Compression transmission method and system for point cloud data

Abstract

The invention discloses a point cloud data compression transmission method and system. The method comprises the following steps: collecting point cloud data; preprocessing the point cloud data to obtain preprocessed point cloud data; performing compression processing on the preprocessed point cloud data by adopting a point cloud compression algorithm based on an octree to obtain compressed point cloud data; and transmitting the compressed point cloud data by adopting a difference transmission technology, and visually displaying the compressed point cloud data. According to the method, the problem that according to an existing compression method based on the scanning mode of the laser radar and the time sequence correlation of the data, when the scanning environment becomes complex or the scanning speed changes, distribution of the point cloud data possibly becomes irregular, and therefore the redundant information recognition difficulty is increased is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of point cloud data processing, and in particular to a method and system for compressing and transmitting point cloud data. Background Art

[0002] In the field of processing and transmission of laser point cloud data, there is already a compression transmission method in the prior art. This method makes full use of the characteristic that the emission angle of the rotating laser radar remains unchanged during the stable scanning process, and realizes effective compression of the data volume by reducing the storage and transmission of redundant information. Specifically, based on the scanning mode of the laser radar and the temporal correlation of the data, this method intelligently screens and compresses the point cloud data generated during the scanning process, thereby reducing the bandwidth requirements for data transmission and improving the efficiency of data transmission. However, although this method exhibits good compression performance under certain conditions, its application scenarios and effects are subject to certain limitations. In particular, when the scanning environment becomes complex, such as when there are a large number of obstructions or reflective surfaces, or when the scanning speed of the laser radar changes, the effect of this method in reducing redundant information may be greatly reduced. This is because the complex environment or changing scanning speed may cause the distribution of point cloud data to become irregular, thereby increasing the difficulty of redundant information identification and reducing compression efficiency. Summary of the invention

[0003] In response to the above-mentioned defects, the present invention proposes a compression transmission method and system for point cloud data, aiming to solve the problem that the existing compression method based on the scanning mode of lidar and the temporal correlation of data may cause the distribution of point cloud data to become irregular when the scanning environment becomes complex or the scanning speed changes, thereby increasing the difficulty of identifying redundant information.

[0004] To achieve this object, the present invention adopts the following technical solutions:

[0005] A method for compressing and transmitting point cloud data comprises the following steps:

[0006] Step S1: collecting point cloud data;

[0007] Step S2: preprocessing the point cloud data to obtain preprocessed point cloud data;

[0008] Step S3: using an octree-based point cloud compression algorithm to compress the preprocessed point cloud data to obtain compressed point cloud data;

[0009] Step S4: using differential transmission technology to transmit the compressed point cloud data, and visually displaying the compressed point cloud data.

[0010] Preferably, step S3 specifically includes the following sub-steps:

[0011] Step S31: construct an initial octree;

[0012] Step S32: placing the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree;

[0013] Step S33: Calculate the centroid of all points inside each non-empty voxel in the 3D voxel network corresponding to the initial octree;

[0014] Step S34: Divide the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree into several parts, calculate the centroid point of each part, and use the displacement vector to encode the relationship between the original point and the centroid point in each part;

[0015] Step S35: performing wavelet transform on the pre-processed point cloud data in the 3D voxel network corresponding to the initial octree to obtain components of different frequencies;

[0016] Step S36: The centroids of all points inside each non-empty voxel in the 3D voxel network corresponding to the initial octree, the centroids of all divided parts of the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree, and components of different frequencies are organized according to a specific data structure to form compressed point cloud data.

[0017] Preferably, in step S4, the compressed point cloud data is transmitted using a differential transmission technology, which specifically includes the following sub-steps:

[0018] Step S41: using the weak check algorithm in the Rsync tool to calculate the checksum of the compressed point cloud data, and transmitting the checksum of the compressed point cloud data to the receiving end;

[0019] Step S42: Calculate the checksum of the existing point cloud data at the receiving end by using the weak check algorithm in the Rsync tool at the receiving end, and compare it with the checksum of the compressed point cloud data to obtain a difference data block;

[0020] Step S43: Transmit the difference data block to the receiving end;

[0021] Step S44: using the Rsync tool in the receiving end to update the existing point cloud data according to the difference data block to obtain updated point cloud data, and using the version control algorithm to record the updated point cloud data to track the changes and history of the point cloud data.

