A pipeline facility reconstruction and identification method based on 3D laser point cloud

Abstract

The present invention discloses a pipeline facility reconstruction and identification method based on three-dimensional laser point clouds, wherein the method obtains three-dimensional point cloud data to be reconstructed, and performs sampling preprocessing on the three-dimensional point cloud data to obtain sampled point cloud data and first mapping information; the sampled point cloud data is combined and divided into blocks to obtain a plurality of point cloud sample blocks and second mapping information, wherein the number of point clouds in each point cloud sample block is the same, and the sample points in each point cloud sample block are evenly distributed in the point cloud space of the sampled point cloud data; based on the second mapping information, the point cloud sample blocks are reconstructed through a point cloud classification model to obtain point cloud reconstruction data; based on the first mapping information, the point cloud reconstruction data is sampled and restored to obtain a target point cloud reconstruction result. This method can effectively reduce the complexity of point cloud data processing, reduce the computing resources required for point cloud reconstruction, and improve the point cloud reconstruction effect and accuracy. The present invention relates to the field of point cloud data processing technology.

Description

Technical Field

[0001] The present invention relates to the technical field of point cloud data processing, and in particular to a pipeline facility reconstruction and recognition method based on three-dimensional laser point cloud. Background Art

[0002] With the construction and development of modern cities, laser 3D point clouds can quickly record the latest spatial status of urban underground pipeline facilities in key tasks such as urban planning, construction, and maintenance. Therefore, how to quickly identify and segment underground facility targets in complex scenes based on the massive amount of point cloud data scanned by LiDAR has become an urgent issue to be solved, providing a key input source for the next step of 3D information extraction.

[0003] At present, before reconstructing underground pipelines through point cloud data, it is necessary to perform object recognition on the point cloud data objects (i.e., data semantic segmentation). For semantic segmentation of point cloud data, traditional segmentation methods have their limitations. It is difficult to complete the semantic segmentation task of point cloud data with huge point cloud quantities and complex scenes. Specifically, traditional point cloud data segmentation methods can be roughly divided into two types. The first is a plane-based block segmentation method. This segmentation method often cannot effectively capture the overall geometric structure of the object, which makes the point cloud reconstruction effect unsatisfactory; the second is a probability-based random segmentation method. The point cloud data processing of this method is more complicated, the required computing resources are more, and the point cloud reconstruction accuracy is not high.

[0004] Therefore, the problems existing in the existing technology still need to be solved and optimized. Summary of the Invention

[0005] The purpose of the present invention is to solve one of the technical problems existing in the related art to at least a certain extent.

[0006] To this end, one purpose of an embodiment of the present invention is to provide a pipeline facility reconstruction and identification method based on three-dimensional laser point cloud, wherein the method can effectively reduce the complexity of point cloud data processing, reduce the computing resources required for point cloud reconstruction, and improve the point cloud reconstruction effect and accuracy.

[0007] In order to achieve the above technical objectives, the technical solutions adopted in the embodiments of the present application include:

[0008] In a first aspect, an embodiment of the present application provides a pipeline facility reconstruction and identification method based on a three-dimensional laser point cloud, comprising:

[0009] Acquiring three-dimensional point cloud data to be reconstructed, and performing sampling preprocessing on the three-dimensional point cloud data to obtain sampled point cloud data and first mapping information, wherein the first mapping information is used to represent a point cloud mapping relationship between the three-dimensional point cloud data before sampling and the three-dimensional point cloud data after sampling;

[0010] performing a combined block processing on the sampled point cloud data to obtain a plurality of point cloud sample blocks and second mapping information, wherein each of the point cloud sample blocks has the same number of point clouds, and the sample points in each of the point cloud sample blocks are evenly distributed within the point cloud space of the sampled point cloud data, and the second mapping information is used to represent a point cloud mapping relationship between the point cloud sample blocks and the sampled point cloud data;

[0011] Based on the second mapping information, reconstructing the point cloud sample block using a trained point cloud classification model to obtain point cloud reconstruction data;

[0012] The point cloud reconstruction data is sampled and restored according to the first mapping information to obtain a target point cloud reconstruction result.

[0013] In addition, the method according to the above embodiment of the present application may also have the following additional technical features:

[0014] Furthermore, in one embodiment of the present application, the sampling and pre-processing of the three-dimensional point cloud data to obtain the sampled point cloud data and the first mapping information includes:

[0015] Performing data cleaning on the three-dimensional point cloud data to obtain first point cloud data;

[0016] Performing three-dimensional grid division on the first point cloud data to obtain second point cloud data;

[0017] Grid sampling is performed on the second point cloud data to obtain the sampled point cloud data and the first mapping information.

[0018] Furthermore, in one embodiment of the present application, the combining and blocking processing of the sampled point cloud data to obtain a plurality of point cloud sample blocks and second mapping information includes:

[0019] Sampling the farthest point of the point cloud on the sampled point cloud data to obtain third point cloud data;

[0020] According to the sampled point cloud data, point cloud neighbor classification is performed on the third point cloud data to obtain a plurality of the point cloud sample blocks and the second mapping information.

