defined based on the following formula:
其中,d
i,j代表所述子点云块中,顶点i与顶点j之间的欧氏距离;δ代表所述子点云块中,顶点i与顶点j之间欧氏距离的平均绝对值偏差;τ代表预设的欧氏距离的阈值;p
i,j是代表所述子点云块中,顶点i与顶点j之间颜色预测值之间的差值;θ代表所述子点云块中,顶点i与顶点j之间颜色预测值的差值之间的平均绝对值偏差;ψ代表所述子点云块中,顶点i与顶点j之间颜色预测值的差值对应的预设阈值。
Among them, d i, j represents the Euclidean distance between vertex i and vertex j in the sub-point cloud block; δ represents the average absolute Euclidean distance between vertex i and vertex j in the sub-point cloud block. value deviation; τ represents the preset Euclidean distance threshold; p i,j represents the difference between the color prediction values between vertex i and vertex j in the sub-point cloud block; θ represents the sub-point In the cloud block, the average absolute value deviation between the difference in color prediction value between vertex i and vertex j; ψ represents the difference in color prediction value between vertex i and vertex j in the sub-point cloud block corresponding to Preset threshold.
一种可选的实施方式中,所述子点云块中顶点的颜色预测值可以通过所述子点云块中相邻顶点颜色属性值的反距离加权获得,所述子点云块中顶点的颜色预测值通过下述公式获得:In an optional implementation, the color prediction value of the vertex in the sub-point cloud block can be obtained by inverse distance weighting of the color attribute values of adjacent vertices in the sub-point cloud block. The vertex in the sub-point cloud block The color prediction value of is obtained by the following formula:
其中,p
i代表顶点i对应的颜色预测值;r
j代表顶点j对应的颜色重建值,顶点j为与顶点i相邻的已编码节点;d
i,j代表顶点i与顶点j之间的欧氏距离。
Among them, p i represents the color prediction value corresponding to vertex i; r j represents the color reconstruction value corresponding to vertex j, and vertex j is the encoded node adjacent to vertex i; d i,j represents the distance between vertex i and vertex j Euclidean distance.
一种可选的实施方式中,所述针对所述纹理复杂度小于所述复杂度阈值的所述子点云块,确定该所述子点云块对应的无权图,并基于所述无权图压缩所述子点云块,具体可以包括:In an optional implementation, for the sub-point cloud block whose texture complexity is less than the complexity threshold, an unweighted map corresponding to the sub-point cloud block is determined, and based on the unweighted map Weight graph compression of the sub-point cloud blocks may specifically include:
针对所述纹理复杂度小于所述复杂度阈值的所述子点云块,其中所述子点云块包括一个全局平滑块或多个局部平滑块;For the sub-point cloud block whose texture complexity is less than the complexity threshold, wherein the sub-point cloud block includes a global smooth block or a plurality of local smooth blocks;
针对所述全局平滑块,利用莫顿码编码所述全局平滑块,构造所述子点云块对应的线性图,用以描述所述全局平滑块的连通性;For the global smooth block, Morton code is used to encode the global smooth block, and a linear graph corresponding to the sub-point cloud block is constructed to describe the connectivity of the global smooth block;
针对所述局部平滑块,利用谱聚类算法,根据所述局部平滑块内顶点间的颜色差值进行聚类分析,构建所述子点云块对应的聚类连接图;For the local smooth block, use a spectral clustering algorithm to perform cluster analysis based on the color differences between vertices in the local smooth block, and construct a cluster connection graph corresponding to the sub-point cloud block;
确定所述聚类连接图对应的邻接矩阵,根据所述邻接矩阵确定所述子点云块对应的拉普拉斯矩阵;Determine the adjacency matrix corresponding to the cluster connection graph, and determine the Laplacian matrix corresponding to the sub-point cloud block according to the adjacency matrix;
根据所述拉普拉斯矩阵压缩所述子点云块。The sub-point cloud blocks are compressed according to the Laplacian matrix.
本公开实施例还提供一种点云的压缩装置,所述装置可以包括:Embodiments of the present disclosure also provide a device for compressing point clouds. The device may include:
划分模块,被配置成用于获取待压缩点云,将所述待压缩点云划分为多个子点云块;A dividing module configured to obtain a point cloud to be compressed and divide the point cloud to be compressed into a plurality of sub-point cloud blocks;
相关性确定模块,被配置成用于针对每个所述子点云块,确定该所述子点云块对应的几何属性信息与颜色属性信息之间的相关系数;A correlation determination module configured to determine, for each sub-point cloud block, a correlation coefficient between the geometric attribute information and the color attribute information corresponding to the sub-point cloud block;
第一压缩模块,被配置成用于针对所述相关系数大于预设的系数阈值的所述子点云块,根据所述几何属性信息确定该所述子点云块对应的距离加权图,并基于所述距离加权图压缩所述子点云块;A first compression module configured to determine a distance weighted map corresponding to the sub-point cloud block based on the geometric attribute information for the sub-point cloud block whose correlation coefficient is greater than a preset coefficient threshold, and Compress the sub-point cloud blocks based on the distance-weighted map;
纹理复杂度确定模块,被配置成用于针对所述相关系数小于所述系数阈值的所述子点云块,计算该所述子点云块对应的纹理复杂度;A texture complexity determination module configured to calculate, for the sub-point cloud block whose correlation coefficient is less than the coefficient threshold, the texture complexity corresponding to the sub-point cloud block;
第二压缩模块,被配置成用于针对所述纹理复杂度大于预设的复杂度阈值的所述子点云块,确定该所述子点云块对应的相似度加权图,并基于所述相似度加权图压缩所述子点云块;The second compression module is configured to determine, for the sub-point cloud block whose texture complexity is greater than a preset complexity threshold, a similarity weighted map corresponding to the sub-point cloud block, and based on the The similarity weighted graph compresses the sub-point cloud blocks;
第三压缩模块,被配置成用于针对所述纹理复杂度小于所述复杂度阈值的所述子点云块,确定该所述子点云块对应的无权图,并基于所述无权图压缩所述子点云块。A third compression module configured to determine, for the sub-point cloud block whose texture complexity is less than the complexity threshold, an unweighted map corresponding to the sub-point cloud block, and based on the unweighted Figure compresses the sub-point cloud blocks.
一种可选的实施方式中,所述相关性确定模块可以具体被配置成用于:In an optional implementation, the correlation determination module may be specifically configured to:
针对每个所述子点云块,获取该所述子点云块内,各个顶点间的信号差值产生的几何变化信息与颜色变化信息;For each sub-point cloud block, obtain geometric change information and color change information generated by signal differences between vertices in the sub-point cloud block;
将所述几何变化信息与所述颜色变化信息作为所述几何属性信息与所述颜色属性信息之间相关性的评价指标,获取多个所述几何变化信息对应的观测值以及多个所述颜色变化信息对应的观测值;Using the geometric change information and the color change information as evaluation indicators for the correlation between the geometric attribute information and the color attribute information, obtain a plurality of observation values corresponding to the geometric change information and a plurality of the colors Observed values corresponding to change information;
根据所述几何变化信息对应的观测值以及所述颜色变化信息对应的观测值,确定所述子点云块对应的皮尔森相关系数;According to the observation value corresponding to the geometric change information and the observation value corresponding to the color change information, determine the Pearson correlation coefficient corresponding to the sub-point cloud block;
将所述皮尔森相关系数确定为所述几何属性信息与所述颜色属性信息之间的相关系数。The Pearson correlation coefficient is determined as the correlation coefficient between the geometric attribute information and the color attribute information.
一种可选的实施方式中,所述相关性确定模块还被配置成用于:基于以下公式确定所述子点云块对应的皮尔森相关系数:In an optional implementation, the correlation determination module is further configured to determine the Pearson correlation coefficient corresponding to the sub-point cloud block based on the following formula:
其中,G为代表所述几何变化信息的参数、C为代表所述颜色变化信息的参数、μ
G代表所述几何变化信息的观测值对应的均值、σ
G代表所述几何变化信息的观测值对应的标准差、μ
C代表所述颜色变化信息的观测值对应的均值、σ
C代表所述颜色变化信息的观测值对应的标准差、M代表所述几何变化信息的观测值与所述颜色变化信息的观测值的数量、G
i所述几何变化信息的第i个观测值、C
i代表所述颜色变化信息的第i个观测值、ρ代表所述皮尔森相关系数。
Wherein, G is a parameter representing the geometric change information, C is a parameter representing the color change information, μ G represents the mean value corresponding to the observed value of the geometric change information, and σ G represents the observed value of the geometric change information. The corresponding standard deviation, μ C represents the mean value corresponding to the observed value of the color change information, σ C represents the standard deviation corresponding to the observed value of the color change information, M represents the difference between the observed value of the geometric change information and the color The number of observation values of the change information, G i is the i-th observation value of the geometric change information, C i represents the i-th observation value of the color change information, and ρ represents the Pearson correlation coefficient.
一种可选的实施方式中,所述第一压缩模块可以具体被配置成用于:In an optional implementation, the first compression module may be specifically configured to:
针对所述相关系数大于预设的系数阈值的所述子点云块,确定该所述子点云块中,每两个顶点之间的顶点距离;For the sub-point cloud block whose correlation coefficient is greater than a preset coefficient threshold, determine the vertex distance between every two vertices in the sub-point cloud block;
根据所述顶点距离以及预设的距离阈值,确定所述子点云块中顶点间的边权重,并根据所述边权重构建所述距离加权图;Determine edge weights between vertices in the sub-point cloud block based on the vertex distance and a preset distance threshold, and construct the distance-weighted graph based on the edge weights;
确定所述距离加权图对应的权重矩阵,根据所述权重矩阵确定所述子点云块对应的拉普拉斯矩阵;Determine the weight matrix corresponding to the distance weighted map, and determine the Laplacian matrix corresponding to the sub-point cloud block according to the weight matrix;
根据所述拉普拉斯矩阵压缩所述子点云块。The sub-point cloud blocks are compressed according to the Laplacian matrix.
一种可选的实施方式中,所述纹理复杂度确定模块可以具体被配置成用于:In an optional implementation, the texture complexity determination module may be specifically configured to:
针对所述相关系数小于所述系数阈值的所述子点云块,确定该所述子点云块对应的灰度共生矩阵;For the sub-point cloud block whose correlation coefficient is less than the coefficient threshold, determine the gray level co-occurrence matrix corresponding to the sub-point cloud block;
计算所述灰度共生矩阵对应的二次统计熵值,将所述二次统计熵值确定为所述纹理复杂度。Calculate the quadratic statistical entropy value corresponding to the gray level co-occurrence matrix, and determine the quadratic statistical entropy value as the texture complexity.
一种可选的实施方式中,所述第二压缩模块可以具体被配置成用于:In an optional implementation, the second compression module may be specifically configured to:
针对所述纹理复杂度大于预设的复杂度阈值的所述子点云块,确定该所述子点云块中,每两个顶点之间的顶点距离;For the sub-point cloud block whose texture complexity is greater than a preset complexity threshold, determine the vertex distance between every two vertices in the sub-point cloud block;
确定所述子点云块中每个顶点对应的颜色属性值,针对每个顶点,将该顶点对应的所述颜色属性值以及与其相邻的顶点对应的所述颜色属性值进行反距离加权计算,确定该顶点对应的颜色预测值;Determine the color attribute value corresponding to each vertex in the sub-point cloud block, and for each vertex, perform an inverse distance weighted calculation on the color attribute value corresponding to the vertex and the color attribute value corresponding to its adjacent vertex , determine the color prediction value corresponding to the vertex;
根据所述顶点距离以及每两个顶点之间所述颜色预测值的差值,确定所述子点云块中顶点间的边权重,并根据所述边权重构建所述相似度加权图,其中,所述相似度加权图用以通过引入颜色属性信息来反映所述子点云块中顶点间的纹理相似性;According to the vertex distance and the difference of the color prediction values between each two vertices, the edge weights between the vertices in the sub-point cloud block are determined, and the similarity weighted graph is constructed based on the edge weights, where , the similarity weighted map is used to reflect the texture similarity between vertices in the sub-point cloud block by introducing color attribute information;
确定所述相似度加权图对应的权重矩阵,根据所述权重矩阵确定所述子点云块对应的拉普拉斯矩阵;Determine the weight matrix corresponding to the similarity weighted map, and determine the Laplacian matrix corresponding to the sub-point cloud block according to the weight matrix;
根据所述拉普拉斯矩阵压缩所述子点云块。The sub-point cloud blocks are compressed according to the Laplacian matrix.
