A point cloud compression method and system based on depth map and redundant point removal

Abstract

The present application relates to a kind of point cloud compression method based on depth map and redundant point removal, belong to data compression technical field, comprising the following steps: S1: single frame point cloud data is converted into depth map representation;S2: horizon is estimated;S3: remove near point in depth map;S4: the point of remaining depth map is clustered;S5: according to the density characteristics of point cloud cluster, redundant point is removed;S6: point cloud data is intra predicted;S7: the point cloud cluster label, point cloud cluster center value and prediction residual of each frame distance image are obtained Compression into bitstream and packaged into file.

Description

Technical Field

[0001] The present invention belongs to the technical field of data compression, and relates to a point cloud compression method and system based on depth map and redundant point removal. Background Art

[0002] In recent years, LiDAR (Light Detection and Ranging) has become an indispensable component of numerous practical applications, such as self-driving cars, mobile robots, and drones. LiDAR point clouds can accurately capture a three-dimensional representation of the surrounding environment, known as three-dimensional (3D) LiDAR point clouds. LiDAR point clouds consist of geometric information (3D coordinates) and corresponding attribute information (reflection intensity) of points, and are characterized by sparseness, disorder, and large amounts of data. For example, the most common LiDAR generates over two million points per second. Transmitting and storing such massive amounts of data undoubtedly places enormous pressure on limited network bandwidth and storage devices, significantly hindering the development of LiDAR point cloud-related applications and the implementation of point cloud-related technologies. Therefore, exploring how to efficiently encode LiDAR point clouds has become a pressing issue for both academia and industry, with significant research and application value.

[0003] One of the core challenges of LiDAR point cloud encoding is how to effectively remove spatial redundancy in point cloud geometric information. Due to the sparsity of LiDAR point clouds, especially in outdoor scenes, there are numerous discontinuous regions, meaning that a significant portion of the space contains no points. Consequently, existing representation methods are relatively inefficient. Furthermore, most current methods focus solely on data fidelity, often overlooking the fact that LiDAR point cloud data is intended for machine vision, not human vision. The ultimate goal of LiDAR point cloud encoding is to reduce the amount of data transmitted by the point cloud and enable each terminal to perform a range of machine vision tasks with excellent performance at the decoding end. Therefore, when considering encoder design, it is crucial to incorporate performance evaluation of machine perception tasks. Furthermore, the complexity of some existing methods makes real-time encoding difficult. From a practical perspective, real-time performance helps ensure the feasibility of many application tasks. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a point cloud compression method and system based on depth map and redundant point removal, which converts the single-frame original point cloud into a depth map for representation to structure the sparse and disordered points in the single-frame point cloud data, and clusters the non-ground points in combination with the information of the depth map. In order to better take into account the machine perception application, the redundant points with low density after clustering are eliminated. Under the condition of the same compression rate, more fine quantization of high-density objects can be used to improve the performance of machine perception applications.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] In one aspect, the present invention provides a point cloud compression method based on a depth map and redundant point removal, comprising the following steps:

[0007] S1: Convert single-frame point cloud data into depth map representation;

[0008] S2: Estimate the horizon;

[0009] S3: remove the perigee in the depth map;

[0010] S4: cluster the remaining points of the depth map;

[0011] S5: Remove redundant points based on the density characteristics of the point cloud cluster;

[0012] S6: Perform intra-frame prediction on point cloud data;

[0013] S7: Compress the point cloud cluster labels, point cloud cluster center values ​​and prediction residuals of each frame of the range image into a bit stream and package them into a file.

[0014] Furthermore, in step S1, the 3D points represented by the Cartesian coordinate system are mapped into the depth map. The formula for converting the Cartesian coordinate system into the spherical coordinate system is:

[0015]

[0016] For a point P(x,y,z) in a point cloud, it is represented as P′(ρ,φ,θ) in a spherical coordinate system; the angle between P′ and the xy plane is the pitch angle φ, the angle between P′ and the xz plane is the yaw angle θ, and the distance from the origin is ρ;

[0017] The hollow cylinder is then projected onto the xz plane to form a depth map.