[0021] Furthermore, in one embodiment of the present application, the step of sampling the farthest point of the point cloud on the sampled point cloud data to obtain third point cloud data includes:

[0022] Determining a target sampling quantity based on the sampling point cloud data;

[0023] According to the first point cloud sampling sequence, the remaining sampling point cloud data is screened for maximum distance to obtain the distance optimal point;

[0024] According to the optimal distance point, updating the sampling points of the first point cloud sampling sequence to obtain a second point cloud sampling sequence;

[0025] Comparing the target sampling number with the number of sampling points in the second point cloud sampling sequence to obtain a sampling comparison result;

[0026] If the sampling comparison result is that the number of sampling points in the second point cloud sampling sequence is less than the target sampling number, the first point cloud sampling sequence is updated according to the second point cloud sampling sequence, and then the process returns to the step of performing maximum distance screening on the remaining sampling point cloud data after sampling according to the first point cloud sampling sequence to obtain the distance optimum point; alternatively, if the sampling comparison result is that the number of sampling points in the second point cloud sampling sequence is equal to the target sampling number, the second point cloud sampling sequence is determined as the third point cloud data.

[0027] Furthermore, in one embodiment of the present application, performing point cloud neighbor classification on the third point cloud data according to the sampled point cloud data to obtain a plurality of the point cloud sample blocks and the second mapping information includes:

[0028] Obtaining a tree structure corresponding to the sampling point cloud data and a preset number of sample points;

[0029] According to the number of sample points and the third point cloud data, a neighbor point search is performed on the tree structure to obtain a plurality of the point cloud sample blocks and the second mapping information.

[0030] Furthermore, in one embodiment of the present application, the step of performing point cloud reconstruction on the point cloud sample block using a trained point cloud classification model based on the second mapping information to obtain point cloud reconstruction data includes:

[0031] Inputting the point cloud sample block into the trained point cloud classification model to perform point cloud classification prediction to obtain a point cloud classification result;

[0032] Performing index averaging on the point cloud classification results according to the second mapping information to obtain a reconstruction prediction result;

[0033] The category probability value corresponding to each sample point of the reconstruction prediction result is screened for the maximum value to obtain the point cloud reconstruction data.

[0034] Furthermore, in the embodiment of the present application, the trained point cloud classification model is obtained by training through the following steps:

[0035] Get training sample blocks;

[0036] Performing data enhancement and normalization on the training sample block to obtain a target sample block;

[0037] The target sample block is input into the initialized point cloud classification model for training to obtain the trained point cloud classification model.

[0038] In a second aspect, an embodiment of the present application provides a pipeline facility reconstruction and identification system based on a three-dimensional laser point cloud, comprising:

[0039] a first processing unit, configured to acquire three-dimensional point cloud data to be reconstructed, and perform sampling preprocessing on the three-dimensional point cloud data to obtain sampled point cloud data and first mapping information, wherein the first mapping information is used to represent a point cloud mapping relationship between the three-dimensional point cloud data before sampling and the three-dimensional point cloud data after sampling;

[0040] a second processing unit, configured to perform combined block processing on the sampled point cloud data to obtain a plurality of point cloud sample blocks and second mapping information, wherein each of the point cloud sample blocks has the same number of point clouds, and the sample points in each of the point cloud sample blocks are uniformly distributed within the point cloud space of the sampled point cloud data, and the second mapping information is used to represent a point cloud mapping relationship between the point cloud sample blocks and the sampled point cloud data;

[0041] a third processing unit, configured to perform point cloud reconstruction on the point cloud sample block using a trained point cloud classification model based on the second mapping information to obtain point cloud reconstruction data;

[0042] The fourth processing unit is used to sample and restore the point cloud reconstruction data according to the first mapping information to obtain a target point cloud reconstruction result.

[0043] In a third aspect, an embodiment of the present application further provides an electronic device, including:

[0044] at least one processor;

[0045] at least one memory for storing at least one program;

[0046] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0047] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores a program executable by a processor, and the program executable by the processor is used to implement the above method when executed by the processor.

[0048] The advantages and benefits of this application will be partially given in the following description, and partially become apparent from the following description, or learned through practice of this application:

[0049] The embodiment of the present application discloses a pipeline facility reconstruction and identification method based on three-dimensional laser point cloud, wherein the method obtains three-dimensional point cloud data to be reconstructed, and performs sampling preprocessing on the three-dimensional point cloud data to obtain sampled point cloud data and first mapping information, wherein the first mapping information is used to characterize the point cloud mapping relationship between the three-dimensional point cloud data before sampling and the three-dimensional point cloud data after sampling; the sampled point cloud data is combined and divided into blocks to obtain a plurality of point cloud sample blocks and second mapping information, the number of point clouds in each point cloud sample block is the same, and the sample points in each point cloud sample block are uniformly distributed in the point cloud space of the sampled point cloud data, and the second mapping information is used to characterize the point cloud mapping relationship between the point cloud sample block and the sampled point cloud data; based on the second mapping information, the point cloud sample block is point cloud reconstructed by a trained point cloud classification model to obtain point cloud reconstruction data; according to the first mapping information, the point cloud reconstruction data is sampled and restored to obtain a target point cloud reconstruction result. This method is based on the combined blocking of sampled point cloud data, and can obtain point cloud sample blocks with sample points evenly distributed in the point cloud space. The number of sample points in each point cloud sample block is the same, which can avoid the randomness problem in the selection of sampling points. The complexity of point cloud data processing is low, and the required computing resources are less, which can effectively improve the accuracy and effect of point cloud reconstruction. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present application or the related technical solutions in the prior art. It should be understood that the drawings introduced below are only for the convenience of clearly expressing some embodiments of the technical solutions of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0051] Figure 1 A schematic diagram of a process for pipeline facility reconstruction and identification based on three-dimensional laser point cloud provided in an embodiment of the present application;