一种可选的实施方式中,所述第三压缩模块可以具体被配置成用于:In an optional implementation, the third compression module may be specifically configured to:
针对所述纹理复杂度小于所述复杂度阈值的所述子点云块,其中所述子点云块包括一个全局平滑块或多个局部平滑块;For the sub-point cloud block whose texture complexity is less than the complexity threshold, wherein the sub-point cloud block includes a global smooth block or a plurality of local smooth blocks;
针对所述全局平滑块,利用莫顿码编码所述全局平滑块,构造所述子点云块对应的线性图,用以描述所述全局平滑块的连通性;For the global smooth block, Morton code is used to encode the global smooth block, and a linear graph corresponding to the sub-point cloud block is constructed to describe the connectivity of the global smooth block;
针对所述局部平滑块,利用谱聚类算法,根据所述局部平滑块内顶点间的颜色差值进行聚类分析,构建所述子点云块对应的聚类连接图;For the local smooth block, use a spectral clustering algorithm to perform cluster analysis based on the color differences between vertices in the local smooth block, and construct a cluster connection graph corresponding to the sub-point cloud block;
确定所述聚类连接图对应的邻接矩阵,根据所述邻接矩阵确定所述子点云块对应的拉 普拉斯矩阵;Determine the adjacency matrix corresponding to the cluster connection graph, and determine the Laplacian matrix corresponding to the sub-point cloud block according to the adjacency matrix;
根据所述拉普拉斯矩阵压缩所述子点云块。The sub-point cloud blocks are compressed according to the Laplacian matrix.
本公开实施例还提供一种电子设备,该电子设备可以包括:处理器、存储器和总线,所述存储器存储有所述处理器可执行的机器可读指令,当电子设备运行时,所述处理器与所述存储器之间通过总线通信,所述机器可读指令被所述处理器执行时执行上述点云的压缩方法,或上述点云的压缩方法中任一种可能的实施方式中的步骤。Embodiments of the present disclosure also provide an electronic device. The electronic device may include: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processing There is communication between the processor and the memory through a bus. When the machine-readable instructions are executed by the processor, the above-mentioned point cloud compression method or the steps in any possible implementation of the above-mentioned point cloud compression method are performed. .
本公开实施例还提供一种计算机可读存储介质,该计算机可读存储介质上可以存储有计算机程序,该计算机程序被处理器运行时可以执行上述点云的压缩方法,或上述点云的压缩方法中任一种可能的实施方式中的步骤。Embodiments of the present disclosure also provide a computer-readable storage medium. A computer program can be stored on the computer-readable storage medium. When the computer program is run by a processor, it can execute the above-mentioned point cloud compression method, or the above-mentioned point cloud compression. steps in any possible implementation of the method.
本公开实施例提供的一种点云的压缩方法、装置、电子设备及存储介质,通过获取待压缩点云,将待压缩点云划分为多个子点云块;针对每个子点云块,确定该子点云块对应的几何属性信息与颜色属性信息之间的相关系数;针对相关系数大于预设的系数阈值的子点云块,根据几何属性信息确定该子点云块对应的距离加权图,并基于距离加权图压缩子点云块;针对相关系数小于系数阈值的子点云块,计算该子点云块对应的纹理复杂度;针对纹理复杂度大于预设的复杂度阈值的子点云块,确定该子点云块对应的相似度加权图,并基于相似度加权图压缩子点云块;针对纹理复杂度小于复杂度阈值的子点云块,确定该子点云块对应的无权图,并基于无权图压缩子点云块。可以充分考虑点云的几何属性、颜色属性、纹理信息之间的相关性,并具有较优的压缩性能。Embodiments of the present disclosure provide a point cloud compression method, device, electronic device, and storage medium. By obtaining the point cloud to be compressed, the point cloud to be compressed is divided into multiple sub-point cloud blocks; for each sub-point cloud block, determine The correlation coefficient between the geometric attribute information and the color attribute information corresponding to the sub-point cloud block; for the sub-point cloud block whose correlation coefficient is greater than the preset coefficient threshold, the distance weighted map corresponding to the sub-point cloud block is determined based on the geometric attribute information , and compress the sub-point cloud blocks based on the distance weighted map; for sub-point cloud blocks whose correlation coefficient is less than the coefficient threshold, calculate the texture complexity corresponding to the sub-point cloud block; for sub-points whose texture complexity is greater than the preset complexity threshold Cloud block, determine the similarity weighted map corresponding to the sub-point cloud block, and compress the sub-point cloud block based on the similarity weighted map; for sub-point cloud blocks whose texture complexity is less than the complexity threshold, determine the corresponding sub-point cloud block Unweighted graph, and compressed sub-point cloud patches based on the unweighted graph. The correlation between the geometric attributes, color attributes, and texture information of the point cloud can be fully considered, and it has better compression performance.
为使本公开的上述目的、特征和优点能更明显易懂,下文特举较佳实施例,并配合所附附图,作详细说明如下。In order to make the above-mentioned objects, features and advantages of the present disclosure more obvious and understandable, preferred embodiments are given below and described in detail with reference to the accompanying drawings.
为了更清楚地说明本公开实施例的技术方案,下面将对实施例中所需要使用的附图作简单地介绍,此处的附图被并入说明书中并构成本说明书中的一部分,这些附图示出了符合本公开的实施例,并与说明书一起用于说明本公开的技术方案。应当理解,以下附图仅示出了本公开的某些实施例,因此不应被看作是对范围的限定,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他相关的附图。In order to explain the technical solutions of the embodiments of the present disclosure more clearly, the drawings required to be used in the embodiments will be briefly introduced below. The drawings here are incorporated into the specification and constitute a part of this specification. These drawings are The drawings illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the technical solutions of the present disclosure. It should be understood that the following drawings only illustrate certain embodiments of the present disclosure, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without exerting creative efforts, they can also Other relevant drawings are obtained based on these drawings.
图1示出了本公开实施例所提供的一种点云的压缩方法的流程图;Figure 1 shows a flow chart of a point cloud compression method provided by an embodiment of the present disclosure;
图2示出了本公开实施例所提供的一种纹理光滑子点云块的压缩方法的流程图;Figure 2 shows a flow chart of a compression method for textured smooth sub-point cloud blocks provided by an embodiment of the present disclosure;
图3示出了本公开实施例所提供的一种点云的压缩装置的示意图;Figure 3 shows a schematic diagram of a point cloud compression device provided by an embodiment of the present disclosure;
图4示出了本公开实施例所提供的一种电子设备的示意图。FIG. 4 shows a schematic diagram of an electronic device provided by an embodiment of the present disclosure.
图中:300-压缩装置;310-划分模块;320-相关性确定模块;330-第一压缩模块;340-纹理复杂度确定模块;350-第二压缩模块;360-第三压缩模块;400-电子设备;41-处理器;42-存储器;421-内存;422-外部存储器;43-总线。In the figure: 300-compression device; 310-division module; 320-correlation determination module; 330-first compression module; 340-texture complexity determination module; 350-second compression module; 360-third compression module; 400 - Electronic equipment; 41-processor; 42-storage; 421-memory; 422-external memory; 43-bus.
为使本公开实施例的目的、技术方案和优点更加清楚,下面将结合本公开实施例中附图,对本公开实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本公开一部分实施例,而不是全部的实施例。通常在此处附图中描述和示出的本公开实施例的组件可以以各种不同的配置来布置和设计。因此,以下对在附图中提供的本公开的实施例的详细描述并非旨在限制要求保护的本公开的范围,而是仅仅表示本公开的选定实施例。基于本公开的实施例,本领域技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例,都属于本公开保护的范围。In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only These are some embodiments of the present disclosure, but not all embodiments. The components of the embodiments of the present disclosure generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the disclosure provided in the appended drawings is not intended to limit the scope of the claimed disclosure, but rather to represent selected embodiments of the disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without any creative efforts shall fall within the scope of protection of the present disclosure.
应注意到:相似的标号和字母在下面的附图中表示类似项,因此,一旦某一项在一个附图中被定义,则在随后的附图中不需要对其进行进一步定义和解释。It should be noted that similar reference numerals and letters represent similar items in the following figures, therefore, once an item is defined in one figure, it does not need further definition and explanation in subsequent figures.
本文中术语“和/或”,仅仅是描述一种关联关系,表示可以存在三种关系,例如,A和/或B,可以表示:单独存在A,同时存在A和B,单独存在B这三种情况。另外,本文中 术语“至少一种”表示多种中的任意一种或多种中的至少两种的任意组合,例如,包括A、B、C中的至少一种,可以表示包括从A、B和C构成的集合中选择的任意一个或多个元素。The term "and/or" in this article only describes an association relationship, indicating that three relationships can exist. For example, A and/or B can mean: A alone exists, A and B exist simultaneously, and B alone exists. situation. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, and C, which can mean including from A, Any one or more elements selected from the set composed of B and C.
经研究发现,目前,各种对于点云属性信息的压缩方法已经被提出,变换技术是其中的一个重要分支。目前,点云属性压缩的变换技术可以大致分为三大类:区域自适应层次变换、图傅里叶变换和提升变换。但是上述方法并未考虑点云几何与颜色、纹理信息之间的相关性,压缩性能较差。Research has found that currently, various compression methods for point cloud attribute information have been proposed, and transformation technology is an important branch among them. At present, transformation technologies for point cloud attribute compression can be roughly divided into three categories: region-adaptive hierarchical transformation, graph Fourier transform, and lifting transform. However, the above method does not consider the correlation between point cloud geometry and color and texture information, and the compression performance is poor.
基于上述研究,本公开提供了一种点云的压缩方法、装置、电子设备及存储介质,通过获取待压缩点云,将待压缩点云划分为多个子点云块;针对每个子点云块,确定该子点云块对应的几何属性信息与颜色属性信息之间的相关系数;针对相关系数大于预设的系数阈值的子点云块,根据几何属性信息确定该子点云块对应的距离加权图,并基于距离加权图压缩子点云块;针对相关系数小于系数阈值的子点云块,计算该子点云块对应的纹理复杂度;针对纹理复杂度大于预设的复杂度阈值的子点云块,确定该子点云块对应的相似度加权图,并基于相似度加权图压缩子点云块;针对纹理复杂度小于复杂度阈值的子点云块,确定该子点云块对应的无权图,并基于无权图压缩子点云块。可以充分考虑点云的几何属性、颜色属性、纹理信息之间的相关性,并具有较优的压缩性能。Based on the above research, the present disclosure provides a point cloud compression method, device, electronic device and storage medium. By obtaining the point cloud to be compressed, the point cloud to be compressed is divided into multiple sub-point cloud blocks; for each sub-point cloud block , determine the correlation coefficient between the geometric attribute information and color attribute information corresponding to the sub-point cloud block; for the sub-point cloud block whose correlation coefficient is greater than the preset coefficient threshold, determine the distance corresponding to the sub-point cloud block based on the geometric attribute information Weighted map, and compress the sub-point cloud blocks based on the distance-weighted map; for the sub-point cloud blocks whose correlation coefficient is less than the coefficient threshold, calculate the texture complexity corresponding to the sub-point cloud block; for the sub-point cloud blocks whose texture complexity is greater than the preset complexity threshold For sub-point cloud blocks, determine the similarity weighted map corresponding to the sub-point cloud block, and compress the sub-point cloud block based on the similarity weighted map; for sub-point cloud blocks whose texture complexity is less than the complexity threshold, determine the sub-point cloud block The corresponding unweighted graph is used to compress the sub-point cloud blocks based on the unweighted graph. The correlation between the geometric attributes, color attributes, and texture information of the point cloud can be fully considered, and it has better compression performance.
为便于对本实施例进行理解,首先对本公开实施例所公开的一种点云的压缩方法进行详细介绍,本公开实施例所提供的点云的压缩方法的执行主体一般为具有一定计算能力的计算机设备,该计算机设备例如包括:终端设备或服务器或其它处理设备,终端设备可以为用户设备(User Equipment,UE)、移动设备、用户终端、终端、蜂窝电话、无绳电话、个人数字助理(Personal Digital Assistant,PDA)、手持设备、计算设备、车载设备、可穿戴设备等。在一些可能的实现方式中,该点云的压缩方法可以通过处理器调用存储器中存储的计算机可读指令的方式来实现。In order to facilitate the understanding of this embodiment, a point cloud compression method disclosed in the embodiment of the disclosure is first introduced in detail. The execution subject of the point cloud compression method provided by the embodiment of the disclosure is generally a computer with certain computing capabilities. Equipment, the computer equipment includes, for example: terminal equipment or servers or other processing equipment. The terminal equipment can be user equipment (User Equipment, UE), mobile equipment, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (Personal Digital Assistant) Assistant, PDA), handheld devices, computing devices, vehicle-mounted devices, wearable devices, etc. In some possible implementations, the point cloud compression method can be implemented by the processor calling computer-readable instructions stored in the memory.
参见图1所示,为本公开实施例提供的一种点云的压缩方法的流程图,所述方法包括步骤S101~S106,其中:Referring to Figure 1, a flow chart of a point cloud compression method is provided according to an embodiment of the present disclosure. The method includes steps S101 to S106, wherein:
S101、获取待压缩点云,将所述待压缩点云划分为多个子点云块。S101. Obtain a point cloud to be compressed and divide the point cloud to be compressed into multiple sub-point cloud blocks.
在具体实施中,可以利用三维KD树结构对待压缩点云进行块划分,对于待压缩点云中的每一个非叶节点,可以被一个超平面划分为两个子空间,并且相应的每一个子空间又可以以相同的方式递归地进行分割划分完毕后,每一个叶子节点即为一个子点云块。In specific implementation, the three-dimensional KD tree structure can be used to divide the point cloud to be compressed into blocks. For each non-leaf node in the point cloud to be compressed, it can be divided into two subspaces by a hyperplane, and each corresponding subspace It can be divided recursively in the same way. After the division is completed, each leaf node is a child point cloud block.
