Three-dimensional Model Construction Method for Building Structure Design Based on Spatial Point Cloud Data

Abstract

The present invention relates to the technical field of building model construction, and particularly to a method for constructing a three-dimensional model of building structure design based on spatial point cloud data. The present invention divides the spatial point cloud data of a construction building into several projection planes, then divides superpixel clusters according to the distribution characteristics and numerical characteristics of the projection data points in the projection plane, and analyzes the similarity between different superpixel clusters to obtain the clustering of superpixel clusters; then analyzes the positional relationship between the superpixel clusters in the clustering, as well as the volume difference and volume distribution of the superpixel clusters in the clustering, to obtain the noise influence degree of the projection plane; finally, according to the noise influence degree, noise reduction processing is performed on the spatial point cloud data. The method of the present invention performs different degrees of noise reduction processing on the spatial point cloud data of different projection planes and different clusters of the construction building, improves the efficiency and accuracy of the noise reduction processing, and thus helps to construct a reliable three-dimensional model of building structure design.

Description

Technical Field

[0001] The present invention relates to the technical field of construction model construction, and specifically relates to a method for constructing a three-dimensional model of building structure design based on spatial point cloud data. Background Art

[0002] Spatial point cloud data is three-dimensional spatial data collected and processed through laser scanning technology, consisting of a large number of discrete data points, and each data point represents a spatial position. When spatial point cloud data is used for a building three-dimensional model, it can accurately reconstruct the geometric shape of the building and provide detailed information for design, analysis, and maintenance; when used during the building construction process, it can monitor the construction progress. Therefore, it is possible to collect the spatial point cloud data of the construction building, construct an accurate three-dimensional model of the construction building structure, and then use it to accurately monitor the construction progress of the construction building.

[0003] In order to construct a three-dimensional model for a construction building, generally, an unmanned aerial vehicle is used to collect spatial point cloud data of the construction building. However, due to the presence of many reflectors in the construction environment, a large amount of noise exists in the collected spatial point cloud data. Therefore, it is necessary to perform noise reduction processing on the collected spatial point cloud data. When performing noise reduction processing on the spatial point cloud data, due to the irregular distribution of the construction building structure, the noise effects on each part are different; in addition, due to the complex situation at the building construction site, the complexity of the noise is increased, thereby increasing the difficulty of noise reduction processing.

[0004] Therefore, there is an urgent need for a method to accurately perform noise reduction processing on the spatial point cloud data of a construction building to construct a reliable three-dimensional model of building structure design. Summary of the Invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide a method for constructing a three-dimensional model of building structure design based on spatial point cloud data.

[0006] According to the first aspect of the embodiments of the present invention, a method for constructing a three-dimensional model of building structure design based on spatial point cloud data is provided, and the technical solution adopted is specifically as follows:

[0007] Collect the spatial point cloud data of the building;

[0008] Divide the spatial point cloud data into data points of different grid surfaces, project the data points in each grid surface, and obtain the projection plane corresponding to each grid surface;

[0009] Analyze the distribution characteristics and numerical characteristics of the projection data points in the projection plane, perform superpixel segmentation on the projection data points in the projection plane, and obtain a plurality of superpixel clusters;

[0010] Analyze the similarity between the superpixel clusters based on the distribution characteristics and numerical characteristics of the internal projection data points of the superpixel clusters, and obtain the clustering of the superpixel clusters.

[0011] Analyze the positional relationship between the superpixel clusters in the clustering to obtain the distribution factor of the clustering.

[0012] Analyze the volume difference and volume distribution of the superpixel clusters in the clustering to obtain the noise impact factor of the clustering.

[0013] According to the distribution factor and the noise impact factor of the clustering, combined with the volume of the clustering, obtain the noise impact degree of the projection plane.

[0014] Perform noise reduction processing on the spatial point cloud data according to the noise impact degree.

