A point cloud data processing method, device, equipment and storage medium

Abstract

Embodiments of the present application disclose a point cloud data processing method, device and equipment, and a storage medium. The point cloud data processing method comprises: obtaining collection data of each point cloud in a plurality of point cloud blocks processed by octree segmentation; determining compression feature information of the collection data according to an attribute type of the collection data; and performing compression processing on the collection data according to the compression feature information to obtain compressed data of the collection data of the point cloud block. The present scheme can support compression processing of collection data of multiple different attribute types, and different compression feature information is used for compression of collection data of different attribute types, thereby improving the flexibility and accuracy of the collection data compression mode, and the data information compressed by the scheme is more convenient for storage and transmission.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, apparatus, device and storage medium for processing point cloud data. Background Technology

[0002] A point cloud is typically a collection of spatial points acquired by a 3D measurement device, which can be used to represent 3D shapes or objects in space. The attribute information of a point cloud consists of the various dimensions of its location, which may include, but is not limited to, location information, reflection intensity, acquisition time, and color information.

[0003] When a large number of point clouds are collected, storing the attribute information of the point clouds not only requires a lot of memory, but is also not conducive to transmission. Therefore, how to effectively compress the attribute information of point clouds is crucial for the storage and transmission of point cloud data. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and storage medium for processing point cloud data, offering a new solution for compressing different types of attribute information of point clouds.

[0005] In a first aspect, embodiments of the present invention provide a method for processing point cloud data, comprising:

[0006] Acquire the collected data of each point cloud in several point cloud blocks after octree segmentation;

[0007] Based on the attribute type of the collected data, determine the compression feature information of the collected data;

[0008] Based on the compression feature information, the collected data is compressed to obtain compressed data of several point cloud blocks.

[0009] Secondly, embodiments of the present invention also provide a method for processing point cloud data, comprising:

[0010] Obtain compressed data of collected point cloud blocks after octree segmentation;

[0011] Determine the decompression characteristics of the compressed data based on its attribute type;

[0012] Based on the decompression characteristic information, the compressed data is decompressed to obtain the collected data of each point cloud in several point cloud blocks.

[0013] Thirdly, embodiments of the present invention also provide a point cloud data processing apparatus, comprising:

[0014] The data acquisition module is used to acquire the data of each point cloud in several point cloud blocks after octree segmentation.

[0015] The compression feature determination module is used to determine the compression feature information of the collected data based on the attribute type of the collected data.

[0016] The data compression module is used to compress the collected data according to compression feature information to obtain compressed data of the collected data of several point cloud blocks.

[0017] Fourthly, embodiments of the present invention also provide a point cloud data processing apparatus, comprising:

[0018] The compressed data acquisition module is used to acquire compressed data of collected data from several point cloud blocks that have been processed by octree segmentation;

[0019] The decompression feature determination module is used to determine the decompression feature information of compressed data based on the attribute type of the compressed data;

[0020] The data decompression module is used to decompress compressed data according to decompression feature information to obtain the collected data of each point cloud in the point cloud block.

[0021] Fifthly, embodiments of the present invention also provide an electronic device, comprising:

[0022] One or more processors;

[0023] Memory, used to store one or more programs;

[0024] When one or more programs are executed by one or more processors, the one or more processors implement the point cloud data processing method in any embodiment of the present invention.

[0025] Sixthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the point cloud data processing method of any embodiment of the present invention.

[0026] The technical solution of this invention acquires the collected data of each point cloud in several point cloud blocks after octree segmentation; determines the compression feature information corresponding to different attribute types of collected data according to the attribute type of the collected data; and then compresses the collected data of that attribute type based on the compression feature information of each attribute type. This solution can support the compression processing of collected data of multiple different attribute types, and uses different compression feature information for different attribute types of collected data, improving the flexibility and accuracy of the collected data compression method. Furthermore, the data information compressed in this way is easier to store and transmit. Attached Figure Description

[0027] Figure 1This is a flowchart of a point cloud data processing method provided in Embodiment 1 of the present invention.

[0028] Figure 2 This is a flowchart of a point cloud data processing method provided in Embodiment 2 of the present invention.

[0029] Figure 3 This is a flowchart of a point cloud data processing method provided in Embodiment 3 of the present invention.

[0030] Figure 4a This is a signaling flowchart of a point cloud data processing method provided in Embodiment 4 of the present invention.

[0031] Figure 4b This is an application scenario diagram of a point cloud data processing method provided in Embodiment 4 of the present invention.

[0032] Figure 5 This is a schematic diagram of the structure of a point cloud data processing device provided in Embodiment 5 of the present invention.

[0033] Figure 6 This is a schematic diagram of the structure of a point cloud data processing device provided in Embodiment Six of the present invention.

[0034] Figure 7 This is a schematic diagram of the structure of an electronic device provided in Embodiment 7 of the present invention. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0036] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0037] Example 1

[0038] Figure 1 This is a flowchart illustrating a point cloud data processing method according to Embodiment 1 of the present invention. This embodiment is applicable to situations requiring compression processing of various types of point cloud acquisition data, particularly for compressing various types of point cloud acquisition data acquired in real-time and dynamically. The method can be executed by a point cloud data processing device, which can be implemented in hardware and / or software. This device can be configured in an electronic device at the data acquisition end, which may also be equipped with a LiDAR and a camera for acquiring point cloud data in various dimensions. Furthermore, it can acquire and compress the acquisition data collected by the LiDAR and camera in real-time.

[0039] like Figure 1 As shown, the method specifically includes:

[0040] S110. Obtain the collected data of each point cloud in several point cloud blocks after octree segmentation.

[0041] Point clouds can be collections of spatial point data acquired by 3D measurement equipment (such as LiDAR). Specifically, they can be collections of massive amounts of point data. Point clouds can be used to represent 3D shapes or objects in space.