[0022] Preferably, in step S43, the following steps are also included: using a strong verification algorithm in the Rsync tool to obtain a hash value of the matching difference data block.

[0023] Another aspect of the present application provides a point cloud data compression transmission system, the system comprising:

[0024] Acquisition module, used to collect point cloud data;

[0025] A preprocessing module, used for preprocessing point cloud data to obtain preprocessed point cloud data;

[0026] A compression module is used to compress the pre-processed point cloud data using an octree-based point cloud compression algorithm to obtain compressed point cloud data;

[0027] A transmission module, used to transmit the compressed point cloud data using a differential transmission technology;

[0028] The visualization display module is used to visualize the compressed point cloud data.

[0029] Preferably, the compression module comprises:

[0030] Build submodule for building initial octree;

[0031] The placement submodule is used to place the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree;

[0032] The first calculation submodule is used to calculate the centroid of all points inside each non-empty voxel in the 3D voxel network corresponding to the initial octree;

[0033] A partitioning submodule, for partitioning the pre-processed point cloud data in the 3D voxel network corresponding to the initial octree into several parts;

[0034] The second calculation submodule is used to calculate the center of gravity of each part;

[0035] an encoding submodule for encoding the relationship between the original point and the centroid point in each part using a displacement vector;

[0036] The wavelet transform submodule is used to perform wavelet transform on the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree to obtain components of different frequencies;

[0037] The organization submodule is used to organize the centroids of all points inside each non-empty voxel in the 3D voxel network corresponding to the initial octree, the centroids of all divided parts of the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree, and components of different frequencies according to a specific data structure to form compressed point cloud data.

[0038] Preferably, the transmission module comprises:

[0039] The third calculation submodule is used to calculate the checksum of the compressed point cloud data using the weak check algorithm in the Rsync tool;

[0040] A first transmission submodule, used for transmitting the checksum of the compressed point cloud data to a receiving end;

[0041] The first acquisition submodule is used to calculate the checksum of the existing point cloud data at the receiving end by using the weak check algorithm in the Rsync tool at the receiving end, and compare it with the checksum of the compressed point cloud data to obtain a difference data block;

[0042] A second transmission submodule, used for transmitting the difference data block to a receiving end;

[0043] The tracing submodule is used to update the existing point cloud data according to the difference data block by using the Rsync tool in the receiving end to obtain the updated point cloud data, and use the version control algorithm to record the updated point cloud data to trace the changes and history of the point cloud data.

[0044] More preferably, it further includes a second acquisition submodule, wherein the second acquisition submodule is used to obtain the hash value of the matching difference data block using the strong verification algorithm in the Rsync tool.

[0045] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0046] In this solution, an octree-based point cloud compression algorithm is introduced to compress point cloud data. Compared with the existing compression method based on the scanning mode of LiDAR and the temporal correlation of data, the octree-based point cloud compression algorithm can accurately capture the subtle changes of point cloud data by continuously recursively dividing the space when facing complex scanning environments or changing scanning speeds, so as to better handle irregularly distributed point cloud data, thereby better identifying and removing redundant information in point cloud data. In addition, through the hierarchical structure of the octree, efficient organization and management of point cloud data is achieved. This processing method greatly reduces the bandwidth required for data storage and transmission, thereby significantly improving the compression efficiency of point cloud data. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The present invention is a flowchart of the steps of a method for compressing and transmitting point cloud data. DETAILED DESCRIPTION

[0048] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions from beginning to end. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0049] A method for compressing and transmitting point cloud data comprises the following steps:

[0050] Step S1: collecting point cloud data;

[0051] Step S2: preprocessing the point cloud data to obtain preprocessed point cloud data;

[0052] Step S3: using an octree-based point cloud compression algorithm to compress the preprocessed point cloud data to obtain compressed point cloud data;

[0053] Step S4: using differential transmission technology to transmit the compressed point cloud data, and visually displaying the compressed point cloud data.