[0052] Figure 2 A schematic diagram of the structural framework of a pipeline facility reconstruction and identification system based on three-dimensional laser point cloud provided in an embodiment of the present application;

[0053] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application. For the step numbers in the following embodiments, they are provided only for the convenience of explanation and are not intended to limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0056] At present, the reconstruction of underground pipelines through point cloud data is mainly based on deep learning. Before reconstructing the point cloud of the underground pipeline, it is necessary to perform data semantic segmentation on the point cloud data object, and then input the semantically segmented point cloud data into the model used to realize the point cloud reconstruction of the underground pipeline.

[0057] Traditional point cloud data segmentation methods can be roughly divided into two types: the first is a plane-based block segmentation method, and the second is a probability-based random segmentation method. These segmentation methods have certain limitations, and it is difficult to complete the semantic segmentation task of point cloud data with huge point cloud quantities and complex scenes.

[0058] Specifically, the first plane-based block segmentation method usually divides the point cloud data into many cubes, and takes a fixed number of points in each cube. However, when the scene space is large (such as an outdoor scene space), the number of cubes obtained by segmenting and taking fixed points in this way will be very large when input to the PointNet model, which requires more computing resources. It is difficult to capture the overall geometric structure of objects in the scene space, and it is easy to fail to capture the effective overall geometric structure of the object, resulting in unsatisfactory point cloud reconstruction results.

[0059] The second type is a probability-based random segmentation method. Typically, in large-scale point cloud data (e.g., data scales in the millions or tens of millions), an initial sampling value is determined for each data point. The data point with the smallest sampling value is then selected as the first control point. The K-nearest neighbor method is then used to find N data points around the first control point as model input. The sampling values ​​of these N data points are then increased, with the magnitude of the increase related to the distance from the N data points to the first control point. The point with the smallest sampling value is then selected as the second control point, and this cycle repeats. While this method can effectively segment point cloud data, point cloud data processing is complex, requiring more computing resources, and the resulting point cloud data blocks are highly random. This makes it difficult to ensure that all point cloud data is used for subsequent model training or reconstruction (e.g., random sampling can easily lead to some data points in the point cloud data not participating in model training or reconstruction), resulting in low point cloud reconstruction accuracy.

[0060] In view of this, an embodiment of the present invention provides a pipeline facility reconstruction and identification method based on three-dimensional laser point cloud. The method is based on the combined blocking of sampled point cloud data, and can obtain point cloud sample blocks in which sample points are evenly distributed in the point cloud space. The number of sample points in each point cloud sample block is the same, which can effectively reduce the complexity of point cloud data processing and reduce the required computing resources; in addition, the method can also avoid the randomness problem in the selection of sampling points, so that each data point in the point cloud data can participate in the model training or the model reconstruction process of the point cloud, which can effectively improve the accuracy of point cloud reconstruction.

[0061] Reference Figure 1 In an embodiment of the present application, a pipeline facility reconstruction and identification method based on three-dimensional laser point cloud includes:

[0062] Step 110: Acquire 3D point cloud data to be reconstructed, and perform sampling preprocessing on the 3D point cloud data to obtain sampled point cloud data and first mapping information, where the first mapping information is used to represent a point cloud mapping relationship between the 3D point cloud data before sampling and the 3D point cloud data after sampling.

[0063] In the embodiments of the present application, the 3D point cloud data to be reconstructed can be obtained through LiDAR scanning. Specifically, it can be acquired using a base-station laser scanner or a portable laser scanner during ground excavation of the underground pipeline network. During the acquisition process, due to the influence of the construction environment, the collected 3D point cloud data often includes background points, pipeline points, and pipeline accessory points (such as valves) in the excavation scene.

[0064] In some embodiments, step 110 of performing sampling preprocessing on the three-dimensional point cloud data to obtain sampled point cloud data and first mapping information includes:

[0065] A1. Cleaning the three-dimensional point cloud data to obtain first point cloud data;

[0066] A2. performing three-dimensional grid division on the first point cloud data to obtain second point cloud data;

[0067] A3. Perform grid sampling on the second point cloud data to obtain the sampled point cloud data and the first mapping information.

[0068] In an embodiment of the present application, for a certain n1 rows and d columns of three-dimensional point cloud data, each row of data in the three-dimensional point cloud data represents a data point in the three-dimensional point cloud data, and each column of data in the three-dimensional point cloud data represents a characteristic information of the point cloud. Specifically, step A1 can first perform missing value verification on the three-dimensional point cloud data, wherein the missing value verification is used to discard data points in the three-dimensional point cloud data with missing characteristic information and retain data points in the three-dimensional point cloud data with complete characteristic information, thereby obtaining three-dimensional point cloud data after missing value verification, and recording the three-dimensional point cloud data after missing value verification as first intermediate data; then, a preset quantity threshold can be obtained and compared with the quantity threshold and the number of data points in the first intermediate data, retaining the first intermediate data with a number of data points greater than or equal to the quantity threshold, and recording the first intermediate data with a number of data points greater than or equal to the quantity threshold as second intermediate data; then, data points in the second intermediate data with missing or abnormal data color are removed, which can be specifically achieved by point cloud data processing software (such as CloudCompare) or by establishing an automatic recognition program, thereby obtaining first point cloud data with n2 rows and d columns.