[0018] Furthermore, in step S2, points with z component size < 1.5 in the original point cloud are selected and put into the test point set. An appropriate number of points are extracted from the set and the ground plane is selected using the Ransic algorithm. The formula for the ground plane to be estimated is as follows:

[0019] Ax+By+Cz+D=0

[0020] A, B, C, and D are the parameters to be determined.

[0021] Furthermore, the Ransic algorithm is used to generate the ground plane, specifically including the following steps:

[0022] Randomly select N points from the set of test points;

[0023] Calculate model parameters;

[0024] Take the remaining points and calculate whether the distance to the plane is less than the threshold. If so, record it as an inner point and count it. If not, record it as an outer point and count it.

[0025] When all points are taken, determine whether the number of inliers is greater than that of the previous iteration. If so, update the model parameters. If not, continue iterating until the maximum number of iterations is reached.

[0026] Furthermore, in step S3, the distance from each valid point in the depth map to the fitted ground plane is calculated, and the point whose distance is less than a typical value is set as the perigee.

[0027] Further, step S4 specifically includes the following steps:

[0028] S41: Group the points from the same object in each LiDAR scanning channel; check the Euclidean distance between each two adjacent valid points, if the distance is greater than th H , a new channel cluster is created; if the distance is less than th H , then expand the current cluster; check the distance between the last point and the first point of the channel, if they have the continuous property, this will merge the two channel clusters;

[0029] S42: Perform inter-channel grouping and merge the groups on the channels by checking the connection of the filters; connect the filters to determine the connectivity between the current group and the adjacent channel groups; the center point of the filter passes through each valid point on the range image, and during the checking process, the filter checks the distance between the center point and each valid point above the current channel; if the distance is less than th V , then the two point cloud clusters are connected, and the cluster with the larger number of labels is merged into the cluster with the smallest number of labels.

[0030] Furthermore, in step S5, redundant points are removed through a redundant point removal mechanism based on cluster density, specifically including: setting a typical density threshold. When the density of a point cloud cluster is lower than the threshold, the cluster is considered to be a low-density cluster, and all points in such a cluster are divided into a predefined empty set; and point cloud compression is performed using fidelity compression.

[0031] Further, step S6 specifically includes the following steps:

[0032] Divide the pixel block area according to the clustering result in step S5; use the mean value of the pixel block to replace the actual value of all pixels in the block;

[0033] For each point cloud cluster obtained after optimization, calculate its geometric center as the center point;

[0034] For each point in the point cloud cluster, the prediction residual value is calculated and obtained based on its positional relationship with the center point;

[0035] The solution formula for the prediction residual value is as follows:

[0036]

[0037] QuantFactor is used to adjust the compression rate of the point cloud to be compressed. Increasing QuantFactor can increase the compression rate of the point cloud to be compressed.

[0038] Furthermore, step S7 specifically includes: using signed data compression for the point cloud cluster labels and prediction residuals, using floating-point data compression for the floating-point data of the point cloud cluster center value, and then encapsulating the data after entropy encoding of the three into a code stream and outputting the encoded bit stream.

[0039] In another aspect, the present invention provides a point cloud compression system based on a depth map and redundant point removal, comprising:

[0040] Depth map conversion module, used to obtain depth map data from 3D point cloud data;

[0041] A clustering segmentation module, configured to divide each point in the depth map into a plurality of sub-point cloud clusters; the clustering segmentation module includes a ground plane estimation module, a ground plane removal module, and a clustering module;

[0042] The ground plane estimation module is used to extract ground plane information from the point cloud;

[0043] The ground plane removal module is used to remove the perigee in the point cloud;

[0044] The clustering module is used to cluster the points in the depth map according to their horizontal and vertical relationships;

[0045] Redundant point elimination module, used to remove redundant points based on the density characteristics of point cloud clusters;

[0046] The intra-frame prediction module is used to find the center point of each point cloud cluster and calculate the prediction residual value of each point in the point cloud cluster relative to the center point of the point cloud cluster;

[0047] The compression coding module is used to compress the point cloud cluster labels, point cloud cluster center values ​​and prediction residuals of each frame of the range image into a bit stream and package them into a file.