[0054] A point cloud data compression transmission method in this scheme, such as Figure 1 As shown, the first step is to collect point cloud data. In this embodiment, laser radar is used to collect point cloud data. The second step is to preprocess the point cloud data to obtain preprocessed point cloud data. In this embodiment, the preprocessing includes data filtering and downsampling. By preprocessing the point cloud data, it is beneficial to reduce the amount of point cloud data to improve the subsequent data compression efficiency. The third step is to compress the preprocessed point cloud data using an octree-based point cloud compression algorithm to obtain compressed point cloud data. In this embodiment, in point cloud compression, the octree can achieve effective compression of point cloud data. The principle is to recursively divide the three-dimensional space into eight subspaces until there is only one or several points in each voxel. In this way, a large amount of point cloud data can be effectively organized in the nodes of the octree, thereby achieving data compression. The fourth step is to use differential transmission technology to transmit the compressed point cloud data and visualize the compressed point cloud data. In this embodiment, the differential transmission technology is mainly a hash algorithm and a version control algorithm. It analyzes the changed parts of the data and only transmits these changes instead of the entire data set. In the transmission scenario of point cloud data, differential transmission technology can significantly reduce the amount of data that needs to be transmitted, thereby improving transmission efficiency. After receiving the compressed and differentially transmitted point cloud data, the point cloud data will be converted into a format suitable for rendering, and GPU acceleration will be used for efficient rendering to achieve visual display of the point cloud data.

[0055] In this solution, an octree-based point cloud compression algorithm is introduced to compress point cloud data. Compared with the existing compression method based on the scanning mode of LiDAR and the temporal correlation of data, the octree-based point cloud compression algorithm can accurately capture the subtle changes of point cloud data by continuously recursively dividing the space when facing complex scanning environments or changing scanning speeds, so as to better handle irregularly distributed point cloud data, thereby better identifying and removing redundant information in point cloud data. In addition, through the hierarchical structure of the octree, efficient organization and management of point cloud data is achieved. This processing method greatly reduces the bandwidth required for data storage and transmission, thereby significantly improving the compression efficiency of point cloud data.

[0056] Preferably, step S3 specifically includes the following sub-steps:

[0057] Step S31: construct an initial octree;

[0058] Step S32: placing the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree;

[0059] Step S33: Calculate the centroid of all points inside each non-empty voxel in the 3D voxel network corresponding to the initial octree;

[0060] Step S34: Divide the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree into several parts, calculate the centroid point of each part, and use the displacement vector to encode the relationship between the original point and the centroid point in each part;

[0061] Step S35: performing wavelet transform on the pre-processed point cloud data in the 3D voxel network corresponding to the initial octree to obtain components of different frequencies;

[0062] Step S36: The centroids of all points inside each non-empty voxel in the 3D voxel network corresponding to the initial octree, the centroids of all divided parts of the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree, and components of different frequencies are organized according to a specific data structure to form compressed point cloud data.

[0063] In this embodiment, in step S31, by constructing an initial octree, an effective spatial index can be provided for the point cloud data. In step S32, the 3D voxel grid corresponding to the initial octree is a three-dimensional space grid composed of recursively divided voxels, in which each voxel can contain a certain number of point cloud data points. By placing the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree, it is beneficial to the subsequent point cloud data compression processing. In step S33, by calculating the centroid of all points inside each non-empty body, it is beneficial to simplify the representation of the point cloud data inside each non-empty body. In step S34, by dividing and encoding the point cloud data, it is beneficial to reduce the redundant information of the point cloud data, thereby improving the subsequent compression efficiency. In step S35, wavelet transform is a common signal processing technology. Wavelet transform is used to decompose the point cloud data into components of different frequencies, and high-frequency components are compressed to reduce the amount of data; low-frequency components remain intact. In step S36, the specific data structure is a tree structure.