[0069] It can be understood that step A2 can be downsampling the first point cloud data based on the grid unit to achieve three-dimensional grid division of the first point cloud data to obtain the second point cloud data. Specifically, it can be based on the three-dimensional space of the first point cloud data, determining a plurality of uniform grid units, each grid unit including a plurality of data points, and the specific size of the grid unit can be preset; step A3 specifically samples the data points in each grid unit into a grid sampling point through a statistical method. Specifically, the three-dimensional coordinate information (e.g., x-axis coordinate information, y-axis coordinate information, y-axis coordinate information) and color channel information (e.g., R color information, G color information, B color information) of all data points in the grid unit can be averaged to obtain the three-dimensional coordinate information and color channel information of the grid sampling point, thereby obtaining first mapping information and n3 rows and d columns of sampling point cloud data, where the number of rows n3 of the sampling point cloud data is less than the number of rows n2 of the first point cloud data. The first mapping information is used to represent the point cloud mapping relationship between the three-dimensional point cloud data before sampling and the three-dimensional point cloud data after sampling, and specifically can be the point cloud mapping relationship between the multiple data points in the grid unit and the corresponding grid sampling points.

[0070] Step 120: performing grouping and block processing on the sampled point cloud data to obtain a plurality of point cloud sample blocks and second mapping information, wherein each of the point cloud sample blocks has the same number of point clouds, and the sample points in each of the point cloud sample blocks are evenly distributed within the point cloud space of the sampled point cloud data, and the second mapping information is used to represent the point cloud mapping relationship between the point cloud sample blocks and the sampled point cloud data;

[0071] In an embodiment of the present application, the combined block processing of step 120 is used to divide the sampling point cloud data of n3 rows and d columns into a number of point cloud sample blocks, each point cloud sample block includes n4 rows and d columns of point cloud data, and the second mapping information is used to characterize the point cloud mapping relationship between the point cloud sample block and the sampling point cloud data, specifically, it can be the point cloud mapping relationship between multiple data points in the point cloud sample block and a certain data point in the sampling point cloud data.

[0072] In some embodiments, the step 120 of combining and dividing the sampled point cloud data into blocks to obtain a plurality of point cloud sample blocks and second mapping information includes:

[0073] B1. Sampling the farthest point of the point cloud on the sampled point cloud data to obtain third point cloud data;

[0074] Furthermore, the step B1 of sampling the farthest point of the sampled point cloud data to obtain third point cloud data includes:

[0075] B11. Determine the target sampling quantity based on the sampling point cloud data;

[0076] B12. According to the first point cloud sampling sequence, the remaining sampling point cloud data is screened for maximum distance to obtain the distance optimum point;

[0077] B13. Update the sampling points of the first point cloud sampling sequence according to the optimal distance point to obtain a second point cloud sampling sequence;

[0078] B14. Compare the target sampling number with the number of sampling points in the second point cloud sampling sequence to obtain a sampling comparison result;

[0079] B15. If the sampling comparison result is that the number of sampling points in the second point cloud sampling sequence is less than the target sampling number, then the first point cloud sampling sequence is updated according to the second point cloud sampling sequence, and then the process returns to the step of performing maximum distance screening on the remaining sampling point cloud data after sampling according to the first point cloud sampling sequence to obtain the distance optimum point; alternatively, if the sampling comparison result is that the number of sampling points in the second point cloud sampling sequence is equal to the target sampling number, then the second point cloud sampling sequence is determined as the third point cloud data.

[0080] In an embodiment of the present application, step B11 can determine the target sampling number based on the number of data points of the sampling point cloud data. There are many specific determination methods. For example, the target sampling number can be determined based on the ratio between the number of data points of the three-dimensional point cloud data and the number of data points of the sampling point cloud data. This application will not go into details here.

[0081] It is understood that, in step B12, if the number of sampling points in the first point cloud sampling sequence is equal to 1, the sampling points in the first point cloud sampling sequence can be randomly selected from the sampled point cloud data; alternatively, if the number of sampling points in the first point cloud sampling sequence is greater than or equal to 2, the sampling points in the second point cloud sampling sequence can be the second point cloud sampling sequence in the previous sampling process. Furthermore, the maximum distance screening in step B12 can be performed by calculating the distance between all sampling points in the first point cloud sampling sequence and the remaining data points in the sampled point cloud data (i.e., the sampled point cloud data remaining after sampling). The specific distance calculation method can be Euclidean distance, etc., and then the data point corresponding to the calculated maximum distance is determined as the distance optimal point.