[0042] The collected data can be various attribute data collected for each point cloud based on different point cloud measurement principles. Optionally, the point cloud collection data in this embodiment can be customized according to requirements, and may include, but is not limited to, at least one of the following: location data, time data, intensity data, color data, and echo count. For example, the point cloud collection data obtained by laser ranging includes: first location data (X1, Y1, Z1), intensity data, time data, and echo count; the point cloud attribute information obtained by photogrammetry includes: second location data (X2, Y2, Z2) and color data. The first location data is the point cloud's location data in the lidar coordinate system, and the second location data is the point cloud's location data in the camera coordinate system. The location data can be the geometric coordinate data of the point cloud in the corresponding coordinate system. The time data can be the timestamp data of the point cloud collection time determined by GPS time (GPSTimeStamp).

[0043] Optionally, this embodiment can employ octree segmentation theory, performing octree segmentation on the acquired point cloud to be compressed (i.e., the acquired point cloud) based on a preset voxel size, thereby dividing the point cloud to be compressed into several point cloud blocks. Here, a point cloud block can be a point cloud to be compressed with a preset voxel size obtained after octree segmentation. The voxel size can be the smallest size of the cube of the segmented point cloud block. Optionally, the voxel size can be preset or modified according to a setting.

[0044] Optionally, after performing octree segmentation on the point cloud to be compressed, for each resulting point cloud block, the acquired data for each point cloud within that block is obtained. Specifically, this acquisition method can be based on pre-set retention precision for location data, time data, color data, and intensity data, acquiring at least one of these as the acquired data for that point cloud. Before acquiring the acquired data, the retention precision for various types of acquired data can be pre-set. Specifically, this retention precision can be the number of bits to retain for the corresponding information data. Optionally, the retention precision for acquired data of different attribute types can be the same or different.

[0045] S120. Determine the compression feature information of the collected data based on the attribute type of the collected data.

[0046] The compression feature information can be the feature information referenced when compressing the collected data. Different attribute types of collected data correspond to different compression feature information. Once the attribute type of the collected data is determined, the compression feature information to be selected can be determined. Optionally, in this embodiment, different rules for determining compression feature information can be pre-set for different attribute types of collected data based on the characteristics of the data information of different attribute types. For example, the rules for determining the corresponding compression feature information can be determined based on whether the data information of each attribute type has a pattern or whether it is distributed within a certain range.

[0047] Specifically, this can be achieved through the following sub-steps:

[0048] S1201. Determine whether the collected data belongs to the first type of data or the second type of data. If it belongs only to the first type of data, execute S1202. If it belongs only to the second type of data, execute S1203. If part of it belongs to the first type of data and the other part belongs to the second type of data, then S1202 needs to be executed for the part belonging to the first type of data and S1203 needs to be executed for the part belonging to the second type of data.

[0049] The first type of data can be irregularly collected data with relatively close differences, and may include, but is not limited to, location data and / or time data; for example, it may also include the number of echoes. The second type of data can be collected data with large differences within a certain threshold range, and may include, but is not limited to, intensity data and / or color data.

[0050] It should be noted that the location data, time data, echo count, intensity data, and color data are uncompressed data collected by the 3D measurement equipment.

[0051] S1202. If the collected data includes the first type of data, then the compressed feature information of the first type of data is determined as the center point of each point cloud block.

[0052] The center point can be the central location of each point cloud block after octree segmentation. This center point may or may not correspond to a point cloud acquired by LiDAR.

[0053] Optionally, if it is determined that the collected data of each point cloud in the point cloud block contains the first type of data, then the first type of data corresponding to the center point of each point cloud block is used as the compressed feature information of the first type of data of that point cloud block.

[0054] S1203. If the collected data includes the second type of data, then the compression feature information of the second type of data is determined to be the preset bit depth compression ratio.

[0055] The preset bit depth compression ratio can be the bit depth compression ratio of a single channel, and this ratio can be preset. Optionally, the preset bit depth compression ratios for different types of data in the second type of data can be the same or different. For example, the bit depth compression ratios for intensity data and color data can be different. The larger the bit depth, the larger the space occupied by the second type of data. By reducing the bit depth value, data compression can be achieved. For example, the space occupied by the acquired data is 8 bits, which is the size of the lossless data before compression; the larger the preset bit depth compression ratio, the smaller the space occupied will be, and the more data loss will occur. Preferably, when the acquired point cloud data is close-up data, the accuracy requirement is relatively high, and the preset bit depth compression ratio can be set relatively small; when the acquired point cloud data is distant-upon data, the accuracy requirement is relatively low, and the preset bit depth compression ratio can be set relatively large.

[0056] If it is determined that the collected data contains second-type data, then the preset bit depth compression ratio will be used as the compression feature information of the second-type data.

[0057] It should be noted that, in the technical solution of this embodiment, the first type of data and the second type of data are two parallel types of data corresponding to different attribute types of the collected data.

[0058] This solution targets different attribute types of collected data and selects different compression features based on their data characteristics to compress the collected data of each attribute type, further improving the flexibility and accuracy of collected data compression.

[0059] S130. Based on the compression feature information, the collected data is compressed to obtain compressed data of the collected data of several point cloud blocks.

[0060] The compressed data is the result of compressing the collected data, and its size is smaller than the original size of the collected data.

[0061] Specifically, compressing the collected data can be achieved by determining the corresponding compression method based on different compression feature information, and then compressing the collected data based on the corresponding compression method.

[0062] Based on the above technical solution, the collected data is compressed according to compression feature information to obtain compressed data of collected point cloud blocks. Preferably, it may include:

[0063] If the compressed feature information is the center point of the point cloud block, then perform a first-order operation on the first type of data of each point cloud in the point cloud block and the first type of data corresponding to the center point, and use the result of the first-order operation and the first type of data corresponding to the center point as the compressed data of the first type of data of the point cloud block.

[0064] The first bit operation can be a process of inverting the first bit and then summing the results. Compressed data of the first type of data can be the result of compressing the first type of data.