需要说明的是,KD树的分割是沿坐标轴进行的,所有超平面都垂直于相应的坐标轴。例如沿着x轴分割,只需要给定某x值就可以确定超平面的位置,该超平面将原节点空间分割成两个子空间,其中一个子空间的所有点的x值均小于另外一个子空间中所有点的x值。It should be noted that the segmentation of the KD tree is performed along the coordinate axes, and all hyperplanes are perpendicular to the corresponding coordinate axes. For example, when dividing along the x-axis, you only need to give a certain x value to determine the position of the hyperplane. The hyperplane divides the original node space into two subspaces, and the x values of all points in one subspace are smaller than the other. x-values for all points in space.
S102、针对每个所述子点云块,确定该所述子点云块对应的几何属性信息与颜色属性信息之间的相关系数。S102. For each sub-point cloud block, determine the correlation coefficient between the geometric attribute information and the color attribute information corresponding to the sub-point cloud block.
在该步骤中,针对每一个子点云块,分析每个子点云块内几何信息与属性信息之间相关性。In this step, for each sub-point cloud block, the correlation between the geometric information and attribute information in each sub-point cloud block is analyzed.
可选地,可以针对每个所述子点云块,获取该所述子点云块内,各个顶点间的信号差值产生的几何变化信息与颜色变化信息;将所述几何变化信息与所述颜色变化信息作为所述几何属性信息与所述颜色属性信息之间相关性的评价指标,获取多个所述几何变化信息对应的观测值以及多个所述颜色变化信息对应的观测值;根据所述几何变化信息对应的观测值以及所述颜色变化信息对应的观测值,确定所述子点云块对应的皮尔森相关系数;将所述皮尔森相关系数确定为所述几何属性信息与所述颜色属性信息之间的相关系数。Optionally, for each sub-point cloud block, the geometric change information and color change information generated by the signal differences between vertices in the sub-point cloud block can be obtained; the geometric change information and the The color change information is used as an evaluation index for the correlation between the geometric attribute information and the color attribute information, and a plurality of observation values corresponding to the geometric change information and a plurality of observation values corresponding to the color change information are obtained; according to The observed values corresponding to the geometric change information and the observed values corresponding to the color change information are determined to determine the Pearson correlation coefficient corresponding to the sub-point cloud block; the Pearson correlation coefficient is determined as the geometric attribute information and the The correlation coefficient between the color attribute information.
作为一种可能的实施方式,可以基于以下公式确定所述子点云块对应的皮尔森相关系数:As a possible implementation, the Pearson correlation coefficient corresponding to the sub-point cloud block can be determined based on the following formula:
其中,G为代表所述几何变化信息的参数、C为代表所述颜色变化信息的参数、μ
G代表所述几何变化信息的观测值对应的均值、σ
G代表所述几何变化信息的观测值对应的标准差、μ
C代表所述颜色变化信息的观测值对应的均值、σ
C代表所述颜色变化信息的观测值对应的标准差、M代表所述几何变化信息的观测值与所述颜色变化信息的观测值的数量、G
i所述几何变化信息的第i个观测值、C
i代表所述颜色变化信息的第i个观测值、ρ代表所述皮尔森相关系数。
Wherein, G is a parameter representing the geometric change information, C is a parameter representing the color change information, μ G represents the mean value corresponding to the observed value of the geometric change information, and σ G represents the observed value of the geometric change information. The corresponding standard deviation, μ C represents the mean value corresponding to the observed value of the color change information, σ C represents the standard deviation corresponding to the observed value of the color change information, M represents the difference between the observed value of the geometric change information and the color The number of observation values of the change information, G i is the i-th observation value of the geometric change information, C i represents the i-th observation value of the color change information, and ρ represents the Pearson correlation coefficient.
在具体实施中,几何变化信息与颜色变化信息具有N×(N-1)/2个观测值,其中N为子点云块中顶点的个数。In a specific implementation, the geometric change information and color change information have N×(N-1)/2 observation values, where N is the number of vertices in the sub-point cloud block.
可选地,在确定子点云块中几何属性信息与颜色属性信息之间的相关系数之后,可以将该相关系数的绝对值与一个预设阈值进行二值化操作,用以反映子点云块中几何属性信息与颜色属性信息之间相关性的强弱。Optionally, after determining the correlation coefficient between the geometric attribute information and the color attribute information in the sub-point cloud block, the absolute value of the correlation coefficient can be binarized with a preset threshold to reflect the sub-point cloud. The strength of the correlation between geometric attribute information and color attribute information in the block.
S103、针对所述相关系数大于预设的系数阈值的所述子点云块,根据所述几何属性信息确定该所述子点云块对应的距离加权图,并基于所述距离加权图压缩所述子点云块。S103. For the sub-point cloud block whose correlation coefficient is greater than the preset coefficient threshold, determine the distance-weighted map corresponding to the sub-point cloud block based on the geometric attribute information, and compress the distance-weighted map based on the distance-weighted map. Describe the point cloud block.
在该步骤中,若相关系数大于预设的系数阈值,这说明该子点云块的几何属性信息与颜色属性信息之间具有较强相关性。针对几何属性信息与颜色属性信息之间具有较强相关性的子点云块,则仅使用几何信息来构建子点云块中顶点信号之间的关系,确定该子点云块对应的距离加权图,并基于距离加权图压缩子点云块。In this step, if the correlation coefficient is greater than the preset coefficient threshold, it means that there is a strong correlation between the geometric attribute information and the color attribute information of the sub-point cloud block. For sub-point cloud blocks with strong correlation between geometric attribute information and color attribute information, only geometric information is used to construct the relationship between vertex signals in the sub-point cloud block and determine the distance weighting corresponding to the sub-point cloud block. graph, and compress the sub-point cloud blocks based on the distance-weighted graph.
这里,可以基于以下方法构建子点云块对应的距离加权图:针对所述相关系数大于预设的系数阈值的所述子点云块,确定该所述子点云块中,每两个顶点之间的顶点距离;根据所述顶点距离以及预设的距离阈值,确定所述子点云块中顶点间的边权重,并根据所述边权重构建所述距离加权图。Here, the distance weighted map corresponding to the sub-point cloud block can be constructed based on the following method: for the sub-point cloud block whose correlation coefficient is greater than the preset coefficient threshold, determine the number of vertices of every two vertices in the sub-point cloud block. the vertex distance between them; determine the edge weight between the vertices in the sub-point cloud block according to the vertex distance and the preset distance threshold, and construct the distance-weighted graph based on the edge weight.
需要说明的是,预设的系数阈值可以根据实际需要进行选择,在此不做具体限制。It should be noted that the preset coefficient threshold can be selected according to actual needs, and there is no specific restriction here.
在具体实施中,可以使用基于阈值的高斯核函数来定义距离加权图中的权重,具体如下公式所示:In a specific implementation, a threshold-based Gaussian kernel function can be used to define the weights in the distance-weighted graph, as shown in the following formula:
其中,d
i,j代表子点云块中,顶点i与顶点j之间的欧氏距离;δ代表子点云块中,顶点i与顶点j之间欧氏距离的平均绝对值偏差;τ代表预设的欧氏距离的阈值,τ值可以根据实际需要进行设置,在此不做具体限制。
Among them, d i, j represents the Euclidean distance between vertex i and vertex j in the sub-point cloud block; δ represents the average absolute value deviation of the Euclidean distance between vertex i and vertex j in the sub-point cloud block; τ Represents the preset Euclidean distance threshold. The τ value can be set according to actual needs, and there are no specific restrictions here.
可选地,根据距离加权图中连接各顶点之间的边对应的边权重,可以确定所述距离加权图对应的权重矩阵,根据所述权重矩阵确定所述子点云块对应的拉普拉斯矩阵;根据所述拉普拉斯矩阵压缩所述子点云块。Optionally, according to the edge weight corresponding to the edge connecting each vertex in the distance-weighted graph, the weight matrix corresponding to the distance-weighted graph can be determined, and the Laplan corresponding to the sub-point cloud block is determined according to the weight matrix. Laplacian matrix; compress the sub-point cloud block according to the Laplacian matrix.
在具体实施中,确定距离加权图对应的权重矩阵后,可以根据该权重矩阵确定对应的 度矩阵,其中度矩阵为一个对角矩阵,对角线上元素为权重矩阵中每行元素之和,根据度矩阵与权重矩阵之差即可确定子点云块对应的拉普拉斯矩阵。拉普拉斯矩阵对应的特征向量可以反映子点云块对应的属性信息,将拉普拉斯矩阵对应的特征向量利用预设的压缩机进行编码压缩后,即可完成对子点云块的压缩。In a specific implementation, after determining the weight matrix corresponding to the distance weighted graph, the corresponding degree matrix can be determined based on the weight matrix, where the degree matrix is a diagonal matrix, and the elements on the diagonal are the sum of the elements in each row of the weight matrix, The Laplacian matrix corresponding to the sub-point cloud block can be determined based on the difference between the degree matrix and the weight matrix. The eigenvector corresponding to the Laplacian matrix can reflect the attribute information corresponding to the sub-point cloud block. After encoding and compressing the eigenvector corresponding to the Laplacian matrix using the preset compressor, the sub-point cloud block can be completed. compression.
S104、针对所述相关系数小于所述系数阈值的所述子点云块,计算该所述子点云块对应的纹理复杂度。S104. For the sub-point cloud block whose correlation coefficient is less than the coefficient threshold, calculate the texture complexity corresponding to the sub-point cloud block.
在该步骤中,若相关系数小于系数阈值,则说明该子点云块的几何属性信息与颜色属性信息之间具有较弱相关性。针对几何属性信息与颜色属性信息之间具有较弱相关性的子点云块,需要进行纹理复杂度分析,确定子点云块对应的纹理复杂度。In this step, if the correlation coefficient is less than the coefficient threshold, it means that there is a weak correlation between the geometric attribute information and the color attribute information of the sub-point cloud block. For sub-point cloud blocks with weak correlation between geometric attribute information and color attribute information, texture complexity analysis needs to be performed to determine the texture complexity corresponding to the sub-point cloud block.
作为一种可能的实施方式,可以采用以下方法计算子点云块对应的纹理复杂度:针对所述相关系数小于所述系数阈值的所述子点云块,确定该所述子点云块对应的灰度共生矩阵;计算所述灰度共生矩阵对应的二次统计熵值,将所述二次统计熵值确定为所述纹理复杂度。As a possible implementation, the following method can be used to calculate the texture complexity corresponding to the sub-point cloud block: for the sub-point cloud block whose correlation coefficient is less than the coefficient threshold, determine the corresponding texture complexity of the sub-point cloud block. The gray level co-occurrence matrix; calculate the secondary statistical entropy value corresponding to the gray level co-occurrence matrix, and determine the secondary statistical entropy value as the texture complexity.
这里,可以使用灰度共生矩阵(Gray-Level Co-occurrence Matrix,GLCM)为纹理复杂度度量,用以计算子点云块中灰度对的出现次数,其中,灰度对为子点云块中的顶点与其相邻的顶点之间的量化亮度值。Here, the Gray-Level Co-occurrence Matrix (GLCM) can be used as a texture complexity measure to calculate the number of occurrences of gray-level pairs in sub-point cloud blocks, where the gray-level pairs are sub-point cloud blocks. The quantized brightness value between a vertex in and its adjacent vertices.
需要说明的是,为了克服熵度量的随机性和无序性,可以使用对于GLCM的二次统计熵值来评估子点云块的纹理复杂度,计算二次统计熵值的公式如下所示:It should be noted that in order to overcome the randomness and disorder of the entropy measurement, the quadratic statistical entropy value for GLCM can be used to evaluate the texture complexity of the sub-point cloud block. The formula for calculating the quadratic statistical entropy value is as follows:
其中,Entropy代表灰度共生矩阵对应的二次统计熵;L代表预设的灰度级别,可以根据实际需要进行设置,在此不做具体限制;GLCM
i,j代表灰度共生矩阵。
Among them, Entropy represents the secondary statistical entropy corresponding to the gray-level co-occurrence matrix; L represents the preset gray level, which can be set according to actual needs, and there are no specific restrictions here; GLCM i, j represents the gray-level co-occurrence matrix.
在具体实施中,二次统计熵值越高代表子点云块的纹理越复杂,二次统计熵值越低则代表子点云块的纹理越平滑。In a specific implementation, the higher the secondary statistical entropy value represents, the more complex the texture of the sub-point cloud block is, and the lower the secondary statistical entropy value represents, the smoother the texture of the sub-point cloud block.
S105、针对所述纹理复杂度大于预设的复杂度阈值的所述子点云块,确定该所述子点云块对应的相似度加权图,并基于所述相似度加权图压缩所述子点云块。S105. For the sub-point cloud block whose texture complexity is greater than the preset complexity threshold, determine the similarity weighted map corresponding to the sub-point cloud block, and compress the sub-point cloud block based on the similarity weighted map. Point cloud blocks.
