WO2023245982A1 - Point cloud compression method and apparatus, electronic device, and storage medium - Google Patents

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

A point cloud compression method, device, electronic equipment and storage medium

Cross-references to related applications

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.

Technical field

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.

Background technique

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.

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.

Contents of the invention

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:

Obtain the point cloud to be compressed and divide the point cloud to be compressed 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 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;

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;

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;

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.

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:

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;

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;

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;

The Pearson correlation coefficient is determined as the correlation coefficient between the geometric attribute information and the color attribute information.

In an optional implementation, the Pearson correlation coefficient corresponding to the sub-point cloud block can be determined based on the following formula:

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.

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:

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;

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;

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;

The sub-point cloud blocks are compressed according to the Laplacian matrix.

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:

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.

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:

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;

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.

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:

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.

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:

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;

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;

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;

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;

The sub-point cloud blocks are compressed according to the Laplacian matrix.

In an optional implementation, the edge weight in the similarity-weighted graph can be defined based on the following formula:

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.

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:

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.

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:

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;

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;

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;

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;

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.

In an optional implementation, the correlation determination module may be specifically configured to:

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;

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;

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;

The Pearson correlation coefficient is determined as the correlation coefficient between the geometric attribute information and the color attribute information.

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:

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.

In an optional implementation, the first compression module may be specifically configured to:

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;

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;

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;

The sub-point cloud blocks are compressed according to the Laplacian matrix.

In an optional implementation, the texture complexity determination module may be specifically configured to:

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;

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.

In an optional implementation, the second compression module may be specifically configured to:

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;

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;

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;

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;

The sub-point cloud blocks are compressed according to the Laplacian matrix.

In an optional implementation, the third compression module may be specifically configured to:

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;

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;

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;

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;

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. 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. .

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.

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.

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.

Description of the drawings

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.

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.

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.

Detailed ways

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.

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.

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.

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.

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.

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.

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. Obtain a point cloud to be compressed and divide the point cloud to be compressed into multiple sub-point cloud blocks.

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.

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. 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.

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.

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.

As a possible implementation, the Pearson correlation coefficient corresponding to the sub-point cloud block can be determined based on the following formula:

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.

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.

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. 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.

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.

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.

It should be noted that the preset coefficient threshold can be selected according to actual needs, and there is no specific restriction here.

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:

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.

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.

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. 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.

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.

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.

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.

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:

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.

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. 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.

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.

It should be noted that the preset complexity threshold can be selected according to actual needs, and there is no specific restriction here.

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.

Specifically, the edge weights in the similarity-weighted graph can be defined based on the following formula:

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.

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:

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.

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.

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.

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. 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. .

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.

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. 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.

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.

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. 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.

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.

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.

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:

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. 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.

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. Compress the sub-point cloud block according to the Laplacian matrix.

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.

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.

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.

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.

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:

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.

In an optional implementation, the correlation determination module 320 is specifically used to:

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;

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;

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;

The Pearson correlation coefficient is determined as the correlation coefficient between the geometric attribute information and the color attribute information.

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:

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.

In an optional implementation, the first compression module 330 is specifically used to:

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;

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;

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;

The sub-point cloud blocks are compressed according to the Laplacian matrix.

In an optional implementation, the texture complexity determination module 340 is specifically used to:

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;

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.

In an optional implementation, the second compression module 350 is specifically used to:

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;

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;

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;

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;

The sub-point cloud blocks are compressed according to the Laplacian matrix.

In an optional implementation, the third compression module 360 is specifically used to:

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;

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;

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;

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;

The sub-point cloud blocks are compressed according to the Laplacian matrix.

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.

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.

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:

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 .

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.

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.

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.

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.

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.

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.

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. .

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.

Industrial applicability

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.

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)

1. A point cloud compression method, characterized by including:

   Obtain the point cloud to be compressed and divide the point cloud to be compressed 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 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;

   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;

   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;

   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.
2. 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:

   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;

   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;

   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;

   The Pearson correlation coefficient is determined as the correlation coefficient between the geometric attribute information and the color attribute information.
3. 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:

   

   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.
4. 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:

   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;

   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;

   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;

   The sub-point cloud blocks are compressed according to the Laplacian matrix.
5. 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:

   

   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.
6. 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:

   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;

   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.
7. 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:

   

   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.
8. 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:

   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;

   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;

   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;

   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;

   The sub-point cloud blocks are compressed according to the Laplacian matrix.
9. The method according to claim 8, characterized in that the edge weight in the similarity weighted graph is defined based on the following formula:

   

   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.
10. 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:

    

    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.
11. 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:

    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;

    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;

    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;

    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;

    The sub-point cloud blocks are compressed according to the Laplacian matrix.
12. A point cloud compression device, characterized by including:

    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.
13. The apparatus of claim 12, wherein the correlation determination module is configured to:

    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;

    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;

    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;

    The Pearson correlation coefficient is determined as the correlation coefficient between the geometric attribute information and the color attribute information.
14. The apparatus of claim 13, wherein the correlation determination module is further configured to:

    The Pearson correlation coefficient corresponding to the sub-point cloud block is determined based on the following formula:

    

    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.
15. The device according to any one of claims 12 to 14, characterized in that the first compression module is specifically configured to:

    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;

    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;

    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;

    The sub-point cloud blocks are compressed according to the Laplacian matrix.
16. The device according to any one of claims 12 to 15, characterized in that the texture complexity determination module is configured for:

    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;

    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.
17. The device according to any one of claims 12 to 16, characterized in that the second compression module is configured for:

    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;

    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;

    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;

    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;

    The sub-point cloud blocks are compressed according to the Laplacian matrix.
18. The device according to any one of claims 12 to 17, characterized in that the third compression module is configured for:

    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;

    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;

    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;

    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;

    The sub-point cloud blocks are compressed according to the Laplacian matrix.
19. 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.
20. 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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