[0082] It should be noted that the sampling point update in step B13 can be to add the optimal distance point to the first point cloud sampling sequence to obtain the second point cloud sampling sequence. Steps B14 and B15 can be to compare the target sampling number with the number of sampling points in the second point cloud sampling sequence. If the number of sampling points in the second point cloud sampling sequence is less than the target sampling number, the second point cloud sampling sequence of the current sampling process can be determined as the first point cloud sampling sequence of the next sampling process, and then return to step B12. Alternatively, if the number of sampling points in the second point cloud sampling sequence is equal to the target sampling number, it means that the second point cloud sampling sequence has sampled the required number of uniformly distributed data points. At this time, the third point cloud data can be determined based on each sampling point in the second point cloud sampling sequence of the current sampling process.

[0083] B2. Perform point cloud neighbor classification on the third point cloud data according to the sampled point cloud data to obtain a plurality of point cloud sample blocks and the second mapping information.

[0084] Furthermore, the step B2, performing point cloud neighbor classification on the third point cloud data according to the sampled point cloud data to obtain a plurality of the point cloud sample blocks and the second mapping information, includes:

[0085] B21. Obtaining a tree structure corresponding to the sampling point cloud data and a preset number of sample points;

[0086] B22. Perform a neighbor point search on the tree structure according to the number of sample points and the third point cloud data to obtain a plurality of point cloud sample blocks and the second mapping information.

[0087] In an embodiment of the present application, step B21 can be to construct a tree structure based on the sampling point cloud data, and the tree structure can specifically be a KD tree structure (KD tree). The preset number of sample points can be the number of nearest neighbor points corresponding to each data point in the third point cloud data, and the number of sample points is the same as the aforementioned target sampling number.

[0088] It can be understood that, for a certain data point in the third point cloud data, step B22 can be to perform a recursive search layer by layer on the KD tree structure based on the coordinates of the data point and the required number of sample points, so as to search for a number of nearest neighbor points corresponding to the data point, and the total number of the nearest neighbor points is equal to the number of sample points; then the data point and all the searched nearest neighbor points are determined as a point cloud sample block, and based on the correspondence between the data points of the point cloud sample block and the sampled point cloud data, the second mapping information is determined.

[0089] It should be noted that the sampling of the farthest point of the point cloud in step B1 is used to generate uniformly distributed control points (i.e., the data points in the third point cloud data). Specifically, when processing point cloud data, random sampling directly from the original data may cause the sampling points to be unevenly distributed in space, which will result in too much data in some areas and insufficient data in other areas. The farthest point sampling of the point cloud in the embodiment of the present application gradually selects the point farthest from the current set of sampled points to ensure that the new sampling points are as far away from the existing sampling points as possible, so that the control points are uniform in the point cloud coverage range. This uniform distribution of control points can not only represent the spatial distribution of the entire sampled point cloud data, reduce data redundancy, and improve the representativeness of the sampling. In addition, this uniform distribution of control points can effectively avoid the phenomenon of dense areas in some areas and sparse areas in other areas, thereby making the data points contained in the point cloud sample block generated in step B2 evenly distributed in the entire point cloud space. Step B2 point cloud neighbor classification is used to find the nearest neighbor points around each control point based on the generated control points to form a uniform sub-data block (i.e., point cloud sample block). In addition, the embodiment of the present application, based on steps B1 and B2, can effectively improve the impact of the problem of uneven segmentation of point cloud data, and can reduce the sample differences of point cloud data while reducing the computational complexity of point cloud data processing, thereby avoiding the subsequent model from being biased towards certain local areas in the point cloud data during training, thereby improving the robustness of the model and the accuracy of point cloud reconstruction based on the model. In addition, the embodiment of the present application, based on steps B1 and B2, specifically avoids the potential bias of random sampling (i.e., randomness problem) through the farthest point sampling in step B1, and ensures that the number of point clouds in each point cloud sample block is the same through the neighbor point search in step B2, so that each sampling point in the point cloud data can participate in the training process of the subsequent model, that is, avoids the situation where some sampling points do not participate in the model training process due to randomness problems, which can further improve the training efficiency of the point cloud classification model, as well as the point cloud reconstruction effect and accuracy.

[0090] It should be added that the embodiment of the present application is based on the farthest point sampling in step B1, which can effectively avoid the potential deviation of random sampling (i.e., randomness problem).

[0091] Step 130: Based on the second mapping information, reconstruct the point cloud sample block using the trained point cloud classification model to obtain point cloud reconstruction data;

[0092] In an embodiment of the present application, step 130 may be to input the point cloud sample block into a trained point cloud classification model, output the predicted category corresponding to each point cloud sample block through the trained point cloud classification model, and then determine the point cloud reconstruction data corresponding to the sampled point cloud data based on each predicted category and the corresponding second mapping information.

[0093] In some embodiments, step 130, based on the second mapping information, reconstructing the point cloud sample block using a trained point cloud classification model to obtain point cloud reconstruction data, includes:

[0094] C1. Inputting the point cloud sample block into the trained point cloud classification model to perform point cloud classification prediction and obtain a point cloud classification result;

[0095] C2. Perform index averaging on the point cloud classification results based on the second mapping information to obtain a reconstruction prediction result;

[0096] C3. Perform maximum screening on the category probability value corresponding to each sample point of the reconstruction prediction result to obtain the point cloud reconstruction data.