在该步骤中,若纹理复杂度大于预设的复杂度阈值,则说明该子点云块的纹理较为复杂。针对几何属性信息与颜色属性信息之间具有较弱相关性但是纹理较为复杂的子点云块,需要引入颜色空间来提供更多信息,因此采用相似度加权图进行压缩。In this step, if the texture complexity is greater than the preset complexity threshold, it means that the texture of the sub-point cloud block is relatively complex. For sub-point cloud blocks that have weak correlation between geometric attribute information and color attribute information but have complex textures, a color space needs to be introduced to provide more information, so a similarity weighted map is used for compression.
需要说明的是,预设的复杂度阈值可以根据实际需要进行选择,在此不做具体限制。It should be noted that the preset complexity threshold can be selected according to actual needs, and there is no specific restriction here.
作为一种可能的实施方式,可以采用以下方法确定反映子点云块中顶点间的纹理相似性的相似度加权图:确定所述子点云块中每个顶点对应的颜色属性值,针对每个顶点,将该顶点对应的所述颜色属性值以及与其相邻的顶点对应的所述颜色属性值进行反距离加权计算,确定该顶点对应的颜色预测值;根据所述顶点距离以及每两个顶点之间所述颜色预测值的差值,确定所述子点云块中顶点间的边权重,并根据所述边权重构建所述相似度加权图,其中,所述相似度加权图用以通过引入颜色属性信息来反映所述子点云块中顶点间的纹理相似性。As a possible implementation, the following method can be used to determine a similarity weighted map that reflects the texture similarity between vertices in the sub-point cloud block: determine the color attribute value corresponding to each vertex in the sub-point cloud block, and for each a vertex, perform an inverse distance weighted calculation on the color attribute value corresponding to the vertex and the color attribute value corresponding to its adjacent vertex, and determine the color prediction value corresponding to the vertex; according to the vertex distance and each two The difference between the color prediction values between vertices is used to determine the edge weights between vertices in the sub-point cloud block, and the similarity weighted graph is constructed based on the edge weights, where the similarity weighted graph is used to The texture similarity between vertices in the sub-point cloud blocks is reflected by introducing color attribute information.
具体的,可以基于以下公式定义相似度加权图中的边权重:Specifically, the edge weights in the similarity-weighted graph can be defined based on the following formula:
其中,d
i,j代表子点云块中,顶点i与顶点j之间的欧氏距离;δ代表子点云块中,顶点i与顶点j之间欧氏距离的平均绝对值偏差;τ代表预设的欧氏距离的阈值,τ值可以根据实际需要进行设置,在此不做具体限制;p
i,j是代表子点云块中,顶点i与顶点j之间颜色预测值之间的差值;θ代表子点云块中,顶点i与顶点j之间颜色预测值的差值之间的平均绝对值偏差;ψ代表子点云块中,顶点i与顶点j之间颜色预测值的差值对应的预设阈值,ψ值可以根据实际需要进行设置,在此不做具体限制。
Among them, d i, j represents the Euclidean distance between vertex i and vertex j in the sub-point cloud block; δ represents the average absolute value deviation of the Euclidean distance between vertex i and vertex j in the sub-point cloud block; τ Represents the preset Euclidean distance threshold. The τ value can be set according to actual needs, and there are no specific restrictions here; p i, j represents the difference between the color prediction values between vertex i and vertex j in the sub-point cloud block. The difference of The preset threshold corresponding to the difference in values, and the ψ value can be set according to actual needs, and there are no specific restrictions here.
需要说明的是,子点云块中顶点的颜色预测值可以通过子点云块中相邻顶点颜色属性值的反距离加权获得,具体公式如下所示:It should be noted that the color prediction value of the vertex in the sub-point cloud block can be obtained by the inverse distance weighting of the color attribute values of adjacent vertices in the sub-point cloud block. The specific formula is as follows:
其中,p
i代表顶点i对应的颜色预测值;r
j代表顶点j对应的颜色重建值,顶点j为与顶点i相邻的已编码节点;d
i,j代表顶点i与顶点j之间的欧氏距离。
Among them, p i represents the color prediction value corresponding to vertex i; r j represents the color reconstruction value corresponding to vertex j, and vertex j is the encoded node adjacent to vertex i; d i,j represents the distance between vertex i and vertex j Euclidean distance.
可选地,根据相似度加权图中连接各顶点之间的边对应的边权重,可以确定所述相似度加权图对应的权重矩阵,根据所述权重矩阵确定所述子点云块对应的拉普拉斯矩阵;根据所述拉普拉斯矩阵压缩所述子点云块。Optionally, according to the edge weight corresponding to the edge connecting each vertex in the similarity weighted graph, the weight matrix corresponding to the similarity weighted graph can be determined, and the pull corresponding to the sub-point cloud block can be determined according to the weight matrix. Laplacian matrix; compress the sub-point cloud blocks according to the Laplacian matrix.
其中,相似度加权图对应的权重矩阵为一种同时考虑子点云块几何信息和属性信息的图傅里叶变换权重矩阵。Among them, the weight matrix corresponding to the similarity weighted graph is a graph Fourier transform weight matrix that considers both the geometric information and attribute information of the sub-point cloud blocks.
在具体实施中,确定相似度加权图对应的权重矩阵后,可以根据该权重矩阵确定对应的度矩阵,其中度矩阵为一个对角矩阵,对角线上元素为权重矩阵中每行元素之和,根据度矩阵与权重矩阵之差即可确定子点云块对应的拉普拉斯矩阵。拉普拉斯矩阵对应的特征向量可以反映子点云块对应的属性信息,将拉普拉斯矩阵对应的特征向量利用预设的压缩机进行编码压缩后,即可完成对该子点云块的压缩。In a specific implementation, after determining the weight matrix corresponding to the similarity weighted graph, the corresponding degree matrix can be determined based on the weight matrix, where the degree matrix is a diagonal matrix, and the elements on the diagonal are the sum of the elements in each row of the weight matrix. , the Laplacian matrix corresponding to the sub-point cloud block can be determined based on the difference between the degree matrix and the weight matrix. The feature vector corresponding to the Laplacian matrix can reflect the attribute information corresponding to the sub-point cloud block. After encoding and compressing the feature vector corresponding to the Laplacian matrix using the preset compressor, the sub-point cloud block can be completed. of compression.
S106、针对所述纹理复杂度小于所述复杂度阈值的所述子点云块,确定该所述子点云块对应的无权图,并基于所述无权图压缩所述子点云块。S106. For the sub-point cloud block whose texture complexity is less than the complexity threshold, determine the unweighted map corresponding to the sub-point cloud block, and compress the sub-point cloud block based on the unweighted map. .
在该步骤中,若纹理复杂度小于预设的复杂度阈值,则说明该子点云块的纹理较为光滑。针对几何属性信息与颜色属性信息之间具有较弱相关性但是纹理较为光滑的子点云块,则采用无权图进行压缩。In this step, if the texture complexity is less than the preset complexity threshold, it means that the texture of the sub-point cloud block is relatively smooth. For sub-point cloud blocks that have weak correlation between geometric attribute information and color attribute information but have smooth textures, unweighted graphs are used for compression.
作为一种可能的实施方式,针对纹理较为光滑的子点云块的压缩方法可以参见图2所示,图2为本公开实施例提供的一种纹理光滑子点云块的压缩方法的流程图,所述方法包括步骤S1061~S1064,其中,纹理复杂度小于所述复杂度阈值的所述子点云块包括一个相似数据簇,即全局平滑块或多个相似数据块,即局部平滑块。As a possible implementation, the compression method for sub-point cloud blocks with smooth texture can be seen in Figure 2. Figure 2 is a flow chart of a compression method for sub-point cloud blocks with smooth texture provided by an embodiment of the present disclosure. , the method includes steps S1061 to S1064, wherein the sub-point cloud blocks whose texture complexity is less than the complexity threshold include one similar data cluster, that is, a global smooth block or multiple similar data blocks, that is, a local smooth block.
S1061、针对所述全局平滑块,利用莫顿码编码所述全局平滑块,构造所述子点云块对应的线性图,用以描述所述全局平滑块的连通性。S1061. For the global smooth block, use Morton code to encode the global smooth block, and construct a linear graph corresponding to the sub-point cloud block to describe the connectivity of the global smooth block.
在具体实施中,用于压缩纹理平滑子点云块的无权图包括基于莫顿编码的线形图,这里采用基于莫顿编码的线形图描述纹理平滑子点云块中,全局平滑块的连通性。In a specific implementation, the unweighted graph used to compress the texture smooth sub-point cloud block includes a line graph based on Morton coding. Here, a line graph based on Morton coding is used to describe the connectivity of the global smooth block in the texture smooth sub-point cloud block. sex.
这样,采用基于莫顿编码的线形图描述纹理平滑子点云块中的连通性进而代替完全连接图,可以降低计算复杂度。In this way, using a line graph based on Morton coding to describe the connectivity in texture smooth sub-point cloud blocks instead of a fully connected graph can reduce computational complexity.
S1062、针对所述局部平滑块,利用谱聚类算法,根据所述局部平滑块内顶点间的颜色差值进行聚类分析,构建所述子点云块对应的聚类连接图。S1062. For the local smooth block, use a spectral clustering algorithm to perform cluster analysis based on the color differences between vertices in the local smooth block, and construct a cluster connection graph corresponding to the sub-point cloud block.
在具体实施中,用于压缩纹理平滑子点云块的无权图还包括基于聚类分析的连接图,这里采用基于聚类分析的连接图描述纹理平滑子点云块中,局部平滑块的连通性。In a specific implementation, the unweighted graph used to compress the texture smooth sub-point cloud block also includes a connection graph based on cluster analysis. Here, a connection graph based on cluster analysis is used to describe the local smooth block in the texture smooth sub-point cloud block. Connectivity.
具体的,进行聚类分析可以将纹理较为光滑的子点云块中的多个局部平滑区域进行聚类,以更好的分析局部平滑区域内各个顶点之间的相关性。Specifically, cluster analysis can be performed to cluster multiple local smooth areas in sub-point cloud blocks with smooth textures to better analyze the correlation between vertices in the local smooth areas.
作为一种可能的实施方式,聚类分析可以采用谱聚类的方式,其所使用的图聚类权重可以采用高斯核函数,通过以下公式定义:As a possible implementation, cluster analysis can adopt spectral clustering, and the graph clustering weight used can adopt Gaussian kernel function, which is defined by the following formula:
其中,W
i,j代表连接顶点i和j的边的权值,c(i,j)代表顶点i和j的颜色差值,θ代表c对应的平均绝对偏差,用来控制相似度度量对差异的敏感度。
Among them, W i,j represents the weight of the edge connecting vertices i and j, c(i,j) represents the color difference between vertices i and j, and θ represents the average absolute deviation corresponding to c, which is used to control the similarity measure pair Differential sensitivity.
S1063、确定所述聚类连接图对应的邻接矩阵,根据所述邻接矩阵确定所述子点云块对应的拉普拉斯矩阵。S1063. Determine the adjacency matrix corresponding to the cluster connection graph, and determine the Laplacian matrix corresponding to the sub-point cloud block according to the adjacency matrix.
在具体实施中,可以采用邻接矩阵来描述聚类连接图中,针对纹理平滑子点云块中,局部平滑块聚类内的强相关性和聚类间的弱相关性,在确定聚类连接图对应的邻接矩阵之后,可以根据该邻接矩阵确定对应的度矩阵,其中度矩阵为一个对角矩阵,对角线上元素为邻接矩阵中每行元素之和,根据度矩阵与邻接矩阵之差即可确定子点云块对应的拉普拉斯矩阵。In a specific implementation, the adjacency matrix can be used to describe the cluster connection graph. For the strong correlation within the local smooth block cluster and the weak correlation between clusters in the texture smooth sub-point cloud block, the cluster connection is determined. After the adjacency matrix corresponding to the graph, the corresponding degree matrix can be determined based on the adjacency matrix. The degree matrix is a diagonal matrix. The elements on the diagonal are the sum of the elements in each row of the adjacency matrix. According to the difference between the degree matrix and the adjacency matrix The Laplacian matrix corresponding to the sub-point cloud block can be determined.
S1064、根据所述拉普拉斯矩阵压缩所述子点云块。S1064. Compress the sub-point cloud block according to the Laplacian matrix.
在具体实施中,拉普拉斯矩阵对应的特征向量可以反映子点云块对应的属性信息,将拉普拉斯矩阵对应的特征向量利用预设的压缩机进行编码压缩后,即可完成对该子点云块的压缩。In a specific implementation, the feature vector corresponding to the Laplacian matrix can reflect the attribute information corresponding to the sub-point cloud block. After encoding and compressing the feature vector corresponding to the Laplacian matrix using a preset compressor, the comparison can be completed. Compression of this sub-point cloud block.