[0015] In some embodiments of the present invention, dividing the spatial point cloud data into data points on different grid surfaces includes:

[0016] Use a triangular grid model to perform surface reconstruction on the spatial point cloud data, divide the grid in the triangular grid algorithm into multiple grid surfaces, and then use a region growing algorithm to segment the grid surfaces to divide the spatial point cloud data into data points on different grid surfaces.

[0017] In some embodiments of the present invention, project the data points in each grid surface to obtain the projection plane corresponding to each grid surface, including:

[0018] Project all the data points in each grid surface onto a plane perpendicular to its viewing angle to obtain the projection plane corresponding to each grid surface, where the data value of the projection data point in the projection plane is the height value between the corresponding data point and the projection plane of the projection data point.

[0019] In some embodiments of the present invention, analyze the distribution characteristics of the projection data points in the projection plane, including:

[0020] In the projection plane, obtain the first minimum distance between all projection data points and other projection data points, and record the maximum value of all the first minimum distances as the first distance.

[0021] For each projection data point in the projection plane, draw a circle in the projection plane with the projection data point as the center and the first distance as the radius to obtain the surrounding area of the projection data point.

[0022] Analyze the density of the projection data points in the surrounding area to obtain the distribution characteristics of the projection data points in the projection plane.

[0023] In some embodiments of the present invention, analyzing the numerical characteristics of the projection data points in the projection plane includes:

[0024] In the surrounding area, obtain the second minimum distance between all projection data points and other projection data points, and denote the maximum value among all the second minimum distances as the second distance;

[0025] For any projection data point in the surrounding area, if its data value is the largest within the range of the second distance, then denote this projection data point as an extreme value point, and obtain all the extreme value points in the surrounding area;

[0026] Analyze the distance and numerical difference between any two of the extreme value points to obtain the numerical characteristics of the projection data points in the projection plane.

[0027] In some embodiments of the present invention, analyzing the positional relationship between the superpixel clusters in the clustering to obtain the distribution factor of the clustering includes:

[0028] Obtain the centroid of each superpixel cluster in the clustering;

[0029] In the clustering, obtain the minimum distance between the centroid of each superpixel cluster and the centroids of all other superpixel clusters, and denote it as the third distance of the superpixel cluster;

[0030] In the clustering, analyze the information entropy of the positional distribution between each superpixel cluster and all other superpixel clusters;

[0031] Obtain the distribution factor of the clustering according to the third distance and the information entropy.

[0032] In some embodiments of the present invention, in the clustering, analyzing the information entropy of the positional distribution between each superpixel cluster and all other superpixel clusters includes:

[0033] Extend horizontally through the centroid of the superpixel cluster to obtain the extension line of the superpixel cluster;

[0034] In the clustering, connect the centroids between the superpixel cluster and each other superpixel cluster to obtain the length of the centroid connection line, denote it as the fourth distance, and obtain the angle of the clockwise direction between the centroid connection line and the extension line, denote it as the first angle;

[0035] Calculate the product of the fourth distance and the first angle between the superpixel cluster and each other superpixel cluster to obtain the information entropy of the superpixel cluster.

[0036] In some embodiments of the present invention, obtaining the distribution factor of the clustering according to the third distance and the information entropy includes:

[0037] Analyze the dispersion degree among the third distances corresponding to all the superpixel clusters in the cluster, and analyze the dispersion degree among the information entropies corresponding to all the superpixel clusters in the cluster, to obtain the distribution factor of the cluster.

[0038] In some embodiments of the present invention, analyze the volume difference situation and volume distribution situation of the superpixel clusters in the cluster, to obtain the noise influence factor of the cluster, including:

[0039] Obtain the superpixel cluster with the largest volume in the cluster, denoted as the first cluster;

[0040] Calculate the average volume ratio of all the remaining superpixel clusters in the cluster to the first cluster, to obtain the volume difference situation of the superpixel clusters in the cluster;

[0041] Obtain the two superpixel clusters closest to the volume of each superpixel cluster in the cluster, denoted as the close clusters;

[0042] Connect the centroid of the superpixel cluster with the centroids of its corresponding two close clusters respectively, and denote the angle between the two centroid connection lines as the second angle;

[0043] Analyze the difference between the second angle of each superpixel cluster in the cluster and the second angles of other superpixel clusters, and combine with the volume of the superpixel cluster, to obtain the volume distribution situation of the superpixel clusters in the cluster;

[0044] Combine the volume difference situation and the volume distribution situation, to obtain the noise influence factor of the cluster.