[0097] In an embodiment of the present application, step C1 may be to input the obtained point cloud sample block into a trained point cloud classification model for point cloud classification prediction, and obtain the predicted category output by the trained point cloud classification model (i.e., point cloud classification result). The point cloud classification result generally includes multiple predicted categories and scores or probability values ​​corresponding to each predicted category. Specifically, for a point cloud sample block with n4 rows and d columns, the point cloud classification model may output a point cloud classification result with n4 rows and c columns, where column c of the point cloud classification result is the total number of categories required to be classified by the point cloud classification model, and each column of the point cloud classification result is used to record the category probability of the point cloud sample block data point in that column.

[0098] It can be understood that since the sampled point cloud data will be divided into multiple point cloud sample blocks after combined block processing, there may be repeated data points between the point cloud sample blocks. After the multiple point cloud sample blocks are respectively input into the trained point cloud classification model for point cloud classification prediction, there may be data points with multiple point cloud classification predictions in the multiple point cloud classification results obtained. Therefore, step C2 can be based on the second mapping information to determine the multiple data points corresponding to each point cloud classification result in the sampled point cloud data; then, for a data point with multiple point cloud classification predictions, the multiple point cloud classification results corresponding to this data point can be averaged to obtain the overall classification result corresponding to the data point. The same is true for the remaining data points with multiple point cloud classification predictions, and can be simply deduced; then, the overall classification results corresponding to the data points with multiple point cloud classification predictions and the point cloud classification results corresponding to the data points without multiple point cloud classification predictions are integrated to obtain the reconstructed prediction result.

[0099] It should be noted that step C3 can be to perform maximum value screening on the category probability value corresponding to each sample point in the reconstructed prediction result. Specifically, for a certain data point in the reconstructed prediction result, it can be to screen the numerical values ​​between multiple prediction categories corresponding to the data point, and determine the prediction category with the largest score or probability value as the prediction category corresponding to the data point. The same is true for the remaining data points in the reconstructed prediction result, thereby obtaining point cloud reconstruction data.

[0100] Exemplarily, the point cloud classification model in the embodiment of the present application can be a RandLA-Net network model, whose structure is a U-Net point cloud segmentation network, which mainly includes an encoding layer, a downsampling layer, a decoding layer, and an upsampling layer. The method used by the RandLA-Net network in the downsampling link is random downsampling with the fastest computing performance. In order to make up for the greater information loss caused by random downsampling, the encoding layer of the network model first divides the point cloud into different local areas, and then performs feature aggregation on the points in each local area. Multi-scale feature extraction is then performed on the aggregated local features through a multi-layer neural network to obtain a feature representation with global perception. Finally, each data point is classified or segmented through a fully connected layer, and the corresponding point cloud classification result is output.

[0101] It should be added that, in actual applications, corresponding point cloud classification models can be constructed for specific pipeline application scenarios. Specifically, the embodiments of the present application can construct corresponding point cloud classification models for each pipeline application scenario, such as a point cloud classification model corresponding to a sewage pipeline scenario (DSD-Drainage Service Department), a point cloud classification model corresponding to a drinking water pipeline scenario (WSD-Water Supply Department), a point cloud classification model corresponding to a simple composite scene (DSD and WSD), and a point cloud classification model corresponding to a detailed mixed scene. The examples in this application are for illustration only and do not limit this application.

[0102] Furthermore, the trained point cloud classification model is obtained by training through the following steps:

[0103] D1. Obtain training sample blocks;

[0104] D2. Perform data enhancement and normalization on the training sample block to obtain a target sample block;

[0105] D3. Input the target sample block into the initialized point cloud classification model for training to obtain the trained point cloud classification model.

[0106] In the embodiment of the present application, the training sample block in step D1 can be the aforementioned point cloud sample block, or it can be a point cloud sample block obtained by performing the aforementioned steps 110 and 120 on new training point cloud data. The data augmentation and normalization in step D2 can include normalizing the color channel information of each data point in the training sample block to the range of (0, 1) and performing center normalization on the three-dimensional coordinate information of each data point in the training sample block to obtain a normalized training sample block; then, performing random translation, random rotation, and other operations on the normalized training sample block to obtain a target sample block.

[0107] It is understood that before a point cloud classification model is put into use, it needs to be trained to adjust its internal parameters to achieve better classification prediction results. Specifically, a target sample block can be input into the point cloud classification model, and the model will predict the target sample block. The model output data is the classification result of the target sample block. After obtaining the classification result of the target sample block output by the model, the accuracy of the model prediction can be evaluated based on the classification result of the target sample block and the label corresponding to the target sample block, thereby updating the model parameters.

[0108] Specifically, for machine learning models, the accuracy of the model prediction results can be measured by a loss function (LossFunction). The loss function is defined on a single training data and is used to measure the prediction error of a training data. Specifically, the loss value of the training data is determined by the label of the single training data and the prediction result of the model on the training data. During actual training, a training data set has a lot of training data, so a cost function (CostFunction) is generally used to measure the overall error of the training data set. The cost function is defined on the entire training data set and is used to calculate the average value of the prediction error of all training data, which can better measure the prediction effect of the model. For general machine learning models, based on the aforementioned cost function, plus a regularization term that measures the complexity of the model, it can be used as the objective function of the training. Based on this objective function, the loss value of the entire training data set can be calculated. There are many types of commonly used loss functions, such as 0-1 loss function, square loss function, absolute loss function, logarithmic loss function, cross entropy loss function, etc., which can all be used as loss functions of machine learning models, which will not be elaborated one by one here. In the embodiment of the present application, any one of the loss functions can be selected to determine the loss value of the training, such as the cross entropy loss function. Based on the training loss, the backpropagation algorithm is used to update the model parameters. After several iterations, a trained point cloud classification model is obtained. The specific number of iterations can be pre-set, or training is considered complete when the test set meets the required accuracy.