[0065] Specifically, in this embodiment, for each point cloud in a point cloud block, the inverse of the first type of data for each point cloud is calculated, and then summed with the first type of data corresponding to the center point. This completes the first-order operation on the first type of data of each point cloud and the first type of data corresponding to the center point. Then, the result of the first-order operation on each point cloud and the first type of data corresponding to the center point are used as the compressed data of the first type of data for that point cloud block.

[0066] For example, if the first type of data contains location data (i.e., the geometric coordinates of the point cloud), the opposite of the corresponding geometric coordinates of each point cloud in the point cloud block can be calculated by the first bit operation, and then summed with the geometric coordinates corresponding to the center point. The summation result of the geometric coordinates corresponding to each point cloud, along with the geometric coordinates corresponding to the center point, is used as the compressed data of the location data in the compressed data of the first type of data.

[0067] If the compression feature information is a preset bit depth compression ratio, then the second type of data of each point cloud in the point cloud block is processed by bit depth compression according to the preset bit depth compression ratio, and the bit depth compression result is used as the compressed data of the second type of data of the point cloud block.

[0068] Specifically, this embodiment can compress the second type of data according to the bit depth compression ratio. For example, if the second type of data is color data, a single channel of color data typically has a threshold value of 0-255, usually occupying 8 bits of space. If we want to compress the space occupied by the bit depth of a single channel from 8 bits to 4 bits, a preset bit depth compression ratio of 2 is used. If it is determined that the acquired data contains color data, the preset bit depth compression ratio is used as compression feature information, and the color data is compressed according to the bit depth compression ratio to obtain the bit depth compression result of the color data. The bit depth compression result of the color data is used as the compressed color data, and the space occupied by a single channel of the compressed color data is compressed from 8 bits to 4 bits.

[0069] This solution targets different types of compressed feature information and selects the corresponding compression processing method based on their data type to compress the collected data of each attribute type, further improving the flexibility and accuracy of the collected data compression processing.

[0070] It should be noted that this embodiment of the invention only uses an octree method to divide all acquired point clouds into point cloud blocks, and does not subsequently use octree compression encoding to perform structured compression encoding on the divided point cloud blocks. A drawback of traditional octree-based structured compression encoding is that if any data is lost during subsequent transmission (i.e., packet loss), regardless of the amount of lost data, the compressed data in the entire data transmission process cannot be decrypted normally. However, this embodiment of the invention uses an unstructured compression encoding method for point cloud data compression. Because it does not use octree-based structured compression encoding, even if data loss occurs during the entire data transmission process, only a portion of the data in the corresponding point cloud block will be unparseable. The data parsing process for other normally transmitted point cloud blocks remains largely unaffected. Furthermore, for LiDAR, the loss of even a single point cloud point will not affect the overall analysis and decision-making. Therefore, this solution effectively addresses the shortcomings of traditional octree-based structured compression encoding.

[0071] The technical solution of this invention acquires the collected data of each point cloud in several point cloud blocks after octree segmentation; determines the compression feature information corresponding to different attribute types of collected data according to the attribute type of the collected data; and then compresses the collected data of that attribute type based on the compression feature information of each attribute type. This solution can support the compression processing of collected data of multiple different attribute types, and uses different compression feature information for different attribute types of collected data, improving the flexibility and accuracy of the collected data compression method. Furthermore, the data information compressed in this way is easier to store and transmit.

[0072] Example 2

[0073] Figure 2 This is a flowchart illustrating a point cloud data processing method according to Embodiment 2 of the present invention. Based on the above embodiments, this embodiment further details the process of obtaining point cloud blocks after octree segmentation. Figure 2 As shown, the method includes:

[0074] S210. Acquire point cloud data from the lidar and image data from the camera;

[0075] The lidar and camera are installed on the same mobile device.

[0076] The point cloud data from the lidar can be data collected by the lidar when scanning the environment. This can include, but is not limited to, initial location data, time data, or intensity data.

[0077] The image data from the camera can be image data captured by the camera based on the ambient space. This may include, but is not limited to, second-position data or color data.

[0078] It should be noted that in this embodiment, the first position data is the position data of the point cloud in the lidar coordinate system, and the second position data is the position data of the point cloud in the camera coordinate system.

[0079] In this embodiment, the LiDAR and camera integrated on the mobile device scan the surrounding environment and collect image data and point cloud data during the movement of the mobile device. The collected image data and point cloud data are then transmitted to the data acquisition terminal device configured on the mobile device. Accordingly, the data acquisition terminal device can obtain the point cloud data collected by the LiDAR and the image data collected by the camera.

[0080] The lidar and camera are installed on the same mobile device, which makes it easy to acquire data on the same environment or the same object at the same time.

[0081] S220. The point cloud data and image data are fused to obtain the collected point cloud data.

[0082] Among them, the collected point cloud can be all the point clouds corresponding to the point cloud data collected by the lidar.

[0083] Specifically, fusing point cloud data and image data can be a process of combining two sets of data with different attributes. For example, location data, intensity data, and time data recorded in the point cloud data can be used as the acquisition data for each point cloud. Then, based on the calibration parameters between the camera and radar, each pixel in the image data is transformed into the radar coordinate system. The corresponding point cloud for each pixel in the radar coordinate system is found, and the pixel value (i.e., color data) of that pixel is added to the acquisition data of that point cloud, thereby achieving the fusion of point cloud data and image data.

[0084] S230. Divide the collected point cloud into at least one data packet point cloud according to the preset data packet volume.

[0085] It should be noted that since the LiDAR and camera collect data in real time while the mobile device is moving, this embodiment needs to divide the collected point cloud into several groups for processing based on a preset data packet volume to facilitate data storage and transmission. Each group of point clouds can be considered a data packet point cloud. The preset data packet volume can be the pre-set size of a data packet point cloud. Specifically, it can be the maximum number of point clouds that a data packet point cloud can contain.