[0048] The present invention offers the following benefits: It proposes a method for removing redundant points based on depth maps and point clouds. This effectively eliminates redundant points from irregular and sparse point cloud data, ensuring high perceptual capabilities in downstream machine perception tasks. Furthermore, the device implementing the present invention utilizes a parallelizable point clustering module, enabling real-time point cloud compression processing on embedded or edge computing devices.

[0049] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0051] Figure 1 Schematic diagram of the point cloud compression method based on depth map and redundant point removal according to Example 1 of the present invention;

[0052] Figure 2 is a flow chart of the ground plane estimation method design in Example 1 of the present invention;

[0053] Figure 3 This is a schematic diagram of the bit stream encoding design in Example 1 of the present invention;

[0054] Figure 4 This is a schematic diagram of the structure of the point cloud compression system described in Example 2 of the present invention. DETAILED DESCRIPTION

[0055] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0056] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0057] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the embodiments of the present invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the embodiments of the present invention.

[0058] Example 1

[0059] This embodiment is a preferred implementation of the point cloud compression method based on depth map and redundant point removal of the present invention.

[0060] See also Figure 1 ,like Figure 1 As shown, the method of this embodiment includes:

[0061] Convert single-frame point cloud data into a depth map representation, that is, map the 3D points represented by the Cartesian coordinate system into the depth map. In this invention, a 64*2000 resolution depth map is used. The specific resolution used for mapping varies depending on the laser radar. The formula for converting the Cartesian coordinate system to the spherical coordinate system is given below:

[0062]

[0063] For example, a point P(x, y, z) in a point cloud is represented in spherical coordinates as P′(ρ, φ, θ). The angle between P′ and the xy plane is the pitch angle φ, the angle between P′ and the xz plane is the yaw angle θ, and the distance from the origin is ρ. Next, the hollow cylinder is projected onto the xz plane to form a depth map.

[0064] The second step is to estimate the ground plane. A large portion of the points in the LiDAR point cloud are located on the ground plane. This method selects points in the original point cloud with a z component less than 1.5 and places them into the test point set. An appropriate number of points are extracted from these points and the Ransic algorithm is used to determine the ground plane. The formula for the estimated ground plane is as follows:

[0065] Ax+By+Cz+D=0

[0066] A, B, C, and D are the parameters to be determined. The implementation process of the Ransic algorithm is as follows: Figure 2 As shown, the following steps are included:

[0067] 1) Randomly select N points from the set of points to be tested. In this embodiment, 10 points are selected.

[0068] 2) Calculate model parameters;

[0069] 3) Take the remaining points and calculate whether the distance to the plane is less than the threshold. If so, record it as an inner point and count it. If not, record it as an outer point and count it.

[0070] 4) After all points are taken, determine whether the number of inliers is greater than that of the previous iteration. If so, update the model parameters. If not, continue iterating until the maximum number of iterations is reached.

[0071] The third step is to remove the perigee from the depth map. This step calculates the distance from each valid point (i.e., non-zero point) in the depth map to the fitted ground plane. Points whose distance is less than a typical value are set as the perigee. This typical value is set to 0.1m in this invention.

[0072] The fourth step is to cluster the remaining points in the depth map. As a perception task, point cloud clustering algorithms can be used to segment points into object instances. The first sub-step is to group points from the same object in each lidar scan channel. We check the Euclidean distance between each two adjacent valid points. If the distance is greater than th H , a new channel cluster is created; if the distance is less than th H , the current cluster is expanded. At the same time, due to the lidar scanning mechanism, each channel is a ring in three-dimensional space. Therefore, it is necessary to check the distance between the last point and the first point of the channel to ensure continuity. If it has a continuous property, the two channel clusters will be merged. The second sub-step is to perform inter-channel grouping. In this part, a connection check similar to a filter is designed to merge the groups on the channel. The connection filter determines the connectivity between the current group and the adjacent channel group. Due to the missing point problem, points belonging to the same object may be separated in multiple non-adjacent channels, resulting in over-segmentation problems. In this case, the size of the filter can determine the search space of the local range image area, thereby solving this problem. In this sub-step, the center point of the filter will pass through each valid point on the range image. During the inspection process, the filter will check the distance between the center point and each valid point above the current channel. If the distance is less than th V , then the two point cloud clusters are connected, and the cluster with a larger number of labels will be merged into the cluster with the smallest number of labels.