[0064] Preferably, the compressed point cloud data is transmitted using a differential transmission technology, which specifically includes the following sub-steps:

[0065] Step S41: using the weak check algorithm in the Rsync tool to calculate the checksum of the compressed point cloud data, and transmitting the checksum of the compressed point cloud data to the receiving end;

[0066] Step S42: Calculate the checksum of the existing point cloud data at the receiving end by using the weak check algorithm in the Rsync tool at the receiving end, and compare it with the checksum of the compressed point cloud data to obtain a difference data block;

[0067] Step S43: Transmit the difference data block to the receiving end;

[0068] Step S44: using the Rsync tool in the receiving end to update the existing point cloud data according to the difference data block to obtain updated point cloud data, and using the version control algorithm to record the updated point cloud data to track the changes and history of the point cloud data.

[0069] In this embodiment, in step S41, the Rsync tool is one of the tools required for the hash algorithm, and the Rsync tool includes a weak checksum algorithm, namely a rolling checksum algorithm. The sending end calculates the checksum of the compressed point cloud data by using the weak checksum algorithm, which is conducive to the subsequent comparison of the checksums of the sending end and the receiving end. In step S42, when the receiving end receives the checksum of the compressed point cloud data, the receiving end will use the weak checksum algorithm in the Rsync tool to calculate the checksum of the existing point cloud data at the receiving end, and by comparing the checksum of the existing point cloud data at the receiving end with the checksum of the compressed point cloud data, determine which data blocks are different or newly added, and only transmit these different or newly added data blocks. In step S43, the sending end can reduce the amount of data transmitted by transmitting the difference data block to the receiving end, thereby improving the transmission efficiency. In step S44, by using the Rsync tool in the receiving end to update the existing point cloud data according to the difference data block, it is conducive to avoiding repeated transmission of unchanged data, thereby saving bandwidth and time, and improving the efficiency of data transmission. The version control algorithm is an algorithm used to manage version changes of files, codes or other data. Its core principle is based on a data structure called a version tree. By using the version control algorithm at the receiving end to record the updated point cloud data, it is convenient to track the changes and history of the point cloud data.

[0070] Preferably, in step S43, the following steps are further included: using a strong verification algorithm in the Rsync tool to obtain a hash value of the matching difference data block. In this embodiment, the Rsync tool also includes a strong verification algorithm, such as an MD5 algorithm or a SHA-1 algorithm. The sender obtains the hash value of the matching difference data block through the strong verification algorithm in the Rsync tool, which is conducive to ensuring the integrity of the difference data block.

[0071] Another aspect of the present application provides a point cloud data compression transmission system, the system comprising:

[0072] Acquisition module, used to collect point cloud data;

[0073] A preprocessing module, used for preprocessing point cloud data to obtain preprocessed point cloud data;

[0074] A compression module is used to compress the pre-processed point cloud data using an octree-based point cloud compression algorithm to obtain compressed point cloud data;

[0075] A transmission module, used to transmit the compressed point cloud data using a differential transmission technology;

[0076] The visualization display module is used to visualize the compressed point cloud data.

[0077] The present solution is a point cloud data compression and transmission system, which realizes the compression and transmission of point cloud data through the cooperation of acquisition module, preprocessing module, compression module, transmission module and visualization display module. In the present solution, an octree-based point cloud compression algorithm is introduced to compress point cloud data. Compared with the existing compression method based on the scanning mode of laser radar and the temporal correlation of data, when facing complex scanning environments or changing scanning speeds, the octree-based point cloud compression algorithm can accurately capture the subtle changes of point cloud data by continuously recursively dividing the space, so as to better process irregularly distributed point cloud data, thereby better identifying and removing redundant information in point cloud data. In addition, through the hierarchical structure of the octree, efficient organization and management of point cloud data is achieved. This processing method greatly reduces the bandwidth required for data storage and transmission, thereby significantly improving the compression efficiency of point cloud data.