[0086] Specifically, the collected point cloud can be divided into data packet point clouds containing at least one data packet of the preset data packet size, based on the preset data packet size. For example, the preset data packet size can be set to contain a maximum of 5000 point cloud data points. If the number of collected point cloud data points exceeds 5000, then every 5000 collected point cloud data points are grouped together, and each group of collected point cloud data points constitutes one data packet point cloud. The threshold for the number of data points in each data packet point cloud group is 5000.

[0087] S240. Based on the preset voxel size, perform octree segmentation on the point cloud of each data packet to obtain several point cloud blocks after octree segmentation.

[0088] The preset voxel size can be the preset size of the point cloud block after octree segmentation. The preset voxel size is pre-set.

[0089] Octree segmentation is a process based on octree segmentation theory, which divides each group of data packets into several point cloud blocks according to a preset voxel size. Optionally, the sizes of the resulting point cloud blocks may be the same or different.

[0090] S250. Acquire the collected data of each point cloud in several point cloud blocks after octree segmentation.

[0091] S260. Determine the compression feature information of the collected data based on the attribute type of the collected data.

[0092] S270. Based on the compression feature information, the collected data is compressed to obtain compressed data of the collected data of several point cloud blocks.

[0093] The technical solution of this invention acquires point cloud data from a lidar radar and image data from a camera; fuses the point cloud data and image data to obtain acquired point cloud data; divides the acquired point cloud into at least one data packet point cloud according to a preset data packet volume; performs octree segmentation on each data packet point cloud based on a preset voxel size to obtain several point cloud blocks after octree segmentation; acquires the acquired data of each point cloud in the several point cloud blocks after octree segmentation; determines the compression feature information of the acquired data according to the attribute type of the acquired data; and compresses the acquired data according to the compression feature information to obtain compressed data of the acquired point cloud block data. Through preprocessing of the acquired data, several point cloud blocks fused in the same coordinate system are obtained, providing conditions for point cloud data compression processing and facilitating subsequent processing of the point cloud data according to attribute type.

[0094] Accordingly, according to any of the methods described in the embodiments of the present invention, after obtaining compressed data of the collected data of several point cloud blocks, the point cloud data processing method further includes:

[0095] Output compressed data of the collected point cloud blocks.

[0096] Optionally, the compressed data of the point cloud block acquisition can be output from the data acquisition end of one mobile device to the data user end of another device, or it can be output from the data acquisition end of the same mobile device to the data user end.

[0097] By outputting compressed data of the collected point cloud blocks, the transmission process of compressed point cloud data is realized, ensuring the subsequent reception and decompression processing of point cloud data.

[0098] Example 3

[0099] Figure 3 This is a flowchart of a point cloud data processing method provided in Embodiment 3 of the present invention. This embodiment is applicable to the decompression processing of compressed data of various types of point cloud acquisition data. The method can be executed by a point cloud data processing device, which can be implemented in hardware and / or software and can be integrated into the electronic device of the data user.

[0100] like Figure 3 As shown, the method specifically includes:

[0101] S310. Obtain compressed data of collected point cloud blocks after octree segmentation.

[0102] The process of acquiring compressed data from the collected data can be achieved through communication between the data acquisition end and the data user end. The data acquisition end outputs compressed data from the collected data to the data user end, and the data user end acquires the compressed data from the collected data.

[0103] It should be noted that the process of compressing the collected data of several point cloud blocks after octree segmentation has been described in detail in the above embodiments and will not be repeated here.

[0104] S320. Determine the decompression characteristic information of the compressed data based on the attribute type of the compressed data.

[0105] Among them, decompression feature information can be the feature information referenced when decompressing the compressed data of the collected data.

[0106] Optionally, different attribute types of compressed data from different collected data correspond to different decompression feature information. Once the attribute type of the compressed data from the collected data is determined, the decompression feature information to be selected can be determined. Optionally, in this embodiment, different rules for determining decompression feature information can be pre-set for compressed data from different attribute types based on the characteristics of the data information. For example, the rules for determining decompression feature information can be determined based on whether the data information of each attribute type has a pattern or whether it is distributed within a certain range.

[0107] Specifically, this can be achieved through the following sub-steps:

[0108] S3201. Determine whether the compressed data collected belongs to the first type of compressed data or the second type of compressed data. If it belongs only to the first type of compressed data, execute S3202. If it belongs only to the second type of compressed data, execute S3203. If part of it belongs to the first type of compressed data and the other part belongs to the second type of compressed data, then execute S3202 for the part belonging to the first type of compressed data and execute S3203 for the part belonging to the second type of compressed data.

[0109] The first type of compressed data can be irregular compressed data of acquired data with relatively close differences, and may include, but is not limited to, compressed data of location data and / or compressed data of time data; for example, it may also include compressed data of echo counts, etc. The second type of compressed data can be compressed data of acquired data with large differences within a certain threshold range, and may include, but is not limited to, compressed data of intensity data and / or compressed data of color data.

[0110] It should be noted that the compressed data of location, time, echo count, intensity, and color are data that have been compressed by the data acquisition end.

[0111] S3202. If the compressed data contains compressed data of the first type of data, then the decompression feature information of the compressed data of the first type of data is determined as the center point of the point cloud block.

[0112] Specifically, if it is determined that the compressed data of the collected data of each point cloud in the point cloud block contains compressed data of the first type of data, then the first type of data corresponding to the center point of the point cloud block is determined as the decompression feature information of the compressed data of the first type of data of the point cloud block.

[0113] S3203. If the compressed data contains compressed data of the second type of data, then the decompression characteristic information of the compressed data of the second type of data is determined to be the preset bit depth decompression ratio.

[0114] The bit depth decompression ratio is the reciprocal of the bit depth compression ratio.

[0115] Specifically, if it is determined that the compressed data contains compressed data of the second type, then the preset bit depth decompression ratio is determined as the decompression characteristic information of the compressed data of the second type.

[0116] It should be noted that, in the technical solution of this embodiment, the compressed data of the first type of data and the compressed data of the second type of data are two parallel types of data corresponding to different attribute types of the compressed data of the collected data.