[0045] In some embodiments of the present invention, perform noise reduction processing on the spatial point cloud data according to the noise influence degree, including:

[0046] Perform noise reduction processing on the spatial point cloud data based on the voxel grid filtering algorithm, and adjust the voxel size in the voxel grid filtering algorithm according to the noise influence degree corresponding to each projection plane, to perform noise reduction processing on the spatial point cloud data corresponding to each projection plane.

[0047] Compared with the prior art, the three-dimensional model construction method for building structure design based on spatial point cloud data provided by the present invention has the following beneficial effects:

[0048] The present invention collects spatial point cloud data. First, the spatial point cloud data is divided into several projection planes according to the construction building. Then, superpixel clusters are divided according to the distribution characteristics and numerical characteristics of the projection data points within the projection plane. By analyzing the similarity between different superpixel clusters, the clustering of the superpixel clusters is obtained, realizing the functional block segmentation of the projection plane, facilitating subsequent accurate analysis of the noise impact, and then performing precise and effective noise reduction processing. After that, by analyzing the positional relationship between the superpixel clusters in the clustering, as well as analyzing the volume difference and volume distribution of the superpixel clusters in the clustering, and combining with the volume of the clustering, the noise impact degree of the projection plane is obtained. Finally, according to the noise impact degree, noise reduction processing is performed on the spatial point cloud data, providing good data support for subsequent construction of the three-dimensional model of the construction building. The method of the present invention performs different degrees of noise reduction processing on the spatial point cloud data of different projection planes and different clusters of the construction building, improving the efficiency and accuracy of the noise reduction processing, and thus contributing to the construction of a reliable three-dimensional model of the building structure design. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for description in the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0050] Figure 1 FIG. is a schematic flowchart of a method for constructing a three-dimensional model of a building structure design based on spatial point cloud data provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of the method for constructing a three-dimensional model of a building structure design based on spatial point cloud data proposed by the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs. Terms such as "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, such that a circuit structure, article or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or also includes elements inherent to such article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the presence of additional identical elements in the article or device including the element. Relative terms such as "first" and "second" are used solely to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0053] It should be noted that to ensure the meaningfulness of the calculation results, in the fractional operations of the embodiments of the present invention, when the denominator is 0, a tuning factor greater than 0 needs to be added to the denominator to prevent the denominator from being 0. The value of the tuning factor is set by the implementer according to the actual situation, and this application does not make special restrictions.

[0054] The following specifically describes the specific solution of a three-dimensional model construction method for building structure design based on spatial point cloud data provided by the present invention in conjunction with the accompanying drawings.

[0055] Please refer to Figure 1 , which shows the basic process of a three-dimensional model construction method for building structure design based on spatial point cloud data provided by an embodiment of the present invention.

[0056] As Figure 1 shown, a three-dimensional model construction method for building structure design based on spatial point cloud data provided by an embodiment of the present invention specifically includes:

[0057] S100: Collect the spatial point cloud data of the building.

[0058] To construct a 3D model of a construction building, it is first necessary to collect the spatial point cloud data of the construction building. Due to the large volume of the construction building, it is difficult to collect spatial point cloud data using a handheld laser scanner. Therefore, a drone is used to carry a laser scanner to collect spatial point cloud data. Specifically, first select a laser scanner with an appropriate scanning range and accuracy according to the volume of the construction building, and choose a laser scanner that is waterproof and dustproof to adapt to the harsh construction environment. Calibrate the laser scanner before use to ensure accurate measurement. Then install the laser scanner on the drone and set the flight trajectory of the drone to continuously circle the construction building. The laser scanner emits laser beams. After the laser beams hit the target surface, they are reflected back to the scanner. The device calculates the distance from the target surface point to the laser scanner based on the return time of the laser beams and records the three-dimensional coordinates of the target surface point. During the scanning process of the laser scanner, laser beams are continuously emitted to generate a large amount of point data. Each point contains spatial coordinate information, and these points converge into a spatial point cloud dataset of the construction building.