[0078] Preferably, the compression module comprises:

[0079] Build submodules for building initial octrees;

[0080] The placement submodule is used to place the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree;

[0081] The first calculation submodule is used to calculate the centroid of all points inside each non-empty voxel in the 3D voxel network corresponding to the initial octree;

[0082] A partitioning submodule, for partitioning the pre-processed point cloud data in the 3D voxel network corresponding to the initial octree into several parts;

[0083] The second calculation submodule is used to calculate the center of gravity of each part;

[0084] an encoding submodule for encoding the relationship between the original point and the centroid point in each part using a displacement vector;

[0085] The wavelet transform submodule is used to perform wavelet transform on the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree to obtain components of different frequencies;

[0086] The organization submodule is used to organize the centroids of all points inside each non-empty voxel in the 3D voxel network corresponding to the initial octree, the centroids of all divided parts of the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree, and components of different frequencies according to a specific data structure to form compressed point cloud data.

[0087] In this embodiment, by setting the construction submodule, an effective spatial index can be provided for the point cloud data. By setting the placement submodule, it is beneficial to the subsequent compression processing of the point cloud data. By setting the first calculation submodule, it is beneficial to simplify the representation of the point cloud data inside each non-empty body. By setting the division submodule, the second calculation submodule and the encoding submodule, it is beneficial to reduce the redundant information of the point cloud data, thereby improving the subsequent compression efficiency. In the wavelet transform submodule, wavelet transform is a common signal processing technology. Wavelet transform is used to decompose point cloud data into components of different frequencies, and high-frequency components are compressed to reduce the amount of data; low-frequency components remain intact. In the organization submodule, the specific data structure is a tree structure.

[0088] Preferably, the transmission module includes:

[0089] The third calculation submodule is used to calculate the checksum of the compressed point cloud data using the weak check algorithm in the Rsync tool;

[0090] A first transmission submodule, used for transmitting the checksum of the compressed point cloud data to a receiving end;

[0091] The first acquisition submodule is used to calculate the checksum of the existing point cloud data at the receiving end by using the weak check algorithm in the Rsync tool at the receiving end, and compare it with the checksum of the compressed point cloud data to obtain a difference data block;

[0092] A second transmission submodule, used for transmitting the difference data block to a receiving end;

[0093] The tracing submodule is used to update the existing point cloud data according to the difference data block by using the Rsync tool in the receiving end to obtain the updated point cloud data, and use the version control algorithm to record the updated point cloud data to trace the changes and history of the point cloud data.

[0094] In this embodiment, by setting the third calculation submodule, it is helpful to compare the subsequent checksums of the sending end and the receiving end. By setting the first acquisition submodule, it is helpful to determine which data blocks are different or newly added. By setting the second transmission submodule, it is helpful to reduce the amount of data transmitted, thereby improving the transmission efficiency. In the tracing submodule, by using the Rsync tool in the receiving end to update the existing point cloud data according to the difference data block, it is helpful to avoid repeated transmission of unchanged data, thereby saving bandwidth and time and improving the efficiency of data transmission. By using the version control algorithm in the receiving end to record the updated point cloud data, it is convenient to track the changes and history of the point cloud data.

[0095] More preferably, it further includes a second acquisition submodule, which is used to use the strong verification algorithm in the Rsync tool to obtain the hash value of the matching difference data block. In this embodiment, by setting the second acquisition submodule, it is helpful to ensure the integrity of the difference data block.

[0096] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0097] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for compressing and transmitting point cloud data, characterized in that: The following steps are involved: Step S1: collecting point cloud data; Step S2: preprocessing the point cloud data to obtain preprocessed point cloud data; Step S3: using an octree-based point cloud compression algorithm to compress the preprocessed point cloud data to obtain compressed point cloud data; Step S4: using differential transmission technology to transmit the compressed point cloud data, and visually displaying the compressed point cloud data.