本公开实施例提供的一种点云的压缩方法,通过获取待压缩点云,将待压缩点云划分为多个子点云块;针对每个子点云块,确定该子点云块对应的几何属性信息与颜色属性信息之间的相关系数;针对相关系数大于预设的系数阈值的子点云块,根据几何属性信息确定该子点云块对应的距离加权图,并基于距离加权图压缩子点云块;针对相关系数小于系数阈值的子点云块,计算该子点云块对应的纹理复杂度;针对纹理复杂度大于预设的复杂度阈值的子点云块,确定该子点云块对应的相似度加权图,并基于相似度加权图压缩子点云块;针对纹理复杂度小于复杂度阈值的子点云块,确定该子点云块对应的无权图,并基于无权图压缩子点云块。可以充分考虑点云的几何属性、颜色属性、纹理信息之间的相关性,以实现较优的压缩性能。A point cloud compression method provided by an embodiment of the present disclosure, by obtaining a point cloud to be compressed, dividing the point cloud to be compressed into a plurality of sub-point cloud blocks; for each sub-point cloud block, determining the geometry corresponding to the sub-point cloud block Correlation coefficient between attribute information and color attribute information; for sub-point cloud blocks whose correlation coefficient is greater than the preset coefficient threshold, determine the distance-weighted map corresponding to the sub-point cloud block based on the geometric attribute information, and compress the sub-point cloud block based on the distance-weighted map Point cloud blocks; for sub-point cloud blocks whose correlation coefficient is less than the coefficient threshold, calculate the texture complexity corresponding to the sub-point cloud block; for sub-point cloud blocks whose texture complexity is greater than the preset complexity threshold, determine the sub-point cloud block similarity weighted map corresponding to the block, and compress the sub-point cloud block based on the similarity weighted map; for sub-point cloud blocks whose texture complexity is less than the complexity threshold, determine the unweighted map corresponding to the sub-point cloud block, and compress the sub-point cloud block based on the unweighted Graph compressed sub-point cloud patches. The correlation between the geometric attributes, color attributes, and texture information of the point cloud can be fully considered to achieve better compression performance.
本领域技术人员可以理解,在具体实施方式的上述方法中,各步骤的撰写顺序并不意味着严格的执行顺序而对实施过程构成任何限定,各步骤的具体执行顺序应当以其功能和可能的内在逻辑确定。Those skilled in the art can understand that in the above-mentioned methods of specific embodiments, the writing order of each step does not mean a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be based on its function and possible The internal logic is determined.
基于同一发明构思,本公开实施例中还提供了与点云的压缩方法对应的点云的压缩装置,由于本公开实施例中的装置解决问题的原理与本公开实施例上述点云的压缩方法相似,因此装置的实施可以参见方法的实施,重复之处不再赘述。Based on the same inventive concept, the embodiments of the present disclosure also provide a point cloud compression device corresponding to the point cloud compression method. Since the problem-solving principle of the device in the embodiments of the present disclosure is consistent with the above-mentioned point cloud compression method in the embodiments of the present disclosure, are similar, so the implementation of the device can refer to the implementation of the method, and repeated details will not be repeated.
请参阅图3,图3为本公开实施例提供的一种点云的压缩装置的示意图。如图3中所示, 本公开实施例提供的点云的压缩装置300包括:Please refer to FIG. 3 , which is a schematic diagram of a point cloud compression device provided by an embodiment of the present disclosure. As shown in Figure 3, a point cloud compression device 300 provided by an embodiment of the present disclosure includes:
划分模块310,用于获取待压缩点云,将所述待压缩点云划分为多个子点云块;The dividing module 310 is used to obtain the point cloud to be compressed and divide the point cloud to be compressed into multiple sub-point cloud blocks;
相关性确定模块320,用于针对每个所述子点云块,确定该所述子点云块对应的几何属性信息与颜色属性信息之间的相关系数;The correlation determination module 320 is configured to determine, for each sub-point cloud block, the correlation coefficient between the geometric attribute information and the color attribute information corresponding to the sub-point cloud block;
第一压缩模块330,用于针对所述相关系数大于预设的系数阈值的所述子点云块,根据所述几何属性信息确定该所述子点云块对应的距离加权图,并基于所述距离加权图压缩所述子点云块;The first compression module 330 is configured to determine the distance weighted map corresponding to the sub-point cloud block according to the geometric attribute information for the sub-point cloud block whose correlation coefficient is greater than the preset coefficient threshold, and based on the The distance-weighted graph compresses the sub-point cloud blocks;
纹理复杂度确定模块340,用于针对所述相关系数小于所述系数阈值的所述子点云块,计算该所述子点云块对应的纹理复杂度;The texture complexity determination module 340 is configured to calculate the texture complexity corresponding to the sub-point cloud block for which the correlation coefficient is less than the coefficient threshold;
第二压缩模块350,用于针对所述纹理复杂度大于预设的复杂度阈值的所述子点云块,确定该所述子点云块对应的相似度加权图,并基于所述相似度加权图压缩所述子点云块;The second compression module 350 is configured to determine, for the sub-point cloud block whose texture complexity is greater than a preset complexity threshold, a similarity weighted map corresponding to the sub-point cloud block, and based on the similarity Weighted graph compresses the sub-point cloud blocks;
第三压缩模块360,用于针对所述纹理复杂度小于所述复杂度阈值的所述子点云块,确定该所述子点云块对应的无权图,并基于所述无权图压缩所述子点云块。The third compression module 360 is configured to determine the unweighted map corresponding to the sub-point cloud block whose texture complexity is less than the complexity threshold, and compress the sub-point cloud block based on the unweighted map. The sub-point cloud block.
一种可选的实施方式中,所述相关性确定模块320具体用于:In an optional implementation, the correlation determination module 320 is specifically used to:
针对每个所述子点云块,获取该所述子点云块内,各个顶点间的信号差值产生的几何变化信息与颜色变化信息;For each sub-point cloud block, obtain geometric change information and color change information generated by signal differences between vertices in the sub-point cloud block;
将所述几何变化信息与所述颜色变化信息作为所述几何属性信息与所述颜色属性信息之间相关性的评价指标,获取多个所述几何变化信息对应的观测值以及多个所述颜色变化信息对应的观测值;Using the geometric change information and the color change information as evaluation indicators for the correlation between the geometric attribute information and the color attribute information, obtain a plurality of observation values corresponding to the geometric change information and a plurality of the colors Observed values corresponding to change information;
根据所述几何变化信息对应的观测值以及所述颜色变化信息对应的观测值,确定所述子点云块对应的皮尔森相关系数;According to the observation value corresponding to the geometric change information and the observation value corresponding to the color change information, determine the Pearson correlation coefficient corresponding to the sub-point cloud block;
将所述皮尔森相关系数确定为所述几何属性信息与所述颜色属性信息之间的相关系数。The Pearson correlation coefficient is determined as the correlation coefficient between the geometric attribute information and the color attribute information.
一种可选的实施方式中,所述相关性确定模块320还用于:基于以下公式确定所述子点云块对应的皮尔森相关系数:In an optional implementation, the correlation determination module 320 is further configured to determine the Pearson correlation coefficient corresponding to the sub-point cloud block based on the following formula:
其中,G为代表所述几何变化信息的参数、C为代表所述颜色变化信息的参数、μ
G代表所述几何变化信息的观测值对应的均值、σ
G代表所述几何变化信息的观测值对应的标准差、μ
C代表所述颜色变化信息的观测值对应的均值、σ
C代表所述颜色变化信息的观测值对应的标准差、M代表所述几何变化信息的观测值与所述颜色变化信息的观测值的数量、G
i所述几何变化信息的第i个观测值、C
i代表所述颜色变化信息的第i个观测值、ρ代表所述皮尔森相关系数。
Wherein, G is a parameter representing the geometric change information, C is a parameter representing the color change information, μ G represents the mean value corresponding to the observed value of the geometric change information, and σ G represents the observed value of the geometric change information. The corresponding standard deviation, μ C represents the mean value corresponding to the observed value of the color change information, σ C represents the standard deviation corresponding to the observed value of the color change information, M represents the difference between the observed value of the geometric change information and the color The number of observation values of the change information, G i is the i-th observation value of the geometric change information, C i represents the i-th observation value of the color change information, and ρ represents the Pearson correlation coefficient.
一种可选的实施方式中,所述第一压缩模块330具体用于:In an optional implementation, the first compression module 330 is specifically used to:
针对所述相关系数大于预设的系数阈值的所述子点云块,确定该所述子点云块中,每两个顶点之间的顶点距离;For the sub-point cloud block whose correlation coefficient is greater than a preset coefficient threshold, determine the vertex distance between every two vertices in the sub-point cloud block;
根据所述顶点距离以及预设的距离阈值,确定所述子点云块中顶点间的边权重,并根据所述边权重构建所述距离加权图;Determine edge weights between vertices in the sub-point cloud block based on the vertex distance and a preset distance threshold, and construct the distance-weighted graph based on the edge weights;
确定所述距离加权图对应的权重矩阵,根据所述权重矩阵确定所述子点云块对应的拉普拉斯矩阵;Determine the weight matrix corresponding to the distance weighted map, and determine the Laplacian matrix corresponding to the sub-point cloud block according to the weight matrix;
根据所述拉普拉斯矩阵压缩所述子点云块。The sub-point cloud blocks are compressed according to the Laplacian matrix.
一种可选的实施方式中,所述纹理复杂度确定模块340具体用于:In an optional implementation, the texture complexity determination module 340 is specifically used to:
针对所述相关系数小于所述系数阈值的所述子点云块,确定该所述子点云块对应的灰度共生矩阵;For the sub-point cloud block whose correlation coefficient is less than the coefficient threshold, determine the gray level co-occurrence matrix corresponding to the sub-point cloud block;
计算所述灰度共生矩阵对应的二次统计熵值,将所述二次统计熵值确定为所述纹理复杂度。Calculate the quadratic statistical entropy value corresponding to the gray level co-occurrence matrix, and determine the quadratic statistical entropy value as the texture complexity.
一种可选的实施方式中,所述第二压缩模块350具体用于:In an optional implementation, the second compression module 350 is specifically used to:
针对所述纹理复杂度大于预设的复杂度阈值的所述子点云块,确定该所述子点云块中,每两个顶点之间的顶点距离;For the sub-point cloud block whose texture complexity is greater than a preset complexity threshold, determine the vertex distance between every two vertices in the sub-point cloud block;
确定所述子点云块中每个顶点对应的颜色属性值,针对每个顶点,将该顶点对应的所述颜色属性值以及与其相邻的顶点对应的所述颜色属性值进行反距离加权计算,确定该顶点对应的颜色预测值;Determine the color attribute value corresponding to each vertex in the sub-point cloud block, and for each vertex, perform an inverse distance weighted calculation on the color attribute value corresponding to the vertex and the color attribute value corresponding to its adjacent vertex , determine the color prediction value corresponding to the vertex;
根据所述顶点距离以及每两个顶点之间所述颜色预测值的差值,确定所述子点云块中顶点间的边权重,并根据所述边权重构建所述相似度加权图,其中,所述相似度加权图用以通过引入颜色属性信息来反映所述子点云块中顶点间的纹理相似性;According to the vertex distance and the difference of the color prediction values between each two vertices, the edge weights between the vertices in the sub-point cloud block are determined, and the similarity weighted graph is constructed based on the edge weights, where , the similarity weighted map is used to reflect the texture similarity between vertices in the sub-point cloud block by introducing color attribute information;
确定所述相似度加权图对应的权重矩阵,根据所述权重矩阵确定所述子点云块对应的拉普拉斯矩阵;Determine the weight matrix corresponding to the similarity weighted map, and determine the Laplacian matrix corresponding to the sub-point cloud block according to the weight matrix;
根据所述拉普拉斯矩阵压缩所述子点云块。The sub-point cloud blocks are compressed according to the Laplacian matrix.
一种可选的实施方式中,所述第三压缩模块360具体用于:In an optional implementation, the third compression module 360 is specifically used to:
针对所述纹理复杂度小于所述复杂度阈值的所述子点云块,其中所述子点云块包括一个全局平滑块或多个局部平滑块;For the sub-point cloud block whose texture complexity is less than the complexity threshold, wherein the sub-point cloud block includes a global smooth block or a plurality of local smooth blocks;
针对所述全局平滑块,利用莫顿码编码所述全局平滑块,构造所述子点云块对应的线性图,用以描述所述全局平滑块的连通性;For the global smooth block, Morton code is used to encode the global smooth block, and a linear graph corresponding to the sub-point cloud block is constructed to describe the connectivity of the global smooth block;
针对所述局部平滑块,利用谱聚类算法,根据所述局部平滑块内顶点间的颜色差值进行聚类分析,构建所述子点云块对应的聚类连接图;For the local smooth block, use a spectral clustering algorithm to perform cluster analysis based on the color differences between vertices in the local smooth block, and construct a cluster connection graph corresponding to the sub-point cloud block;
确定所述聚类连接图对应的邻接矩阵,根据所述邻接矩阵确定所述子点云块对应的拉普拉斯矩阵;Determine the adjacency matrix corresponding to the cluster connection graph, and determine the Laplacian matrix corresponding to the sub-point cloud block according to the adjacency matrix;
根据所述拉普拉斯矩阵压缩所述子点云块。The sub-point cloud blocks are compressed according to the Laplacian matrix.
关于装置中的各模块的处理流程、以及各模块之间的交互流程的描述可以参照上述方法实施例中的相关说明,这里不再详述。For a description of the processing flow of each module in the device and the interaction flow between the modules, please refer to the relevant descriptions in the above method embodiments, and will not be described in detail here.