[0059] So far, the spatial point cloud data of the construction building has been obtained.

[0060] S200: Divide the spatial point cloud data into data points of different grid surfaces, project the data points in each grid surface, and obtain the projection plane corresponding to each grid surface.

[0061] Since the spatial point cloud data reflects the three-dimensional structure of the construction building and the distribution of data points on each surface of the construction building is different, it is necessary to divide the spatial point cloud data into data points of different grid surfaces, that is, divide the spatial point cloud data into different surfaces of the construction building. Specifically, first use a triangular mesh model to perform surface reconstruction on the spatial point cloud data, divide the grid in the triangular mesh algorithm into multiple grid surfaces, and then use the region growing algorithm to segment the grid surfaces to divide the spatial point cloud data into data points of different grid surfaces.

[0062] The obtained data points of different grid surfaces represent the data points of different azimuth perspective surfaces of the construction building. Therefore, further project the data points in each grid surface. Specifically, project all the data points in each grid surface onto a plane perpendicular to its perspective to obtain the projection plane corresponding to each grid surface. The data value of the projected data point in the projection plane is the height value between the corresponding data point and the projection plane, and the data points in the projection plane are discrete.

[0063] So far, all the spatial point cloud data has been projected onto the corresponding projection planes, and the projected data points in the projection planes have been obtained.

[0064] During the acquisition of spatial point cloud data, due to the accuracy of the laser scanner and the generation of abnormal points or noise points during the transmission of spatial point cloud data, and because the building structure is irregularly distributed, different parts are affected by different levels of noise. Therefore, it is necessary to divide the projection plane into different parts and analyze the noise conditions of different parts, specifically including steps S300 to S700.

[0065] S300: Analyze the distribution characteristics and numerical characteristics of the projection data points in the projection plane, perform superpixel segmentation on the projection data points in the projection plane to obtain multiple superpixel clusters.

[0066] There are various functional blocks in different areas of the construction building, and the characteristics of the spatial point cloud data of different functional blocks are different. Therefore, before performing noise analysis, it is first necessary to perform functional block segmentation on all projection planes.

[0067] Therefore, in the embodiments of the present invention, by analyzing the distribution characteristics and numerical characteristics of the projection data points in the projection plane, superpixel segmentation is performed on the projection data points in the projection plane to obtain multiple superpixel clusters. Among them:

[0068] To analyze the distribution characteristics of the projection data points in the projection plane, the specific implementation method is as follows: First, in the projection plane, obtain the first minimum distance between all projection data points and other projection data points, and record the maximum value among all the first minimum distances corresponding to the projection data points as the first distance; then, for each projection data point in the projection plane, draw a circle in the projection plane with the projection data point as the center and the first distance as the radius to obtain the surrounding area of each projection data point; finally, analyze the density of the projection data points in the surrounding area corresponding to each projection data point, that is, calculate the ratio of the number of projection data points in the surrounding area to the area of the surrounding area (the area of a circle with a radius of the first distance) to obtain the distribution characteristics of the projection data points in the projection plane.

[0069] To analyze the numerical characteristics of the projection data points in the projection plane, the specific implementation method is as follows: First, in the surrounding area of the projection data points, obtain the second minimum distance between all projection data points and other projection data points, and record the maximum value among all the second minimum distances corresponding to the projection data points as the second distance; then, for any projection data point in the surrounding area, if its data value is the largest within the second distance range (within the range with the projection data point as the center and the second distance range as the radius), then record this projection data point as an extreme value point, and obtain all the extreme value points in the surrounding area corresponding to each projection data point; finally, analyze the distance and numerical difference between any two extreme value points in each surrounding area to obtain the numerical characteristics of the projection data points in the projection plane.