[0117] This solution targets compressed data of different attribute types and selects different decompression feature information based on their data characteristics to decompress compressed data of each attribute type, further improving the flexibility and accuracy of compressed data decompression.

[0118] S330. Based on the decompression feature information, decompress the compressed data to obtain the collected data of each point cloud in several point cloud blocks.

[0119] Optionally, decompressing the compressed data can be performed by determining the corresponding decompression method based on different decompression characteristics, and then decompressing the point cloud data based on the corresponding decompression method. The specific implementation process is as follows:

[0120] If the decompression feature information is the center point of the point cloud block, then the compressed data of the first type of data of each point cloud in the point cloud block and the compressed data of the first type of data corresponding to the center point are respectively subjected to the second bit operation, and the result of the second bit operation and the compressed data of the first type of data corresponding to the center point are used as the first type of data of each point cloud in the point cloud block.

[0121] The second bitwise operation is the inverse operation of the first bitwise operation in the above embodiment. The first type of data can be the result of decompressing the compressed data of the first type of data.

[0122] Specifically, in this embodiment, for each point cloud in a point cloud block, the inverse of the compressed data of the first type of data for each point cloud is calculated, and then summed with the compressed data of the first type of data corresponding to the center point. This completes the second bitwise operation between the compressed data of the first type of data of each point cloud and the compressed data of the first type of data corresponding to the center point. Then, the result of the second bitwise operation for each point cloud and the compressed data of the first type of data corresponding to the center point are used as the first type of data for that point cloud block.

[0123] For example, if the compressed data of the first type of data contains compressed data of location data (i.e., the difference in geometric position coordinates of point clouds), the negative number of the corresponding geometric position coordinate difference of each point cloud in the point cloud block can be calculated by the second bit operation, and then summed with the compressed data of the location data corresponding to the center point. The result of the summation of the geometric position differences corresponding to each point cloud, along with the geometric position coordinates corresponding to the center point, is used as the location data in the compressed data of the first type of data.

[0124] If the decompression feature information is a preset bit depth decompression ratio, then according to the preset bit depth decompression ratio, the compressed data of the second type of data of each point cloud in the point cloud block is subjected to bit depth decompression processing, and the bit depth decompression result is used as the second type of data of each point cloud in the point cloud block.

[0125] Specifically, bit-depth decompression is the inverse operation of bit-depth compression. For example, if the compressed data of the second type of data is compressed color data, the space occupied by a single channel of the compressed color data is 4 bits. The preset bit-depth decompression ratio is 1 / 2. If it is determined that the compressed data contains compressed color data, then the preset bit-depth decompression ratio is used as decompression feature information. The compressed color data is decompressed according to the bit-depth decompression ratio to obtain the bit-depth decompression result of the compressed color data. This bit-depth decompression result is then used as the color data, and the space occupied by a single channel of the color data is decompressed from 4 bits to 8 bits.

[0126] The technical solution of this invention obtains compressed data from a number of point cloud blocks processed by octree segmentation; determines decompression feature information corresponding to different attribute types of compressed data based on the attribute types of the compressed data; and then decompresses the compressed data of each attribute type based on the decompression feature information. This solution can support decompression processing of compressed data of various attribute types, and uses different decompression feature information for different attribute types of compressed data, improving the flexibility and accuracy of the compressed data decompression method. Furthermore, the decompression method corresponds to the compression method, resulting in more complete and accurate data information after decompression.

[0127] According to any of the methods described in the embodiments of the present invention, after obtaining the acquisition data of each point cloud in a plurality of point cloud blocks, the point cloud data processing method further includes: performing subsequent processing on the acquisition data of each point cloud, wherein the subsequent processing includes at least one of rendering, storage, format conversion and output.

[0128] Rendering can be the process of loading decompressed acquired data into rendering software and displaying the composite image as true-color point cloud data in real time; or the process of rendering false-color point cloud data using other attribute information (such as intensity data, time data, or elevation values). Elevation values ​​can be the extreme values ​​and median values ​​of the point cloud data along the Z-axis, and color rendering of the point cloud data can be achieved using these elevation values.

[0129] Storage can be the process of storing the decompressed collected data in the storage module of a mobile device.

[0130] Format conversion can be the process of converting decompressed acquired data from point cloud data format to other data formats. Specifically, it can be converting point cloud data into image data. Optional formats may include: ASCII, BIN, LAS, PLY, or PCD types, etc.

[0131] Output can include directly outputting the decompressed acquisition data, outputting rendered point cloud data, or outputting point cloud data after format conversion, etc.

[0132] By performing subsequent processing on the point cloud data, the point cloud data storage is completed, making it convenient to call the point cloud data later. The rendering, format conversion, or output display of the point cloud data can adapt to different formats and display requirements, facilitating the subsequent use of the point cloud data.

[0133] Example 4

[0134] Figure 4a This is a signaling flowchart of a point cloud data processing method provided in Embodiment 4 of the present invention. Figure 4b This is an application scenario diagram of a point cloud data processing method provided in Embodiment 4 of the invention. Based on the above embodiments, this embodiment provides a preferred example of a data acquisition end and a data usage end cooperating to perform point cloud data processing. For example... Figures 4a-4b As shown, the processing method for this point cloud data includes:

[0135] S401, the data acquisition terminal acquires point cloud data from the lidar and image data from the camera.

[0136] Optionally, the data acquisition terminal in this embodiment can be integrated into the lidar. For example, such as... Figure 4b As shown, electronic devices can be airborne devices, such as drones.

[0137] S402. The data acquisition terminal performs fusion processing on the point cloud data and image data to obtain the acquired point cloud data.

[0138] For example, such as Figure 4b As shown, the airborne equipment is equipped with lidar and cameras to collect data. Before point cloud data compression, the point cloud data is fused. The point cloud data processed by octree segmentation is the fused point cloud data.