本公开实施例提供的一种点云的压缩装置,通过获取待压缩点云,将待压缩点云划分为多个子点云块;针对每个子点云块,确定该子点云块对应的几何属性信息与颜色属性信息之间的相关系数;针对相关系数大于预设的系数阈值的子点云块,根据几何属性信息确定该子点云块对应的距离加权图,并基于距离加权图压缩子点云块;针对相关系数小于系数阈值的子点云块,计算该子点云块对应的纹理复杂度;针对纹理复杂度大于预设的复杂度阈值的子点云块,确定该子点云块对应的相似度加权图,并基于相似度加权图压缩子点云块;针对纹理复杂度小于复杂度阈值的子点云块,确定该子点云块对应的无权图,并基于无权图压缩子点云块。可以充分考虑点云的几何属性、颜色属性、纹理信息之间的相关性,以实现较优的压缩性能。A point cloud compression device provided by an embodiment of the present disclosure divides the point cloud to be compressed into multiple sub-point cloud blocks by acquiring the point cloud to be compressed; for each sub-point cloud block, the geometry corresponding to the sub-point cloud block is determined. Correlation coefficient between attribute information and color attribute information; for sub-point cloud blocks whose correlation coefficient is greater than the preset coefficient threshold, determine the distance-weighted map corresponding to the sub-point cloud block based on the geometric attribute information, and compress the sub-point cloud block based on the distance-weighted map Point cloud blocks; for sub-point cloud blocks whose correlation coefficient is less than the coefficient threshold, calculate the texture complexity corresponding to the sub-point cloud block; for sub-point cloud blocks whose texture complexity is greater than the preset complexity threshold, determine the sub-point cloud block similarity weighted map corresponding to the block, and compress the sub-point cloud block based on the similarity weighted map; for sub-point cloud blocks whose texture complexity is less than the complexity threshold, determine the unweighted map corresponding to the sub-point cloud block, and compress the sub-point cloud block based on the unweighted Graph compressed sub-point cloud patches. The correlation between the geometric attributes, color attributes, and texture information of the point cloud can be fully considered to achieve better compression performance.
对应于图1中的点云的压缩方法,本公开实施例还提供了一种电子设备400,如图4所示,为本公开实施例提供的电子设备400结构示意图,包括:Corresponding to the point cloud compression method in Figure 1, an embodiment of the present disclosure also provides an electronic device 400. As shown in Figure 4, a schematic structural diagram of the electronic device 400 provided by an embodiment of the present disclosure includes:
处理器41、存储器42、和总线43;存储器42用于存储执行指令,包括内存421和外部存储器422;这里的内存421也称内存储器,用于暂时存放处理器41中的运算数据,以及与硬盘等外部存储器422交换的数据,处理器41通过内存421与外部存储器422进行数据交换,当所述电子设备400运行时,所述处理器41与所述存储器42之间通过总线43通信,使得所述处理器41执行图1中的点云的压缩方法的步骤。 Processor 41, memory 42, and bus 43; memory 42 is used to store execution instructions, including memory 421 and external memory 422; memory 421 here is also called internal memory, and is used to temporarily store operation data in processor 41, and with The processor 41 exchanges data with the external memory 422 such as a hard disk through the memory 421 and the external memory 422. When the electronic device 400 is running, the processor 41 and the memory 42 communicate through the bus 43, so that The processor 41 executes the steps of the point cloud compression method in FIG. 1 .
本公开实施例还提供一种计算机可读存储介质,该计算机可读存储介质上存储有计算机程序,该计算机程序被处理器运行时执行上述方法实施例中所述的点云的压缩方法的步 骤。其中,该存储介质可以是易失性或非易失的计算机可读取存储介质。Embodiments of the present disclosure also provide a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is run by a processor, the steps of the point cloud compression method described in the above method embodiments are executed. . Wherein, the storage medium may be a volatile or non-volatile computer-readable storage medium.
本公开实施例还提供一种计算机程序产品,该计算机程序产品包括有计算机指令,所述计算机指令被处理器执行时可以执行上述方法实施例中所述的点云的压缩方法的步骤,具体可参见上述方法实施例,在此不再赘述。An embodiment of the present disclosure also provides a computer program product. The computer program product includes computer instructions. When the computer instructions are executed by a processor, the steps of the point cloud compression method described in the above method embodiment can be performed. Specifically, Please refer to the above method embodiments, which will not be described again here.
其中,上述计算机程序产品可以具体通过硬件、软件或其结合的方式实现。在一个可选实施例中,所述计算机程序产品具体体现为计算机存储介质,在另一个可选实施例中,计算机程序产品具体体现为软件产品,例如软件开发包(Software Development Kit,SDK)等等。Among them, the above-mentioned computer program product can be specifically implemented by hardware, software or a combination thereof. In an optional embodiment, the computer program product is embodied as a computer storage medium. In another optional embodiment, the computer program product is embodied as a software product, such as a Software Development Kit (SDK), etc. wait.
所属领域的技术人员可以清楚地了解到,为描述的方便和简洁,上述描述的装置的具体工作过程,可以参考前述方法实施例中的对应过程,在此不再赘述。在本公开所提供的几个实施例中,应该理解到,所揭露的装置和方法,可以通过其它的方式实现。以上所描述的装置实施例仅仅是示意性的,例如,所述单元的划分,仅仅为一种逻辑功能划分,实际实现时可以有另外的划分方式,又例如,多个单元或组件可以结合或者可以集成到另一个系统,或一些特征可以忽略,或不执行。另一点,所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些通信接口,装置或单元的间接耦合或通信连接,可以是电性,机械或其它的形式。Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiment, and will not be described again here. In the several embodiments provided in this disclosure, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or can be integrated into another system, or some features can be ignored, or not implemented. On the other hand, the coupling or direct coupling or communication connection between each other shown or discussed may be through some communication interfaces, and the indirect coupling or communication connection of the devices or units may be in electrical, mechanical or other forms.
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的,作为单元显示的部件可以是或者也可以不是物理单元,即可以位于一个地方,或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
另外,在本公开各个实施例中的各功能单元可以集成在一个处理单元中,也可以是各个单元单独物理存在,也可以两个或两个以上单元集成在一个单元中。In addition, each functional unit in various embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
所述功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时,可以存储在一个处理器可执行的非易失的计算机可读取存储介质中。基于这样的理解,本公开的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来,该计算机软件产品存储在一个存储介质中,包括若干指令用以使得一台计算机设备(可以是个人计算机,服务器,或者网络设备等)执行本公开各个实施例所述方法的全部或部分步骤。而前述的存储介质包括:U盘、移动硬盘、只读存储器(Read-Only Memory,ROM)、随机存取存储器(Random Access Memory,RAM)、磁碟或者光盘等各种可以存储程序代码的介质。If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present disclosure is essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present disclosure. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program code. .
最后应说明的是:以上所述实施例,仅为本公开的具体实施方式,用以说明本公开的技术方案,而非对其限制,本公开的保护范围并不局限于此,尽管参照前述实施例对本公开进行了详细的说明,本领域的普通技术人员应当理解:任何熟悉本技术领域的技术人员在本公开揭露的技术范围内,其依然可以对前述实施例所记载的技术方案进行修改或可轻易想到变化,或者对其中部分技术特征进行等同替换;而这些修改、变化或者替换,并不使相应技术方案的本质脱离本公开实施例技术方案的精神和范围,都应涵盖在本公开的保护范围之内。因此,本公开的保护范围应所述以权利要求的保护范围为准。Finally, it should be noted that the above-mentioned embodiments are only specific implementations of the present disclosure and are used to illustrate the technical solutions of the present disclosure rather than to limit them. The protection scope of the present disclosure is not limited thereto. Although refer to the foregoing The embodiments describe the present disclosure in detail. Those of ordinary skill in the art should understand that any person familiar with the technical field can still modify the technical solutions recorded in the foregoing embodiments within the technical scope disclosed in the present disclosure. Changes may be easily imagined, or equivalent substitutions may be made to some of the technical features; and these modifications, changes or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and shall be included in the present disclosure. within the scope of protection. Therefore, the protection scope of the present disclosure should be determined by the protection scope of the claims.
本公开实施例提供的一种点云的压缩方法、装置、电子设备及存储介质,通过获取待压缩点云,将待压缩点云划分为多个子点云块;针对每个子点云块,确定该子点云块对应的几何属性信息与颜色属性信息之间的相关系数;针对相关系数大于预设的系数阈值的子点云块,根据几何属性信息确定该子点云块对应的距离加权图,并基于距离加权图压缩子点云块;针对相关系数小于系数阈值的子点云块,计算该子点云块对应的纹理复杂度;针对纹理复杂度大于预设的复杂度阈值的子点云块,确定该子点云块对应的相似度加权图,并基于相似度加权图压缩子点云块;针对纹理复杂度小于复杂度阈值的子点云块,确定该子点云块对应的无权图,并基于无权图压缩子点云块。可以充分考虑点云的几何属性、颜色属性、纹理信息之间的相关性,并具有较优的压缩性能。Embodiments of the present disclosure provide a point cloud compression method, device, electronic device, and storage medium. By obtaining the point cloud to be compressed, the point cloud to be compressed is divided into multiple sub-point cloud blocks; for each sub-point cloud block, determine The correlation coefficient between the geometric attribute information and the color attribute information corresponding to the sub-point cloud block; for the sub-point cloud block whose correlation coefficient is greater than the preset coefficient threshold, the distance weighted map corresponding to the sub-point cloud block is determined based on the geometric attribute information , and compress the sub-point cloud blocks based on the distance weighted map; for sub-point cloud blocks whose correlation coefficient is less than the coefficient threshold, calculate the texture complexity corresponding to the sub-point cloud block; for sub-points whose texture complexity is greater than the preset complexity threshold Cloud block, determine the similarity weighted map corresponding to the sub-point cloud block, and compress the sub-point cloud block based on the similarity weighted map; for sub-point cloud blocks whose texture complexity is less than the complexity threshold, determine the corresponding sub-point cloud block Unweighted graph, and compressed sub-point cloud patches based on the unweighted graph. The correlation between the geometric attributes, color attributes, and texture information of the point cloud can be fully considered, and it has better compression performance.
此外,可以理解的是,本申请的点云的压缩方法、装置、电子设备及存储介质是可以重现的,并且可以用在多种工业应用中。例如,本申请的点云的压缩方法、装置、电子设备及存储介质可以用于点云数据处理技术领域。In addition, it can be understood that the point cloud compression method, device, electronic device and storage medium of the present application are reproducible and can be used in a variety of industrial applications. For example, the point cloud compression method, device, electronic device and storage medium of this application can be used in the technical field of point cloud data processing.
Claims (20)
- 一种点云的压缩方法,其特征在于,包括:A point cloud compression method, characterized by including:获取待压缩点云,将所述待压缩点云划分为多个子点云块;Obtain the point cloud to be compressed and divide the point cloud to be compressed into multiple sub-point cloud blocks;针对每个所述子点云块,确定该所述子点云块对应的几何属性信息与颜色属性信息之间的相关系数;For each sub-point cloud block, determine the correlation coefficient between the geometric attribute information and the color attribute information corresponding to the sub-point cloud block;针对所述相关系数大于预设的系数阈值的所述子点云块,根据所述几何属性信息确定该所述子点云块对应的距离加权图,并基于所述距离加权图压缩所述子点云块;For the sub-point cloud block whose correlation coefficient is greater than a preset coefficient threshold, determine the distance weighted map corresponding to the sub-point cloud block based on the geometric attribute information, and compress the sub-point cloud block based on the distance weighted map. point cloud block;针对所述相关系数小于所述系数阈值的所述子点云块,计算该所述子点云块对应的纹理复杂度;For the sub-point cloud block whose correlation coefficient is less than the coefficient threshold, calculate the texture complexity corresponding to the sub-point cloud block;针对所述纹理复杂度大于预设的复杂度阈值的所述子点云块,确定该所述子点云块对应的相似度加权图,并基于所述相似度加权图压缩所述子点云块;For the sub-point cloud block whose texture complexity is greater than a preset complexity threshold, determine the similarity weighted map corresponding to the sub-point cloud block, and compress the sub-point cloud based on the similarity weighted map piece;针对所述纹理复杂度小于所述复杂度阈值的所述子点云块,确定该所述子点云块对应的无权图,并基于所述无权图压缩所述子点云块。For the sub-point cloud block whose texture complexity is less than the complexity threshold, an unweighted map corresponding to the sub-point cloud block is determined, and the sub-point cloud block is compressed based on the unweighted map.