2. The method for compressing and transmitting point cloud data according to claim 1, characterized in that: In step S3, the following sub-steps are specifically included: Step S31: construct an initial octree; Step S32: placing the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree; Step S33: Calculate the centroid of all points inside each non-empty voxel in the 3D voxel network corresponding to the initial octree; Step S34: Divide the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree into several parts, calculate the centroid point of each part, and use the displacement vector to encode the relationship between the original point and the centroid point in each part; Step S35: performing wavelet transform on the pre-processed point cloud data in the 3D voxel network corresponding to the initial octree to obtain components of different frequencies; Step S36: The centroids of all points inside each non-empty voxel in the 3D voxel network corresponding to the initial octree, the centroids of all divided parts of the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree, and components of different frequencies are organized according to a specific data structure to form compressed point cloud data.

3. The method for compressing and transmitting point cloud data according to claim 1, characterized in that: In step S4, the compressed point cloud data is transmitted using a differential transmission technique, which specifically includes the following sub-steps: Step S41: using the weak check algorithm in the Rsync tool to calculate the checksum of the compressed point cloud data, and transmitting the checksum of the compressed point cloud data to the receiving end; Step S42: Calculate the checksum of the existing point cloud data at the receiving end by using the weak check algorithm in the Rsync tool at the receiving end, and compare it with the checksum of the compressed point cloud data to obtain a difference data block; Step S43: Transmit the difference data block to the receiving end; Step S44: using the Rsync tool in the receiving end to update the existing point cloud data according to the difference data block to obtain updated point cloud data, and using the version control algorithm to record the updated point cloud data to track the changes and history of the point cloud data.

4. The method for compressing and transmitting point cloud data according to claim 3, characterized in that: In step S43, the following steps are also included: using the strong verification algorithm in the Rsync tool to obtain the hash value of the matching difference data block.

5. A point cloud data compression transmission system, using the point cloud data compression transmission method according to any one of claims 1 to 4, characterized in that: The system comprises: Acquisition module, used to collect point cloud data; A preprocessing module, used for preprocessing point cloud data to obtain preprocessed point cloud data; A compression module is used to compress the pre-processed point cloud data using an octree-based point cloud compression algorithm to obtain compressed point cloud data; A transmission module, used to transmit the compressed point cloud data using a differential transmission technology; The visualization display module is used to visualize the compressed point cloud data.

6. A point cloud data compression transmission system according to claim 5, characterized in that: The compression module comprises: Build submodules for building initial octrees; The placement submodule is used to place the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree; The first calculation submodule is used to calculate the centroid of all points inside each non-empty voxel in the 3D voxel network corresponding to the initial octree; A partitioning submodule, for partitioning the pre-processed point cloud data in the 3D voxel network corresponding to the initial octree into several parts; The second calculation submodule is used to calculate the center of gravity of each part; an encoding submodule for encoding the relationship between the original point and the centroid point in each part using a displacement vector; The wavelet transform submodule is used to perform wavelet transform on the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree to obtain components of different frequencies; The organization submodule is used to organize the centroids of all points inside each non-empty voxel in the 3D voxel network corresponding to the initial octree, the centroids of all divided parts of the preprocessed point cloud data in the 3D voxel network corresponding to the initial octree, and components of different frequencies according to a specific data structure to form compressed point cloud data.

7. The point cloud data compression transmission system according to claim 5, characterized in that: The transmission module comprises: The third calculation submodule is used to calculate the checksum of the compressed point cloud data using the weak check algorithm in the Rsync tool; A first transmission submodule, used for transmitting the checksum of the compressed point cloud data to a receiving end; The first acquisition submodule is used to calculate the checksum of the existing point cloud data at the receiving end by using the weak check algorithm in the Rsync tool at the receiving end, and compare it with the checksum of the compressed point cloud data to obtain a difference data block; A second transmission submodule, used for transmitting the difference data block to a receiving end; The tracing submodule is used to update the existing point cloud data according to the difference data block by using the Rsync tool in the receiving end to obtain the updated point cloud data, and use the version control algorithm to record the updated point cloud data to trace the changes and history of the point cloud data.

8. The point cloud data compression transmission system according to claim 7, characterized in that: It also includes a second acquisition submodule, which is used to use the strong verification algorithm in the Rsync tool to obtain the hash value of the matching difference data block.

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