[0139] S403. The data acquisition terminal divides the acquired point cloud into at least one data packet point cloud according to the preset data packet volume.

[0140] S404. The data acquisition end performs octree segmentation on the point cloud of each data packet based on the preset voxel size to obtain point cloud blocks after octree segmentation.

[0141] S405. The data acquisition terminal acquires the data of each point cloud in several point cloud blocks after octree segmentation.

[0142] S406. The data acquisition terminal determines the compression feature information of the acquired data based on the attribute type of the acquired data.

[0143] S407. The data acquisition end compresses the acquired data according to the compression feature information to obtain compressed data of the point cloud block.

[0144] For example, such as Figure 4b As shown, the compressed data collected can be compressed point cloud data.

[0145] S408, The data acquisition end outputs the compressed data of the acquired data to the data user end.

[0146] S409. The data user obtains compressed data of collected point cloud blocks after octree segmentation.

[0147] For example, such as Figure 4b As shown, the electronic device at the data user end can be a ground-based device.

[0148] S410. The data user determines the decompression characteristic information of the compressed data based on the attribute type of the compressed data.

[0149] S411. The data user terminal decompresses the compressed data according to the decompression feature information to obtain the collected data of each point cloud in the point cloud block.

[0150] For example, such as Figure 4b As shown, the collected data can be the decompressed data.

[0151] S412. The data user performs subsequent processing on the collected data.

[0152] For example, such as Figure 4b As shown, subsequent processing can include data rendering and data saving. Data rendering can output both true-color and false-color point cloud data.

[0153] The technical solution of this invention realizes the compression and decompression of point cloud data through the interactive use of the data acquisition end and the data user end. At the same time, different compression and decompression methods are adopted for point cloud data with different attribute types, which improves the flexibility and accuracy of point cloud data compression. Furthermore, the data information compressed in this way is easier to store, transmit and use.

[0154] Example 5

[0155] Figure 5This is a schematic diagram of a point cloud data processing device provided in Embodiment 5 of the present invention. This device can implement the point cloud data processing methods provided in Embodiments 1, 2, or 4 of the present invention. This embodiment is applicable to situations where various types of collected point cloud data are compressed, especially for situations where various types of collected point cloud data acquired in real-time are compressed. This device can execute point cloud data processing methods and can be implemented in hardware and / or software. The device can be configured in an electronic device at a data acquisition end, which can also be equipped with a LiDAR and a camera for acquiring point cloud data in various dimensions. Furthermore, it can acquire and compress the collected data from the LiDAR and camera in real time.

[0156] like Figure 5 As shown, the device includes:

[0157] The data acquisition module 510 is used to acquire the data of each point cloud in several point cloud blocks after octree segmentation.

[0158] The compression feature determination module 520 is used to determine the compression feature information of the collected data based on the attribute type of the collected data;

[0159] The data compression module 530 is used to compress the collected data according to the compression feature information to obtain compressed data of the collected data of several point cloud blocks.

[0160] The technical solution of this invention acquires the collected data of each point cloud in several point cloud blocks after octree segmentation; determines the compression feature information corresponding to different attribute types of collected data according to the attribute type of the collected data; and then compresses the collected data of that attribute type based on the compression feature information of each attribute type. This solution can support the compression processing of collected data of multiple different attribute types, and uses different compression feature information for different attribute types of collected data, improving the flexibility and accuracy of the collected data compression method. Furthermore, the data information compressed in this way is easier to store and transmit.

[0161] Optionally, the compression feature determination module 520 can be used for:

[0162] If the collected data contains the first type of data, then the compressed feature information of the first type of data is determined as the center point of the point cloud block; wherein, the first type of data includes location data and / or time data;

[0163] If the collected data contains the second type of data, then the compression feature information of the second type of data is determined to be the preset bit depth compression ratio; wherein, the second type of data includes intensity data and / or color data.

[0164] Optionally, the data compression module 530 can be used for:

[0165] If the compressed feature information is the center point of the point cloud block, then perform the first bit operation on the first type data of each point cloud in the point cloud block and the first type data corresponding to the center point, and use the result of the first bit operation and the first type data corresponding to the center point as the compressed data of the first type data of the point cloud block.

[0166] If the compression feature information is a preset bit depth compression ratio, then the second type of data of each point cloud in the point cloud block is processed by bit depth compression according to the preset bit depth compression ratio, and the bit depth compression result is used as the compressed data of the second type of data of the point cloud block.

[0167] Correspondingly, the point cloud data processing device also includes:

[0168] The data acquisition module is used to acquire point cloud data from the LiDAR and image data from the camera; wherein the LiDAR and the camera are installed on the same mobile device;

[0169] The data fusion module is used to fuse point cloud data and image data to obtain the collected point cloud data.

[0170] The data packet segmentation module is used to divide the collected point cloud into at least one data packet point cloud according to the preset data packet volume;

[0171] The point cloud block acquisition module is used to perform octree segmentation on the point cloud of each data packet based on a preset voxel size, so as to obtain several point cloud blocks after octree segmentation.

[0172] Correspondingly, the point cloud data processing device also includes:

[0173] The compressed data output module is used to output compressed data of the collected point cloud blocks.

[0174] The point cloud data processing apparatus provided in the embodiments of the present invention can execute the point cloud data processing method provided in the above-described embodiments one, two or four of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0175] Example 6

[0176] Figure 6 This is a schematic diagram of a point cloud data processing device provided in Embodiment Six of the present invention. This device can implement the point cloud data processing method provided in Embodiments Three or Four of the present invention. This embodiment is applicable to the decompression of various types of compressed point cloud data. The device can execute the point cloud data processing method and can be implemented in hardware and / or software. This point cloud data processing device can be integrated into the electronic device used by the data user.

[0177] like Figure 6 As shown, the device includes:

[0178] The compressed data acquisition module 610 is used to acquire compressed data of collected data of several point cloud blocks after octree segmentation.