- 根据权利要求1所述的方法,其特征在于,所述针对每个所述子点云块,确定该所述子点云块对应的几何属性信息与颜色属性信息之间的相关系数,包括:The method according to claim 1, characterized in that, for each sub-point cloud block, determining the correlation coefficient between the geometric attribute information and the color attribute information corresponding to the sub-point cloud block includes:针对每个所述子点云块,获取该所述子点云块内,各个顶点间的信号差值产生的几何变化信息与颜色变化信息;For each sub-point cloud block, obtain geometric change information and color change information generated by signal differences between vertices in the sub-point cloud block;将所述几何变化信息与所述颜色变化信息作为所述几何属性信息与所述颜色属性信息之间相关性的评价指标,获取多个所述几何变化信息对应的观测值以及多个所述颜色变化信息对应的观测值;Using the geometric change information and the color change information as evaluation indicators for the correlation between the geometric attribute information and the color attribute information, obtain a plurality of observation values corresponding to the geometric change information and a plurality of the colors Observed values corresponding to change information;根据所述几何变化信息对应的观测值以及所述颜色变化信息对应的观测值,确定所述子点云块对应的皮尔森相关系数;According to the observation value corresponding to the geometric change information and the observation value corresponding to the color change information, determine the Pearson correlation coefficient corresponding to the sub-point cloud block;将所述皮尔森相关系数确定为所述几何属性信息与所述颜色属性信息之间的相关系数。The Pearson correlation coefficient is determined as the correlation coefficient between the geometric attribute information and the color attribute information.
- 根据权利要求2所述的方法,其特征在于,基于以下公式确定所述子点云块对应的皮尔森相关系数:The method according to claim 2, characterized in that the Pearson correlation coefficient corresponding to the sub-point cloud block is determined based on the following formula:其中,G为代表所述几何变化信息的参数、C为代表所述颜色变化信息的参数、μ G代表所述几何变化信息的观测值对应的均值、σ G代表所述几何变化信息的观测值对应的标准差、μ C代表所述颜色变化信息的观测值对应的均值、σ C代表所述颜色变化信息的观测值对应的标准差、M代表所述几何变化信息的观测值与所述颜色变化信息的观测值的数量、G i所述几何变化信息的第i个观测值、C i代表所述颜色变化信息的第i个观测值、ρ代表所述皮尔森相关系数。 Wherein, G is a parameter representing the geometric change information, C is a parameter representing the color change information, μ G represents the mean value corresponding to the observed value of the geometric change information, and σ G represents the observed value of the geometric change information. The corresponding standard deviation, μ C represents the mean value corresponding to the observed value of the color change information, σ C represents the standard deviation corresponding to the observed value of the color change information, M represents the difference between the observed value of the geometric change information and the color The number of observation values of the change information, G i is the i-th observation value of the geometric change information, C i represents the i-th observation value of the color change information, and ρ represents the Pearson correlation coefficient.
- 根据权利要求1至3中任一项所述的方法,其特征在于,所述针对所述相关系数大于预设的系数阈值的所述子点云块,根据所述几何属性信息确定该所述子点云块对应的距离加权图,并基于所述距离加权图压缩所述子点云块,包括:The method according to any one of claims 1 to 3, characterized in that, for the sub-point cloud block whose correlation coefficient is greater than a preset coefficient threshold, the said sub-point cloud block is determined according to the geometric attribute information. The distance weighted map corresponding to the sub-point cloud block, and compressing the sub-point cloud block based on the distance weighted map, including:针对所述相关系数大于预设的系数阈值的所述子点云块,确定该所述子点云块中,每两个顶点之间的顶点距离;For the sub-point cloud block whose correlation coefficient is greater than a preset coefficient threshold, determine the vertex distance between every two vertices in the sub-point cloud block;根据所述顶点距离以及预设的距离阈值,确定所述子点云块中顶点间的边权重,并根据所述边权重构建所述距离加权图;Determine edge weights between vertices in the sub-point cloud block based on the vertex distance and a preset distance threshold, and construct the distance-weighted graph based on the edge weights;确定所述距离加权图对应的权重矩阵,根据所述权重矩阵确定所述子点云块对应的拉普拉斯矩阵;Determine the weight matrix corresponding to the distance weighted map, and determine the Laplacian matrix corresponding to the sub-point cloud block according to the weight matrix;根据所述拉普拉斯矩阵压缩所述子点云块。The sub-point cloud blocks are compressed according to the Laplacian matrix.
- 根据权利要求1至4中任一项所述的方法,其特征在于,使用基于阈值的高斯核函数来定义所述距离加权图中的权重,所述基于阈值的高斯核函数如下公式所示:The method according to any one of claims 1 to 4, characterized in that a threshold-based Gaussian kernel function is used to define weights in the distance weighted graph, and the threshold-based Gaussian kernel function is as shown in the following formula:其中,d i,j代表所述子点云块中,顶点i与顶点j之间的欧氏距离;δ代表所述子点云块中,顶点i与顶点j之间欧氏距离的平均绝对值偏差;τ代表预设的欧氏距离的阈值。 Among them, d i, j represents the Euclidean distance between vertex i and vertex j in the sub-point cloud block; δ represents the average absolute Euclidean distance between vertex i and vertex j in the sub-point cloud block. value deviation; τ represents the preset Euclidean distance threshold.
- 根据权利要求1至5中任一项所述的方法,其特征在于,所述针对所述相关系数小于所述系数阈值的所述子点云块,计算该所述子点云块对应的纹理复杂度,包括:The method according to any one of claims 1 to 5, characterized in that, for the sub-point cloud block whose correlation coefficient is less than the coefficient threshold, the texture corresponding to the sub-point cloud block is calculated. Complexity, including:针对所述相关系数小于所述系数阈值的所述子点云块,确定该所述子点云块对应的灰度共生矩阵;For the sub-point cloud block whose correlation coefficient is less than the coefficient threshold, determine the gray level co-occurrence matrix corresponding to the sub-point cloud block;计算所述灰度共生矩阵对应的二次统计熵值,将所述二次统计熵值确定为所述纹理复杂度。Calculate the quadratic statistical entropy value corresponding to the gray level co-occurrence matrix, and determine the quadratic statistical entropy value as the texture complexity.
- 根据权利要求6所述的方法,其特征在于,使用对于所述灰度共生矩阵的二次统计熵值来评估所述子点云块的所述纹理复杂度,计算所述二次统计熵值的公式如下所示:The method of claim 6, wherein a quadratic statistical entropy value for the gray level co-occurrence matrix is used to evaluate the texture complexity of the sub-point cloud block, and the quadratic statistical entropy value is calculated The formula for is as follows:其中,Entropy代表所述灰度共生矩阵对应的二次统计熵;L代表预设的灰度级别;GLGM i,j代表所述灰度共生矩阵。 Wherein, Entropy represents the secondary statistical entropy corresponding to the gray level co-occurrence matrix; L represents the preset gray level; GLGM i, j represents the gray level co-occurrence matrix.
- 根据权利要求1至7中任一项所述的方法,其特征在于,所述针对所述纹理复杂度大于预设的复杂度阈值的所述子点云块,确定该所述子点云块对应的相似度加权图,并基于所述相似度加权图压缩所述子点云块,包括:The method according to any one of claims 1 to 7, characterized in that, for the sub-point cloud block whose texture complexity is greater than a preset complexity threshold, determining the sub-point cloud block Corresponding similarity weighted map, and compressing the sub-point cloud blocks based on the similarity weighted map, including:针对所述纹理复杂度大于预设的复杂度阈值的所述子点云块,确定该所述子点云块中,每两个顶点之间的顶点距离;For the sub-point cloud block whose texture complexity is greater than a preset complexity threshold, determine the vertex distance between every two vertices in the sub-point cloud block;确定所述子点云块中每个顶点对应的颜色属性值,针对每个顶点,将该顶点对应的所述颜色属性值以及与其相邻的顶点对应的所述颜色属性值进行反距离加权计算,确定该顶点对应的颜色预测值;Determine the color attribute value corresponding to each vertex in the sub-point cloud block, and for each vertex, perform an inverse distance weighted calculation on the color attribute value corresponding to the vertex and the color attribute value corresponding to its adjacent vertex , determine the color prediction value corresponding to the vertex;根据所述顶点距离以及每两个顶点之间所述颜色预测值的差值,确定所述子点云块中顶点间的边权重,并根据所述边权重构建所述相似度加权图,其中,所述相似度加权图用以通过引入颜色属性信息来反映所述子点云块中顶点间的纹理相似性;According to the vertex distance and the difference of the color prediction values between each two vertices, the edge weights between the vertices in the sub-point cloud block are determined, and the similarity weighted graph is constructed based on the edge weights, where , the similarity weighted map is used to reflect the texture similarity between vertices in the sub-point cloud block by introducing color attribute information;确定所述相似度加权图对应的权重矩阵,根据所述权重矩阵确定所述子点云块对应的拉普拉斯矩阵;Determine the weight matrix corresponding to the similarity weighted map, and determine the Laplacian matrix corresponding to the sub-point cloud block according to the weight matrix;根据所述拉普拉斯矩阵压缩所述子点云块。The sub-point cloud blocks are compressed according to the Laplacian matrix.
- 根据权利要求8所述的方法,其特征在于,基于以下公式定义所述相似度加权图中的所述边权重:The method according to claim 8, characterized in that the edge weight in the similarity weighted graph is defined based on the following formula:其中,d i,j代表所述子点云块中,顶点i与顶点j之间的欧氏距离;δ代表所述子点云块中,顶点i与顶点j之间欧氏距离的平均绝对值偏差;τ代表预设的欧氏距离的阈值;p i,j是代表所述子点云块中,顶点i与顶点j之间颜色预测值之间的差值;θ代表所述子点云块中,顶点i与顶点j之间颜色预测值的差值之间的平均绝对值偏差;ψ代表所述子点云块中,顶点i与顶点j之间颜色预测值的差值对应的预设阈值。 Among them, d i, j represents the Euclidean distance between vertex i and vertex j in the sub-point cloud block; δ represents the average absolute Euclidean distance between vertex i and vertex j in the sub-point cloud block. value deviation; τ represents the preset Euclidean distance threshold; p i,j represents the difference between the color prediction values between vertex i and vertex j in the sub-point cloud block; θ represents the sub-point In the cloud block, the average absolute value deviation between the difference in color prediction value between vertex i and vertex j; ψ represents the difference in color prediction value between vertex i and vertex j in the sub-point cloud block corresponding to Preset threshold.
- 根据权利要求8或9所述的方法,其特征在于,所述子点云块中顶点的颜色预测值通过所述子点云块中相邻顶点颜色属性值的反距离加权获得,所述子点云块中顶点的颜色预测值通过下述公式获得:The method according to claim 8 or 9, characterized in that the color prediction value of the vertex in the sub-point cloud block is obtained by inverse distance weighting of the color attribute values of adjacent vertices in the sub-point cloud block, and the sub-point cloud block The color prediction value of the vertices in the point cloud block is obtained by the following formula:其中,p i代表顶点i对应的颜色预测值;r j代表顶点j对应的颜色重建值,顶点j为与顶点i相邻的已编码节点;d i,j代表顶点i与顶点j之间的欧氏距离。 Among them, p i represents the color prediction value corresponding to vertex i; r j represents the color reconstruction value corresponding to vertex j, and vertex j is the encoded node adjacent to vertex i; d i,j represents the distance between vertex i and vertex j Euclidean distance.
- 根据权利要求1至10中任一项所述的方法,其特征在于,所述针对所述纹理复杂度小于所述复杂度阈值的所述子点云块,确定该所述子点云块对应的无权图,并基于所述无权图压缩所述子点云块,包括:The method according to any one of claims 1 to 10, characterized in that, for the sub-point cloud block whose texture complexity is less than the complexity threshold, it is determined that the sub-point cloud block corresponds to an unweighted graph, and compressing the sub-point cloud blocks based on the unweighted graph, including:针对所述纹理复杂度小于所述复杂度阈值的所述子点云块,其中所述子点云块包括一个全局平滑块或多个局部平滑块;For the sub-point cloud block whose texture complexity is less than the complexity threshold, wherein the sub-point cloud block includes a global smooth block or a plurality of local smooth blocks;针对所述全局平滑块,利用莫顿码编码所述全局平滑块,构造所述子点云块对应的线性图,用以描述所述全局平滑块的连通性;For the global smooth block, Morton code is used to encode the global smooth block, and a linear graph corresponding to the sub-point cloud block is constructed to describe the connectivity of the global smooth block;针对所述局部平滑块,利用谱聚类算法,根据所述局部平滑块内顶点间的颜色差值进行聚类分析,构建所述子点云块对应的聚类连接图;For the local smooth block, use a spectral clustering algorithm to perform cluster analysis based on the color differences between vertices in the local smooth block, and construct a cluster connection graph corresponding to the sub-point cloud block;确定所述聚类连接图对应的邻接矩阵,根据所述邻接矩阵确定所述子点云块对应的拉普拉斯矩阵;Determine the adjacency matrix corresponding to the cluster connection graph, and determine the Laplacian matrix corresponding to the sub-point cloud block according to the adjacency matrix;根据所述拉普拉斯矩阵压缩所述子点云块。The sub-point cloud blocks are compressed according to the Laplacian matrix.