[0109] Step 140: Sample and restore the point cloud reconstruction data according to the first mapping information to obtain a target point cloud reconstruction result.

[0110] In an embodiment of the present application, since the point cloud reconstruction data obtained in step 130 is reconstruction data corresponding to the three-dimensional point cloud data after sampling preprocessing, the point cloud reconstruction data can be upsampled based on the point cloud mapping relationship recorded in the first mapping information, thereby obtaining a target point cloud reconstruction result corresponding to the three-dimensional point cloud data to be reconstructed.

[0111] A pipeline facility reconstruction and identification system based on three-dimensional laser point cloud proposed in accordance with an embodiment of the present application is described in detail below with reference to the accompanying drawings.

[0112] Reference Figure 2 In the embodiments of the present application, a pipeline facility reconstruction and identification system based on three-dimensional laser point cloud is proposed, comprising:

[0113] A first processing unit 101 is configured to obtain 3D point cloud data to be reconstructed and perform sampling preprocessing on the 3D point cloud data to obtain sampled point cloud data and first mapping information, wherein the first mapping information is used to represent a point cloud mapping relationship between the 3D point cloud data before sampling and the 3D point cloud data after sampling;

[0114] A second processing unit 102 is configured to perform group-block processing on the sampled point cloud data to obtain a plurality of point cloud sample blocks and second mapping information, wherein each point cloud sample block has the same number of point clouds and the sample points in each point cloud sample block are evenly distributed within the point cloud space of the sampled point cloud data, and the second mapping information is used to represent a point cloud mapping relationship between the point cloud sample block and the sampled point cloud data;

[0115] A third processing unit 103 is configured to perform point cloud reconstruction on the point cloud sample block using a trained point cloud classification model based on the second mapping information to obtain point cloud reconstruction data;

[0116] The fourth processing unit 104 is configured to perform sampling and restoration on the point cloud reconstruction data according to the first mapping information to obtain a target point cloud reconstruction result.

[0117] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0118] Reference Figure 3 , an embodiment of the present application further provides an electronic device, including:

[0119] at least one processor 201;

[0120] At least one memory 202, configured to store at least one program;

[0121] When the at least one program is executed by the at least one processor 201 , the at least one processor 201 implements the above method embodiment.

[0122] Similarly, it can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0123] An embodiment of the present application further provides a computer-readable storage medium, in which a program executable by the processor 201 is stored. The program executable by the processor 201 is used to implement the above-mentioned method embodiment when executed by the processor 201.

[0124] Similarly, the contents of the above method embodiments are applicable to the computer-readable storage medium embodiments. The functions specifically implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0125] In some optional embodiments, the functions / operations mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, the two boxes shown in succession may actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiments presented and described in the flow chart of the present application are provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logic flows presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0126] In addition, although the present application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present application. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the routine skills of an engineer. Therefore, a person skilled in the art can implement the present application as set forth in the claims using ordinary techniques without undue experimentation. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the appended claims and their equivalents.

[0127] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the embodiment method of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0128] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0129] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0130] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0131] In the above description of this specification, reference to the terms "one embodiment / example," "another embodiment / example," or "certain embodiments / examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples.

[0132] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and intent of the present application, and that the scope of the present application is defined by the claims and their equivalents.

[0133] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present application, and these equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A pipeline facility reconstruction and identification method based on three-dimensional laser point cloud, characterized in that: include: Acquiring three-dimensional point cloud data to be reconstructed, and performing sampling preprocessing on the three-dimensional point cloud data to obtain sampled point cloud data and first mapping information, wherein the first mapping information is used to represent a point cloud mapping relationship between the three-dimensional point cloud data before sampling and the three-dimensional point cloud data after sampling; performing a combined block processing on the sampled point cloud data to obtain a plurality of point cloud sample blocks and second mapping information, wherein each of the point cloud sample blocks has the same number of point clouds, and the sample points in each of the point cloud sample blocks are evenly distributed within the point cloud space of the sampled point cloud data, and the second mapping information is used to represent a point cloud mapping relationship between the point cloud sample blocks and the sampled point cloud data; Based on the second mapping information, reconstructing the point cloud sample block using a trained point cloud classification model to obtain point cloud reconstruction data; Performing sampling and restoration on the point cloud reconstruction data according to the first mapping information to obtain a target point cloud reconstruction result; The combining and block processing of the sampled point cloud data to obtain a plurality of point cloud sample blocks and second mapping information includes: Sampling the farthest point of the point cloud on the sampled point cloud data to obtain third point cloud data; performing point cloud nearest neighbor classification on the third point cloud data according to the sampled point cloud data to obtain a plurality of the point cloud sample blocks and the second mapping information; The step of sampling the farthest point of the point cloud on the sampled point cloud data to obtain third point cloud data includes: Determining a target sampling quantity based on the sampling point cloud data; According to the first point cloud sampling sequence, the remaining sampling point cloud data is screened for maximum distance to obtain the distance optimal point; According to the optimal distance point, updating the sampling points of the first point cloud sampling sequence to obtain a second point cloud sampling sequence; Comparing the target sampling number with the number of sampling points in the second point cloud sampling sequence to obtain a sampling comparison result; If the sampling comparison result is that the number of sampling points in the second point cloud sampling sequence is less than the target sampling number, the first point cloud sampling sequence is updated according to the second point cloud sampling sequence, and then the process returns to the step of performing maximum distance screening on the remaining sampling point cloud data after sampling according to the first point cloud sampling sequence to obtain the distance optimum point; alternatively, if the sampling comparison result is that the number of sampling points in the second point cloud sampling sequence is equal to the target sampling number, the second point cloud sampling sequence is determined as the third point cloud data.