[0179] The decompression feature determination module 620 is used to determine the decompression feature information of the compressed data based on the attribute type of the compressed data;

[0180] The data decompression module 630 is used to decompress compressed data according to decompression feature information to obtain the collected data of each point cloud in the point cloud block.

[0181] The technical solution of this invention obtains compressed data of collected point cloud blocks processed by octree segmentation; determines decompression feature information of compressed data according to the attribute type of compressed data; and decompresses the compressed data according to the decompression feature information to obtain the collected data of each point cloud in the point cloud block, thus completing the decompression process of point cloud data. Furthermore, for different data types, the decompression process and the compression process are inverse operations, ensuring the accuracy of point cloud data decompression and avoiding situations where decompression is impossible due to data loss, thereby ensuring the stability of point cloud data decompression.

[0182] Optionally, the decompression feature determination module 620 can be used for:

[0183] If the compressed data contains compressed data of the first type of data, then the decompression feature information of the compressed data of the first type of data is determined as the center point of the point cloud block; wherein, the compressed data of the first type of data includes compressed data of location data and / or compressed data of time data;

[0184] If the compressed data of the collected data includes compressed data of the second type of data, then the decompression feature information of the compressed data of the second type of data is determined to be the preset bit depth decompression ratio; wherein, the compressed data of the second type of data includes compressed data of intensity data and / or compressed data of color data.

[0185] Optionally, the data decompression module 630 can be used for:

[0186] If the decompression feature information is the center point of the point cloud block, then the compressed data of the first type of data of each point cloud in the point cloud block and the compressed data of the first type of data corresponding to the center point are respectively subjected to the second bit operation, and the result of the second bit operation and the compressed data of the first type of data corresponding to the center point are used as the first type of data of each point cloud in the point cloud block.

[0187] If the decompression feature information is a preset bit depth decompression ratio, then according to the preset bit depth decompression ratio, the compressed data of the second type of data of each point cloud in the point cloud block is subjected to bit depth decompression processing, and the bit depth decompression result is used as the second type of data of each point cloud in the point cloud block.

[0188] Correspondingly, the point cloud data processing device also includes:

[0189] The data post-processing module is used to perform post-processing on the collected data of each point cloud. The post-processing includes at least one of rendering, storage, format conversion and output.

[0190] The point cloud data processing apparatus provided in this embodiment of the invention can execute the point cloud data processing method provided in Embodiments 3 or 4 above, and has the corresponding functional modules and beneficial effects of the method.

[0191] Example 7

[0192] Figure 7 This is a schematic diagram of the structure of an electronic device provided in Embodiment 8 of the present invention. Figure 7 A block diagram is shown that is suitable for implementing embodiments of the present invention. Figure 7 The device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0193] like Figure 7 As shown, the electronic device 700 is presented in the form of a general-purpose computing device. The components of the electronic device 700 may include, but are not limited to: one or more processors or processing units 710, system memory 720, and bus 730 connecting different system components (including system memory 720 and processing unit 710).

[0194] Bus 730 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0195] Electronic device 700 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 700, including volatile and non-volatile media, removable and non-removable media.

[0196] System memory 720 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 721 and / or cache memory (cache 722). Electronic device 700 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 723 may be used to read and write non-removable, non-volatile magnetic media (… Figure 7 Not shown; usually referred to as a "hard drive"). Although Figure 7 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 730 via one or more data media interfaces. System memory 720 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present invention.

[0197] A program / utility 725 having a set (at least one) of program modules 724 may be stored, for example, in system memory 720. Such program modules 724 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 724 typically perform the functions and / or methods described in the embodiments of this invention.

[0198] Electronic device 700 can also communicate with one or more external devices 800 (e.g., keyboard, pointing device, display 810, etc.), and with one or more devices that enable a user to interact with electronic device 700, and / or with any device that enables electronic device 700 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 740. Furthermore, electronic device 700 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 750. As shown, network adapter 750 communicates with other modules of electronic device 700 via bus 730. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0199] The processing unit 710 executes various functional applications and data processing by running programs stored in the system memory 720, such as implementing the point cloud data processing method provided in the embodiments of the present invention.

[0200] Example 8

[0201] Embodiment 8 of the present invention also provides a computer-readable storage medium storing a computer program (or computer-executable instructions) thereon, which is used by a processor to perform the point cloud data processing method provided in the embodiments of the present invention.

[0202] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0203] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0204] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0205] Computer program code for performing the operations of embodiments of the present invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0206] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the embodiments of the present invention have been described in detail above, the embodiments of the present invention are not limited to the above embodiments. Many other equivalent embodiments may be included without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for processing point cloud data, characterized in that, include: Acquire the collected data of each point cloud in several point cloud blocks after octree segmentation; Based on the attribute type of the collected data, determine the compression feature information of the collected data; Based on the compression feature information, the collected data is compressed to obtain compressed data of the collected data of the plurality of point cloud blocks; The compression process of the point cloud data adopts an unstructured compression encoding method; Before acquiring the collected data of each point cloud in several point cloud blocks after octree segmentation, the process includes: Acquire point cloud data from a lidar and image data from a camera; wherein the lidar and the camera are installed on the same mobile device; The step of determining the compression feature information of the collected data based on the attribute type of the collected data includes: If the collected data includes the first type of data, then the compressed feature information of the first type of data is determined as the center point of each point cloud block; wherein, the first type of data includes location data and time data; If the collected data contains the second type of data, then the compression feature information of the second type of data is determined to be a preset bit depth compression ratio; wherein, the second type of data includes intensity data and color data; The step of compressing the acquired data according to the compression feature information to obtain compressed data of the acquired data of the plurality of point cloud blocks includes: If the compressed feature information is the center point of the point cloud block, then the first type of data of each point cloud in the point cloud block and the first type of data corresponding to the center point are respectively subjected to the first bit operation, and the result of the first bit operation and the first type of data corresponding to the center point are used as the compressed data of the first type of data of the point cloud block. If the compression feature information is a preset bit depth compression ratio, then the second type of data of each point cloud in the point cloud block is subjected to bit depth compression processing according to the preset bit depth compression ratio, and the bit depth compression result and the preset bit depth compression ratio are used as the compressed data of the second type of data of the point cloud block.