- 一种点云的压缩装置,其特征在于,包括:A point cloud compression device, characterized by including:划分模块,被配置成用于获取待压缩点云,将所述待压缩点云划分为多个子点云块;A dividing module configured to obtain a point cloud to be compressed and divide the point cloud to be compressed into a plurality of sub-point cloud blocks;相关性确定模块,被配置成用于针对每个所述子点云块,确定该所述子点云块对应的几何属性信息与颜色属性信息之间的相关系数;A correlation determination module configured to determine, for each sub-point cloud block, a correlation coefficient between the geometric attribute information and the color attribute information corresponding to the sub-point cloud block;第一压缩模块,被配置成用于针对所述相关系数大于预设的系数阈值的所述子点云块,根据所述几何属性信息确定该所述子点云块对应的距离加权图,并基于所述距离加权图压缩所述子点云块;A first compression module configured to determine a distance weighted map corresponding to the sub-point cloud block based on the geometric attribute information for the sub-point cloud block whose correlation coefficient is greater than a preset coefficient threshold, and Compress the sub-point cloud blocks based on the distance-weighted map;纹理复杂度确定模块,被配置成用于针对所述相关系数小于所述系数阈值的所述子点云块,计算该所述子点云块对应的纹理复杂度;A texture complexity determination module configured to calculate, for the sub-point cloud block whose correlation coefficient is less than the coefficient threshold, the texture complexity corresponding to the sub-point cloud block;第二压缩模块,被配置成用于针对所述纹理复杂度大于预设的复杂度阈值的所述子点云块,确定该所述子点云块对应的相似度加权图,并基于所述相似度加权图压缩所述子点云块;The second compression module is configured to determine, for the sub-point cloud block whose texture complexity is greater than a preset complexity threshold, a similarity weighted map corresponding to the sub-point cloud block, and based on the The similarity weighted graph compresses the sub-point cloud blocks;第三压缩模块,被配置成用于针对所述纹理复杂度小于所述复杂度阈值的所述子点云 块,确定该所述子点云块对应的无权图,并基于所述无权图压缩所述子点云块。A third compression module configured to determine, for the sub-point cloud block whose texture complexity is less than the complexity threshold, an unweighted map corresponding to the sub-point cloud block, and based on the unweighted Figure compresses the sub-point cloud blocks.
- 根据权利要求12所述的装置,其特征在于,所述相关性确定模块被配置成用于:The apparatus of claim 12, wherein the correlation determination module is configured to:针对每个所述子点云块,获取该所述子点云块内,各个顶点间的信号差值产生的几何变化信息与颜色变化信息;For each sub-point cloud block, obtain geometric change information and color change information generated by signal differences between vertices in the sub-point cloud block;将所述几何变化信息与所述颜色变化信息作为所述几何属性信息与所述颜色属性信息之间相关性的评价指标,获取多个所述几何变化信息对应的观测值以及多个所述颜色变化信息对应的观测值;Using the geometric change information and the color change information as evaluation indicators for the correlation between the geometric attribute information and the color attribute information, obtain a plurality of observation values corresponding to the geometric change information and a plurality of the colors Observed values corresponding to change information;根据所述几何变化信息对应的观测值以及所述颜色变化信息对应的观测值,确定所述子点云块对应的皮尔森相关系数;According to the observation value corresponding to the geometric change information and the observation value corresponding to the color change information, determine the Pearson correlation coefficient corresponding to the sub-point cloud block;将所述皮尔森相关系数确定为所述几何属性信息与所述颜色属性信息之间的相关系数。The Pearson correlation coefficient is determined as the correlation coefficient between the geometric attribute information and the color attribute information.
- 根据权利要求13所述的装置,其特征在于,所述相关性确定模块还被配置成用于:The apparatus of claim 13, wherein the correlation determination module is further configured to:基于以下公式确定所述子点云块对应的皮尔森相关系数:The Pearson correlation coefficient corresponding to the sub-point cloud block is determined based on the following formula:其中,G为代表所述几何变化信息的参数、C为代表所述颜色变化信息的参数、μ G代表所述几何变化信息的观测值对应的均值、σ G代表所述几何变化信息的观测值对应的标准差、μ C代表所述颜色变化信息的观测值对应的均值、σ C代表所述颜色变化信息的观测值对应的标准差、M代表所述几何变化信息的观测值与所述颜色变化信息的观测值的数量、G i所述几何变化信息的第i个观测值、C i代表所述颜色变化信息的第i个观测值、ρ代表所述皮尔森相关系数。 Wherein, G is a parameter representing the geometric change information, C is a parameter representing the color change information, μ G represents the mean value corresponding to the observed value of the geometric change information, and σ G represents the observed value of the geometric change information. The corresponding standard deviation, μ C represents the mean value corresponding to the observed value of the color change information, σ C represents the standard deviation corresponding to the observed value of the color change information, M represents the difference between the observed value of the geometric change information and the color The number of observation values of the change information, G i is the i-th observation value of the geometric change information, C i represents the i-th observation value of the color change information, and ρ represents the Pearson correlation coefficient.
- 根据权利要求12至14中任一项所述的装置,其特征在于,所述第一压缩模块具体被配置成用于:The device according to any one of claims 12 to 14, characterized in that the first compression module is specifically configured to:针对所述相关系数大于预设的系数阈值的所述子点云块,确定该所述子点云块中,每两个顶点之间的顶点距离;For the sub-point cloud block whose correlation coefficient is greater than a preset coefficient threshold, determine the vertex distance between every two vertices in the sub-point cloud block;根据所述顶点距离以及预设的距离阈值,确定所述子点云块中顶点间的边权重,并根据所述边权重构建所述距离加权图;Determine edge weights between vertices in the sub-point cloud block based on the vertex distance and a preset distance threshold, and construct the distance-weighted graph based on the edge weights;确定所述距离加权图对应的权重矩阵,根据所述权重矩阵确定所述子点云块对应的拉普拉斯矩阵;Determine the weight matrix corresponding to the distance weighted map, and determine the Laplacian matrix corresponding to the sub-point cloud block according to the weight matrix;根据所述拉普拉斯矩阵压缩所述子点云块。The sub-point cloud blocks are compressed according to the Laplacian matrix.
- 根据权利要求12至15中任一项所述的装置,其特征在于,所述纹理复杂度确定模块被配置成用于:The device according to any one of claims 12 to 15, characterized in that the texture complexity determination module is configured for:针对所述相关系数小于所述系数阈值的所述子点云块,确定该所述子点云块对应的灰度共生矩阵;For the sub-point cloud block whose correlation coefficient is less than the coefficient threshold, determine the gray level co-occurrence matrix corresponding to the sub-point cloud block;计算所述灰度共生矩阵对应的二次统计熵值,将所述二次统计熵值确定为所述纹理复杂度。Calculate the quadratic statistical entropy value corresponding to the gray level co-occurrence matrix, and determine the quadratic statistical entropy value as the texture complexity.
- 根据权利要求12至16中任一项所述的装置,其特征在于,所述第二压缩模块被配置成用于:The device according to any one of claims 12 to 16, characterized in that the second compression module is configured for:针对所述纹理复杂度大于预设的复杂度阈值的所述子点云块,确定该所述子点云块中,每两个顶点之间的顶点距离;For the sub-point cloud block whose texture complexity is greater than a preset complexity threshold, determine the vertex distance between every two vertices in the sub-point cloud block;确定所述子点云块中每个顶点对应的颜色属性值,针对每个顶点,将该顶点对应的所述颜色属性值以及与其相邻的顶点对应的所述颜色属性值进行反距离加权计算,确定该顶 点对应的颜色预测值;Determine the color attribute value corresponding to each vertex in the sub-point cloud block, and for each vertex, perform an inverse distance weighted calculation on the color attribute value corresponding to the vertex and the color attribute value corresponding to its adjacent vertex , determine the color prediction value corresponding to the vertex;根据所述顶点距离以及每两个顶点之间所述颜色预测值的差值,确定所述子点云块中顶点间的边权重,并根据所述边权重构建所述相似度加权图,其中,所述相似度加权图用以通过引入颜色属性信息来反映所述子点云块中顶点间的纹理相似性;According to the vertex distance and the difference of the color prediction values between each two vertices, the edge weights between the vertices in the sub-point cloud block are determined, and the similarity weighted graph is constructed based on the edge weights, where , the similarity weighted map is used to reflect the texture similarity between vertices in the sub-point cloud block by introducing color attribute information;确定所述相似度加权图对应的权重矩阵,根据所述权重矩阵确定所述子点云块对应的拉普拉斯矩阵;Determine the weight matrix corresponding to the similarity weighted map, and determine the Laplacian matrix corresponding to the sub-point cloud block according to the weight matrix;根据所述拉普拉斯矩阵压缩所述子点云块。The sub-point cloud blocks are compressed according to the Laplacian matrix.
- 根据权利要求12至17中任一项所述的装置,其特征在于,所述第三压缩模块被配置成用于:The device according to any one of claims 12 to 17, characterized in that the third compression module is configured for:针对所述纹理复杂度小于所述复杂度阈值的所述子点云块,其中所述子点云块包括一个全局平滑块或多个局部平滑块;For the sub-point cloud block whose texture complexity is less than the complexity threshold, wherein the sub-point cloud block includes a global smooth block or a plurality of local smooth blocks;针对所述全局平滑块,利用莫顿码编码所述全局平滑块,构造所述子点云块对应的线性图,用以描述所述全局平滑块的连通性;For the global smooth block, Morton code is used to encode the global smooth block, and a linear graph corresponding to the sub-point cloud block is constructed to describe the connectivity of the global smooth block;针对所述局部平滑块,利用谱聚类算法,根据所述局部平滑块内顶点间的颜色差值进行聚类分析,构建所述子点云块对应的聚类连接图;For the local smooth block, use a spectral clustering algorithm to perform cluster analysis based on the color differences between vertices in the local smooth block, and construct a cluster connection graph corresponding to the sub-point cloud block;确定所述聚类连接图对应的邻接矩阵,根据所述邻接矩阵确定所述子点云块对应的拉普拉斯矩阵;Determine the adjacency matrix corresponding to the cluster connection graph, and determine the Laplacian matrix corresponding to the sub-point cloud block according to the adjacency matrix;根据所述拉普拉斯矩阵压缩所述子点云块。The sub-point cloud blocks are compressed according to the Laplacian matrix.
- 一种电子设备,其特征在于,包括:处理器、存储器和总线,所述存储器存储有所述处理器可执行的机器可读指令,当电子设备运行时,所述处理器与所述存储器之间通过总线通信,所述机器可读指令被所述处理器执行时执行根据权利要求1至11中任一项所述的点云的压缩方法的步骤。An electronic device, characterized in that it includes: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the connection between the processor and the memory is The machine-readable instructions are communicated through a bus, and when the machine-readable instructions are executed by the processor, the steps of the point cloud compression method according to any one of claims 1 to 11 are performed.
- 一种计算机可读存储介质,其特征在于,所述计算机可读存储介质上存储有计算机程序,该计算机程序被处理器运行时执行根据权利要求1至11中任一项所述的点云的压缩方法的步骤。A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and the computer program executes the point cloud according to any one of claims 1 to 11 when run by a processor. Compression method steps.
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Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20210090301A1 (en) * | 2019-09-24 | 2021-03-25 | Apple Inc. | Three-Dimensional Mesh Compression Using a Video Encoder |
CN112637594A (en) * | 2020-12-23 | 2021-04-09 | 青岛大学 | Reference-free 3D point cloud quality evaluation method based on bit stream |
CN113096200A (en) * | 2021-03-24 | 2021-07-09 | 中山大学 | Point cloud attribute compression method, device, equipment and medium based on normal vector |
CN113096198A (en) * | 2021-03-01 | 2021-07-09 | 中山大学 | Bidirectional point cloud attribute prediction compression method, device, equipment and medium |
CN114782564A (en) * | 2022-06-21 | 2022-07-22 | 北京大学深圳研究生院 | Point cloud compression method and device, electronic equipment and storage medium |
Family Cites Families (6)
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CN107403456B (en) * | 2017-07-28 | 2019-06-18 | 北京大学深圳研究生院 | A kind of point cloud genera compression method based on KD tree and optimization figure transformation |
US10699444B2 (en) * | 2017-11-22 | 2020-06-30 | Apple Inc | Point cloud occupancy map compression |
US10867414B2 (en) * | 2018-04-10 | 2020-12-15 | Apple Inc. | Point cloud attribute transfer algorithm |
CN109166160B (en) * | 2018-09-17 | 2022-07-01 | 华侨大学 | Three-dimensional point cloud compression method adopting graph prediction |
CN113766229B (en) * | 2021-09-30 | 2023-04-28 | 咪咕文化科技有限公司 | Encoding method, decoding method, device, equipment and readable storage medium |
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Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20210090301A1 (en) * | 2019-09-24 | 2021-03-25 | Apple Inc. | Three-Dimensional Mesh Compression Using a Video Encoder |
CN112637594A (en) * | 2020-12-23 | 2021-04-09 | 青岛大学 | Reference-free 3D point cloud quality evaluation method based on bit stream |
CN113096198A (en) * | 2021-03-01 | 2021-07-09 | 中山大学 | Bidirectional point cloud attribute prediction compression method, device, equipment and medium |
CN113096200A (en) * | 2021-03-24 | 2021-07-09 | 中山大学 | Point cloud attribute compression method, device, equipment and medium based on normal vector |
CN114782564A (en) * | 2022-06-21 | 2022-07-22 | 北京大学深圳研究生院 | Point cloud compression method and device, electronic equipment and storage medium |
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