[0073] In the fifth step of the present invention, based on the aforementioned classification results, redundant points are further effectively removed based on the density characteristics of the point cloud clusters. Specifically, in the fourth step, we perform a preliminary clustering operation on the points in the depth map based on horizontal and vertical distance to form several point cloud clusters. However, due to the significant sparsity of point cloud data, using an excessively small distance segmentation step size during the clustering process can result in a large number of point cloud clusters containing only a very small number of points, or even clusters with only a few points. Such low-density point cloud clusters not only significantly reduce the compression efficiency of the point cloud data and increase the amount of encoded data, but also carry limited information in downstream deep learning tasks, making them of little benefit to model training and inference. To optimize the compression rate of point cloud data and improve the performance of downstream tasks, this step introduces a redundant point removal mechanism based on cluster density. Specifically, a typical density threshold is set. When the density of a point cloud cluster falls below this threshold, the cluster is considered low-density, and all points in the cluster are grouped into a predefined empty set. This approach effectively reduces the length of the encoded bitstream, lowering storage and transmission costs. At the same time, by retaining key point information in high-density clusters, sufficient feature information is retained during the compression process, thereby not affecting the deep learning model's ability to understand and process point cloud data. In addition, to meet the requirements of non-deep learning downstream tasks, this step can be programmatically disabled, and point cloud compression is performed using fidelity compression.

[0074] In the sixth step of the present invention, based on the aforementioned optimized clustering results, intra-frame prediction of point cloud data is achieved. In the entire point cloud compression framework, frames are used as basic coding units for processing. Here, a frame is defined as a point cloud data set generated after one week of LiDAR scanning. The core idea of ​​the proposed intra-frame prediction algorithm is that the pixel values ​​of the distance image reflect the distance characteristics of each point in space to the origin of the LiDAR coordinate system. Specifically, for point groups belonging to the same object, their distance values ​​to the LiDAR center are usually similar. Therefore, during the processing process, the pixel block area can be divided according to the clustering results in the fifth step. Subsequently, the mean value of the pixel block is used to replace the actual value of all pixels in the block. This operation not only generates more uniform and flat areas, but also effectively reduces the amount of data that needs to be processed during the encoding process, thereby significantly improving the efficiency of subsequent encoding. The specific operation process is as follows:

[0075] First, before performing intra-frame prediction, the center point of each point cloud cluster needs to be obtained. Specifically, for each point cloud cluster obtained after optimization, its geometric center is calculated as the center point. Then, for each point in the point cloud cluster, based on its positional relationship relative to the center point, the prediction residual value is calculated and obtained. The prediction residual value is calculated by taking the difference between the actual coordinates of each point and the coordinates of the center point as the residual to capture the subtle changes of each point in the point cloud cluster, thereby providing effective prediction information for subsequent compression. The solution formula for the prediction residual value is as follows:

[0076]

[0077] By controlling QuantFactor, you can control the compression rate of the point cloud to be compressed. Increasing QuantFactor can increase the compression rate of the point cloud to be compressed.

[0078] After completing the above operations, the "point cloud cluster label", "point cloud cluster center value" (i.e. predicted pixel value) and "prediction residual" of each frame of the distance image can be obtained. The three sets of data obtained above will be further compressed. The specific process of bit stream encoding is as follows: Figure 3 As shown in FIG, the signed integer data such as the point cloud cluster label and the prediction residual are compressed using signed data, and the floating-point data of the point cloud cluster center value are compressed using floating-point data. Then, the data after the entropy coding of the three are encapsulated into a code stream and the coded bit stream is output.

[0079] Example 2

[0080] This embodiment is a preferred implementation of the point cloud compression device based on depth map and redundant point removal of the present invention.

[0081] See also Figure 4 , the device of this embodiment includes:

[0082] Depth map conversion module, used to obtain depth map data from 3D point cloud data;

[0083] The formula for converting the Cartesian coordinate system to the spherical coordinate system is:

[0084]

[0085] For example, a point P(x, y, z) in a point cloud is represented in spherical coordinates as P′(ρ, φ, θ). The angle between P′ and the xy plane is the pitch angle φ, the angle between P′ and the xz plane is the yaw angle θ, and the distance from the origin is ρ. Next, the hollow cylinder is projected onto the xz plane to form a depth map.