2. The method according to claim 1, characterized in that, Before acquiring the point cloud data from several point cloud blocks after octree segmentation, the process also includes: The point cloud data and the image data are fused to obtain the collected point cloud data; The collected point cloud is divided into at least one data packet point cloud according to the preset data packet volume; Based on the preset voxel size, the point cloud of each data packet is divided into several point cloud blocks by an octree.

3. The method according to claim 1 or 2, characterized in that, After obtaining the compressed data of the collected data of the aforementioned point cloud blocks, the system further includes: Output compressed data of the collected data of the aforementioned point cloud blocks.

4. A method for processing point cloud data, characterized in that, include: Obtain compressed data of collected point cloud blocks after octree segmentation; Based on the attribute type of the compressed data, determine the decompression feature information of the compressed data; Based on the decompression feature information, the compressed data is decompressed to obtain the collected data of each point cloud in the plurality of point cloud blocks; The decompression process of the point cloud data adopts an unstructured decompression method corresponding to the unstructured compression encoding method; The step of determining the decompression feature information of the compressed data based on the attribute type of the compressed data includes: If the compressed data contains compressed data of the first type of data, then the decompression feature information of the compressed data of the first type of data is determined as the center point of the point cloud block; wherein, the compressed data of the first type of data includes compressed data of location data and compressed data of time data; the first type of data includes location data and / or time data; If the compressed data contains compressed data of the second type of data, then the decompression feature information of the compressed data of the second type of data is determined to be a preset bit depth decompression ratio; wherein, the compressed data of the second type of data includes compressed data of intensity data and compressed data of color data; the second type of data includes intensity data and / or color data; Based on the decompression feature information, the compressed data is decompressed to obtain the collected data of each point cloud in the plurality of point cloud blocks, including: If the decompression feature information is the center point of the point cloud block, then the compressed data of the first type of data of each point cloud in the point cloud block and the first type of data corresponding to the center point are respectively subjected to a second bit operation, and the result of the second bit operation is used as the first type of data of each point cloud in the point cloud block. If the decompression feature information is a preset bit depth decompression ratio, then according to the preset bit depth decompression ratio, the second type of data of each point cloud in the point cloud block is subjected to bit depth decompression processing, and the bit depth decompression result is used as the second type of data of each point cloud in the point cloud block.

5. The method according to claim 4, characterized in that, After obtaining the collected data of each point cloud in the aforementioned point cloud blocks, the process also includes: The collected data of each point cloud are then processed, including at least one of rendering, storage, format conversion, and output.

6. A point cloud data processing device, characterized in that, include: The data acquisition module is used to acquire the data of each point cloud in several point cloud blocks after octree segmentation. A compression feature determination module is used to determine the compression feature information of the collected data based on the attribute type of the collected data; The data compression module is used to compress the collected data according to the compression feature information to obtain compressed data of the collected data of the plurality of point cloud blocks; The compression process of the point cloud data adopts an unstructured compression encoding method; A data acquisition module is used to acquire point cloud data from a lidar and image data from a camera; wherein the lidar and the camera are installed on the same mobile device; The compression feature determination module is used for: If the collected data contains the first type of data, then the compressed feature information of the first type of data is determined as the center point of the point cloud block; wherein, the first type of data includes location data and time data; If the collected data contains the second type of data, then the compression feature information of the second type of data is determined to be a preset bit depth compression ratio; wherein, the second type of data includes intensity data and color data; The data compression module is used for: If the compressed feature information is the center point of the point cloud block, then the first type of data of each point cloud in the point cloud block and the first type of data corresponding to the center point are respectively subjected to the first bit operation, and the result of the first bit operation and the first type of data corresponding to the center point are used as the compressed data of the first type of data of the point cloud block. If the compression feature information is a preset bit depth compression ratio, then the second type of data of each point cloud in the point cloud block is processed by bit depth compression according to the preset bit depth compression ratio, and the bit depth compression result is used as the compressed data of the second type of data of the point cloud block.

7. A point cloud data processing device, characterized in that, include: The compressed data acquisition module is used to acquire compressed data of collected data from several point cloud blocks that have been processed by octree segmentation; The decompression feature determination module is used to determine the decompression feature information of the compressed data based on the attribute type of the compressed data; The data decompression module is used to decompress the compressed data according to the decompression feature information to obtain the collected data of each point cloud in the plurality of point cloud blocks; The decompression process of the point cloud data adopts an unstructured decompression method corresponding to the unstructured compression encoding method; The decompression feature determination module is used for: If the compressed data contains compressed data of the first type of data, then the decompression feature information of the compressed data of the first type of data is determined as the center point of the point cloud block; wherein, the compressed data of the first type of data includes compressed data of location data and compressed data of time data; If the compressed data of the collected data contains compressed data of the second type of data, then the decompression feature information of the compressed data of the second type of data is determined to be a preset bit depth decompression ratio; wherein, the compressed data of the second type of data includes compressed data of intensity data and compressed data of color data; The data decompression module is used for: If the decompression feature information is the center point of the point cloud block, then the compressed data of the first type of data of each point cloud in the point cloud block and the compressed data of the first type of data corresponding to the center point are respectively subjected to a second bit operation, and the result of the second bit operation and the compressed data of the first type of data corresponding to the center point are used as the first type of data of each point cloud in the point cloud block. If the decompression feature information is a preset bit depth decompression ratio, then according to the preset bit depth decompression ratio, the compressed data of the second type of data of each point cloud in the point cloud block is subjected to bit depth decompression processing, and the bit depth decompression result is used as the second type of data of each point cloud in the point cloud block.

8. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method for processing point cloud data as described in any one of claims 1-3 or 4-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the method for processing point cloud data as described in any one of claims 1-3 or 4-5.

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