[0070] Perform superpixel segmentation on the projection data points of the projection plane to obtain multiple superpixel clusters. The specific implementation method is as follows: According to the distribution characteristics (the density of projection data points in the surrounding area) and numerical characteristics of the projection data points in the projection plane, perform superpixel segmentation on the projection data points of the projection plane to obtain multiple superpixel clusters. The distribution characteristics and numerical characteristics of the projection data points within each superpixel cluster are similar.

[0071] S400: Based on the distribution characteristics and numerical characteristics of the projection data points within the superpixel clusters, analyze the similarity between the superpixel clusters to obtain the clustering of the superpixel clusters.

[0072] Due to differences in structure and material, the density and fluctuation of the spatial point clouds collected at different parts of different functional areas on the surface of a construction building are different. Among them, some individual smaller superpixel clusters may be caused by noise and some special structures on the surface of the construction building. Therefore, it is necessary to analyze the distribution of superpixel clusters in each projection plane of the spatial point cloud data of the construction building.

[0073] In the embodiment of the present invention, based on the distribution characteristics and numerical characteristics of the projection data points within the superpixel clusters, analyze the similarity between the superpixel clusters to obtain the clustering of the superpixel clusters. The specific implementation method is as follows: For the superpixel clusters in the obtained projection plane, obtain the two superpixel clusters with the closest numerical characteristics, distribution characteristics (density), and data values and record them as similar clusters. For the similar clusters of the superpixel clusters, if some superpixel clusters belong to the similar clusters of several superpixel clusters, then record the superpixel cluster and its most similar superpixel cluster as a pair of similar clusters. To ensure that the superpixel clusters within the clustering are relatively similar to each other, for the similar clusters obtained by the above operations, if the remaining superpixel clusters and any superpixel cluster within the similar clusters belong to the similar clusters, then divide them into this similar cluster, thereby obtaining several clusters of similar clusters, that is, obtaining the clustering of multiple superpixel clusters. The distribution of the projection data points within the superpixel clusters in the same clustering is relatively similar.

[0074] S500: Analyze the positional relationship between the superpixel clusters within the clustering to obtain the distribution factor of the clustering.

[0075] For the clustering of superpixel clusters obtained in the above steps, the numerical characteristics, distribution characteristics (density), and data values of the projection data points between the superpixel clusters in the same clustering are relatively similar. However, the positional distribution of different superpixel clusters within the same clustering will affect the distribution regularity of the superpixel clusters within the entire clustering, and the distribution regularity of the superpixel clusters is an important manifestation of the influence of noise.

[0076] Therefore, by analyzing the positional relationship between superpixel clusters in the clustering, the distribution factor of the clustering is obtained. The specific implementation method is as follows: First, obtain the centroid of each superpixel cluster in the clustering; then, in the clustering, obtain the minimum distance between the centroid of each superpixel cluster and the centroids of all other superpixel clusters, which is denoted as the third distance of the superpixel cluster; in the clustering, analyze the information entropy of the positional distribution between each superpixel cluster and all other superpixel clusters. A more specific implementation method is to extend horizontally through the centroid of the superpixel cluster to obtain the extension line of the superpixel cluster; in the clustering, connect the centroids between the superpixel cluster and each other superpixel cluster to obtain the length of the centroid connection line, which is denoted as the fourth distance (each superpixel cluster corresponds to a fourth distance, where represents the number of superpixel clusters in the clustering), and obtain the angle of the clockwise direction between the centroid connection line and the extension line, which is denoted as the first angle (each superpixel cluster corresponds to a first angle); calculate the product of the fourth distance and the first angle between the superpixel cluster and each other superpixel cluster (each superpixel cluster corresponds to a product), and obtain the information entropy of the superpixel cluster according to the product; finally, according to the third distance and the information entropy, obtain the distribution factor of the clustering. A more specific implementation method is to analyze the dispersion degree between the third distances corresponding to all superpixel clusters in the clustering, and analyze the dispersion degree between the information entropies corresponding to all superpixel clusters in the clustering to obtain the distribution factor of the clustering. The calculation formula for constructing the distribution factor of the th clustering is:

[0077]

[0078] In the formula, represents the distribution factor of the th clustering on the projection plane; represents the information entropy of the th superpixel cluster in the th clustering; represents the variance of the information entropies corresponding to all superpixel clusters in the th clustering; represents the third distance of the th superpixel cluster in the th clustering; represents the variance of the third distances corresponding to all superpixel clusters in the th clustering; represents the exponential function with the natural constant as the base.

[0079] For the variance , the smaller its value, the smaller the difference in the information entropy of the superpixel clusters in the cluster, indicating that the difference in the fourth distance and the first angle of the superpixel clusters in the cluster is not significant, indicating that the distribution of the superpixel clusters in the cluster is relatively similar. Therefore, the distribution factor of the cluster is larger; for the variance of the third distance corresponding to all superpixel clusters in the cluster , the smaller its value, the more evenly distributed the distances between the superpixel clusters in the cluster. Therefore, the distribution factor of the cluster is larger; among the clusters with larger distribution factors, the distribution of the superpixel clusters is more regular.

[0080] S600: Analyze the volume difference and volume distribution of the superpixel clusters in the cluster to obtain the noise influence factor of the cluster.

[0081] Since the distribution of noise in the projection plane of the construction building is disordered, it may be affected by some noise in the cluster, resulting in a lower distribution factor of the cluster. Noise will be reflected as superpixel clusters with smaller volumes in the cluster, where the volume is the number of projection data points and there is a large difference from other superpixel clusters in the cluster. Therefore, in the embodiments of the present invention, by analyzing the volume difference and volume distribution of the superpixel clusters in the cluster, the noise influence factor of the cluster is obtained. The specific implementation method is as follows: First, obtain the superpixel cluster with the largest volume in the cluster, denoted as the first cluster; and calculate the average volume ratio of all the remaining superpixel clusters in the cluster to the first cluster to obtain the volume difference of all superpixel clusters corresponding to the cluster; then, obtain the two superpixel clusters closest to the volume of each superpixel cluster in the cluster, denoted as the close clusters; and connect the centroids of the superpixel clusters to the centroids of their corresponding two close clusters respectively, and denote the angle between the two centroid connection lines as the second angle; then, analyze the difference between the second angle of each superpixel cluster in the cluster and the second angle of other superpixel clusters, that is, calculate the difference between the second angle of each superpixel cluster and the average value of the second angles of all superpixel clusters in the cluster, and combine the volume of the superpixel cluster to obtain the volume distribution of the superpixel clusters in the cluster; finally, combine the volume difference and volume distribution to obtain the noise influence factor of the cluster. Construct the formula for calculating the noise influence factor of the

[0082]

[0083] In the formula, represents the noise influence factor of the th cluster on the projection plane; represents the average volume ratio of all the remaining superpixel clusters in the th cluster to the first cluster; The second angle corresponding to the th superpixel cluster in a cluster; represents the mean of the second angles corresponding to all superpixel clusters in the th cluster; represents the th cluster, and the th superpixel cluster's volume size; represents the number of superpixel clusters in the th cluster; represents the hyperbolic function.

[0084] For the average volume ratio of clusters , it represents the volume difference situation of all superpixel clusters in the cluster. The larger its value, the closer the volume sizes of the superpixel clusters within the cluster are. Therefore, when there are superpixel clusters with a smaller volume, it may be affected by noise; for the ratio of the difference between the second angle of the superpixel cluster and the mean of the second angles to the volume of the superpixel cluster in the cluster , the larger its value, the relatively larger the included angle between superpixel clusters with similar volumes and the smaller the volume of the superpixel cluster. Therefore, it indicates that the cluster is more affected by noise.