[0086] Clustering segmentation module, used to divide each point in the depth map into multiple sub-point cloud clusters;

[0087] In this specific embodiment, the cluster segmentation module may further include:

[0088] The ground plane estimation module is used to extract the ground plane information in the point cloud; the method is: according to the height information in the space, the points are placed in the buffer array, a small number of points are randomly sampled from the buffer array, and the ground plane information is extracted according to the height information in the space. Figure 2 The flowchart shown performs ground plane estimation.

[0089] The ground plane removal module is used to remove ground points from the point cloud. This method calculates the distance from each valid point (i.e., non-zero point) in the depth map to the fitted ground plane. Points whose distance is less than a typical value are set as perigees. This typical value is set to 0.1m in this device.

[0090] The clustering module clusters the points in the depth map according to their horizontal and vertical relationships. The specific implementation steps are as follows: The first sub-step is to group the points from the same object in each lidar scanning channel. We check the Euclidean distance between each two adjacent valid points. If the distance is greater than th H , a new channel cluster is created; if the distance is less than th H , the current cluster is expanded. At the same time, due to the lidar scanning mechanism, each channel is a ring in three-dimensional space. Therefore, it is necessary to check the distance between the last point and the first point of the channel to ensure continuity. If it has a continuous property, the two channel clusters will be merged. The second sub-step is to perform inter-channel grouping. In this part, a connection check similar to a filter is designed to merge the groups on the channel. The connection filter determines the connectivity between the current group and the adjacent channel group. Due to the missing point problem, points belonging to the same object may be separated in multiple non-adjacent channels, resulting in over-segmentation problems. In this case, the size of the filter can determine the search space of the local range image area, thereby solving this problem. In this sub-step, the center point of the filter will pass through each valid point on the range image. During the inspection process, the filter will check the distance between the center point and each valid point above the current channel. If the distance is less than th V , then the two point cloud clusters are connected, and the cluster with a larger number of labels will be merged into the cluster with the smallest number of labels.

[0091] The redundant point elimination module follows the cluster segmentation module. Based on the density statistics of each sub-point cloud cluster, it eliminates clusters with low density, which may not be helpful for machine perception recognition. Specifically, the module statistically separates clusters with values ​​below the typical value into empty sets, reducing the length of the encoded bitstream while retaining sufficient feature information. Furthermore, to meet the requirements of non-deep learning downstream tasks, this step can be programmatically disabled, and point cloud compression is performed using fidelity compression.

[0092] The intra-frame prediction module follows the redundant point elimination module. The specific implementation steps are as follows: first, the center point of each point cloud cluster is obtained, and the prediction residual value of each point in the point cloud cluster relative to the center point of the point cloud cluster is calculated.

[0093] After completing the above operations, the “point cloud cluster label”, “point cloud cluster center value” (i.e. predicted pixel value) and “prediction residual” of each frame distance image can be obtained.

[0094] The last module required is the compression encoding module, which compresses the "point cloud cluster label", "point cloud cluster center value" (i.e. predicted pixel value) and "prediction residual" of each frame of the distance image obtained by the above modules into a bit stream and packages it into a file.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A point cloud compression method based on depth map and redundant point removal, characterized by: The following steps are involved: S1: Convert single-frame point cloud data into depth map representation; S2: Estimate the horizon; S3: remove the perigee in the depth map; S4: cluster the remaining points of the depth map; S5: Remove redundant points based on the density characteristics of the point cloud cluster; S6: Perform intra-frame prediction on point cloud data; S7: compressing the point cloud cluster labels, point cloud cluster center values ​​and prediction residuals of each frame of the range image into a bit stream and packaging them into a file; Step S4 specifically includes the following steps: S41: Group the points from the same object in each LiDAR scanning channel; check the Euclidean distance between every two adjacent valid points, if the distance is greater than , a new channel cluster is created; if the distance is less than , then expand the current cluster; check the distance between the last point and the first point of the channel, and if they have the continuous property, merge the two channel clusters; S42: Perform inter-channel grouping and merge the groups on the channels by checking the connection of the filters; connect the filters to determine the connectivity between the current group and the adjacent channel groups; the center point of the filter passes through each valid point on the range image, and during the checking process, the filter checks the distance between the center point and each valid point above the current channel; if the distance is less than , then the two point cloud clusters are connected, and the cluster with a larger number of labels is merged into the cluster with the smallest number of labels; In step S5, redundant points are removed by a cluster density-based redundant point removal mechanism, specifically including: setting a typical density threshold. When the density of a point cloud cluster is lower than the threshold, the cluster is considered to be a low-density cluster, and all points in such a cluster are divided into a predefined empty set; point cloud compression is performed using a fidelity compression method; Step S6 specifically includes the following steps: Divide the pixel block area according to the clustering result in step S5; use the mean value of the pixel block to replace the actual value of all pixels in the block; For each point cloud cluster obtained after optimization, calculate its geometric center as the center point; For each point in the point cloud cluster, the prediction residual value is calculated and obtained based on its positional relationship with the center point; The solution formula for the prediction residual value is as follows: in Indicates the depth value of a pixel after intra-frame prediction. It represents the average depth value of the area where the pixel is located; Used to adjust the compression rate of the point cloud to be compressed, increase It can increase the compression rate of the point cloud to be compressed.