2. The method according to claim 1, characterized in that The sampling and preprocessing of the three-dimensional point cloud data to obtain the sampled point cloud data and the first mapping information includes: Performing data cleaning on the three-dimensional point cloud data to obtain first point cloud data; Performing three-dimensional grid division on the first point cloud data to obtain second point cloud data; Grid sampling is performed on the second point cloud data to obtain the sampled point cloud data and the first mapping information.

3. The method according to claim 1, characterized in that The step of performing point cloud neighbor classification on the third point cloud data according to the sampled point cloud data to obtain a plurality of point cloud sample blocks and the second mapping information includes: Obtaining a tree structure corresponding to the sampling point cloud data and a preset number of sample points; According to the number of sample points and the third point cloud data, a neighbor point search is performed on the tree structure to obtain a plurality of the point cloud sample blocks and the second mapping information.

4. The method according to claim 1, wherein The step of reconstructing the point cloud sample block based on the second mapping information by using a trained point cloud classification model to obtain point cloud reconstruction data includes: Inputting the point cloud sample block into the trained point cloud classification model to perform point cloud classification prediction to obtain a point cloud classification result; Performing index averaging on the point cloud classification results according to the second mapping information to obtain a reconstruction prediction result; The category probability value corresponding to each sample point of the reconstruction prediction result is screened for the maximum value to obtain the point cloud reconstruction data.

5. The method according to claim 1, wherein The trained point cloud classification model is obtained by training through the following steps: Get training sample blocks; Performing data enhancement and normalization on the training sample block to obtain a target sample block; The target sample block is input into the initialized point cloud classification model for training to obtain the trained point cloud classification model.

6. A pipeline facility reconstruction and identification system based on three-dimensional laser point cloud, characterized in that: include: a first processing unit, configured to acquire three-dimensional point cloud data to be reconstructed, and perform sampling preprocessing on the three-dimensional point cloud data to obtain sampled point cloud data and first mapping information, wherein the first mapping information is used to represent a point cloud mapping relationship between the three-dimensional point cloud data before sampling and the three-dimensional point cloud data after sampling; a second processing unit, configured to perform combined block processing on the sampled point cloud data to obtain a plurality of point cloud sample blocks and second mapping information, wherein each of the point cloud sample blocks has the same number of point clouds, and the sample points in each of the point cloud sample blocks are uniformly distributed within the point cloud space of the sampled point cloud data, and the second mapping information is used to represent a point cloud mapping relationship between the point cloud sample blocks and the sampled point cloud data; a third processing unit, configured to perform point cloud reconstruction on the point cloud sample block using a trained point cloud classification model based on the second mapping information to obtain point cloud reconstruction data; a fourth processing unit, configured to perform sampling and restoration on the point cloud reconstruction data according to the first mapping information to obtain a target point cloud reconstruction result; The combining and block processing of the sampled point cloud data to obtain a plurality of point cloud sample blocks and second mapping information includes: Sampling the farthest point of the point cloud on the sampled point cloud data to obtain third point cloud data; performing point cloud nearest neighbor classification on the third point cloud data according to the sampled point cloud data to obtain a plurality of the point cloud sample blocks and the second mapping information; The step of sampling the farthest point of the point cloud on the sampled point cloud data to obtain third point cloud data includes: Determining a target sampling quantity based on the sampling point cloud data; According to the first point cloud sampling sequence, the remaining sampling point cloud data is screened for maximum distance to obtain the distance optimal point; According to the optimal distance point, updating the sampling points of the first point cloud sampling sequence to obtain a second point cloud sampling sequence; Comparing the target sampling number with the number of sampling points in the second point cloud sampling sequence to obtain a sampling comparison result; If the sampling comparison result is that the number of sampling points in the second point cloud sampling sequence is less than the target sampling number, the first point cloud sampling sequence is updated according to the second point cloud sampling sequence, and then the process returns to the step of performing maximum distance screening on the remaining sampling point cloud data after sampling according to the first point cloud sampling sequence to obtain the distance optimum point; alternatively, if the sampling comparison result is that the number of sampling points in the second point cloud sampling sequence is equal to the target sampling number, the second point cloud sampling sequence is determined as the third point cloud data.

7. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the method according to any one of claims 1 to 5 when executed by the processor.

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