2. The point cloud compression method based on depth map and redundant point removal according to claim 1, characterized in that: In step S1, the 3D points represented by the Cartesian coordinate system are mapped to the depth map. The formula for converting the Cartesian coordinate system to the spherical coordinate system is: For points in the point cloud , which is expressed in spherical coordinates as ; The angle between the xy plane and the ground is the pitch angle , the angle with the xz plane is the yaw angle , the distance from the origin is ; The hollow cylinder is then projected onto the xz plane to form a depth map.

3. The point cloud compression method based on depth map and redundant point removal according to claim 1, characterized in that: In step S2, points with z-component size less than 1.5 in the original point cloud are selected and put into the test point set. An appropriate number of points are extracted from the set and the ground plane is estimated using the Ransic algorithm. The formula for the ground plane to be estimated is as follows: A, B, C, and D are the parameters to be determined.

4. The point cloud compression method based on depth map and redundant point removal according to claim 3, characterized in that: The Ransic algorithm is used to estimate the ground plane, specifically including the following steps: Randomly select N points from the set of test points, N=10; Calculate model parameters; Take the remaining points and calculate whether the distance to the ground plane is less than the threshold. If so, record it as an inner point and count it. If not, record it as an outer point and count it. When all points are taken, determine whether the number of inliers is greater than that of the previous iteration. If so, update the model parameters. If not, continue iterating until the maximum number of iterations is reached.

5. The point cloud compression method based on depth map and redundant point removal according to claim 1, characterized in that: In step S3, the distance from each valid point in the depth map to the fitted ground plane is calculated, and the point whose distance is less than a typical value is set as the perigee.

6. The point cloud compression method based on depth map and redundant point removal according to claim 1, characterized in that: Step S7 specifically includes: using signed data compression for the point cloud cluster labels and prediction residuals, using floating-point data compression for the floating-point data of the point cloud cluster center value, and then encapsulating the data after entropy encoding of the three into a code stream and outputting the encoded bit stream.

7. A point cloud compression system based on depth map and redundant point removal, for implementing the point cloud compression method based on depth map and redundant point removal according to any one of claims 1 to 6, characterized in that: include: Depth map conversion module, used to obtain depth map data from 3D point cloud data; A clustering segmentation module, configured to divide each point in the depth map into a plurality of sub-point cloud clusters; the clustering segmentation module includes a ground plane estimation module, a ground plane removal module, and a clustering module; The ground plane estimation module is used to extract ground plane information from the point cloud; The ground plane removal module is used to remove the perigee in the point cloud; The clustering module is used to cluster the points in the depth map according to their horizontal and vertical relationships; Redundant point elimination module, used to remove redundant points based on the density characteristics of point cloud clusters; The intra-frame prediction module is used to find the center point of each point cloud cluster and calculate the prediction residual value of each point in the point cloud cluster relative to the center point of the point cloud cluster; The compression coding module is used to compress the point cloud cluster labels, point cloud cluster center values ​​and prediction residuals of each frame of the range image into a bit stream and package them into a file.

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