[0085] S700: According to the distribution factor and noise influence factor of the cluster, combined with the volume of the cluster, obtain the noise influence degree of the projection plane.

[0086] Through the above steps, the distribution factor and noise influence factor of each cluster in the projection plane are obtained. It is also necessary to obtain the situation of the overall projection plane affected by noise. Therefore, in the embodiments of the present invention, according to the distribution factor and noise influence factor of the cluster, combined with the volume of the cluster, obtain the noise influence degree of the projection plane. Among them, the larger the volume of the cluster, the lower the distribution factor, and the larger the noise influence factor, the higher the overall noise influence on the projection plane. Therefore, the calculation formula for constructing the noise influence degree of the projection plane is:

[0087]

[0088] In the formula, represents the noise influence degree of the projection plane; represents the number of clusters in the projection plane; represents the distribution factor of the th cluster on the projection plane; represents the noise influence factor of the th cluster on the projection plane; represents the volume of the th cluster on the projection plane.

[0089] S800: Denoise the spatial point cloud data according to the degree of noise influence.

[0090] Denoise the spatial point cloud data according to the degree of noise influence. The specific implementation is as follows:

[0091] Voxel grid filtering is a commonly used method for denoising spatial point cloud data. Among them, the adjustment of the voxel size in the voxel grid filtering algorithm is a key step in voxel grid filtering denoising, because the voxel size directly affects the denoising effect and the preservation of details of the spatial point cloud data. Therefore, denoise the spatial point cloud data based on the voxel grid filtering algorithm, and adjust the voxel size in the voxel grid filtering algorithm according to the degree of noise influence corresponding to each projection plane. Among them, the specific method of voxel adjustment is as follows: First, select the initial voxel size according to the volume and density of the spatial point cloud data. In the case of the initial voxel, the spatial point cloud data will be relatively rough and needs to be filtered and smoothed. Due to the complexity of the construction environment, the spatial point cloud data in different parts are affected by noise to different degrees and thus vary to different degrees. It is necessary to adjust the voxel size according to the noise influence factors of different projection planes. Therefore, the voxel size is adjusted to:

[0092]

[0093] In the formula, represents the adjusted voxel size; represents the initial voxel size; represents the th noise influence degree of the projection plane.

[0094] Denoise the spatial point cloud data corresponding to each projection plane according to the adjusted voxel size.

[0095] It should be noted that: The above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0096] The various embodiments in this specification are all described in a progressive manner. For the same or similar parts between the various embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for constructing a three-dimensional model of building structure design based on spatial point cloud data, characterized in that, The method includes: Collecting spatial point cloud data of a building; Dividing the spatial point cloud data into data points of different grid surfaces, projecting the data points in each grid surface, and obtaining a projection plane corresponding to each grid surface; Analyzing the distribution characteristics and numerical characteristics of the projected data points in the projection plane, performing superpixel segmentation on the projected data points in the projection plane, and obtaining a plurality of superpixel clusters; Based on the distribution characteristics and numerical characteristics of the projected data points inside the superpixel clusters, analyzing the similarity between the superpixel clusters, and obtaining the clustering of the superpixel clusters; Analyzing the positional relationship between the superpixel clusters in the clustering, and obtaining the distribution factor of the clustering; Analyzing the volume difference situation and volume distribution situation of the superpixel clusters in the clustering, and obtaining the noise influence factor of the clustering; According to the distribution factor and the noise influence factor of the clustering, combining with the volume of the clustering, obtaining the noise influence degree of the projection plane; According to the noise influence degree, performing noise reduction processing on the spatial point cloud data; Analyzing the positional relationship between the superpixel clusters in the clustering, and obtaining the distribution factor of the clustering, including: Obtaining the centroid of each superpixel cluster in the clustering; In the clustering, obtaining the minimum distance between the centroid of each superpixel cluster and the centroids of all other superpixel clusters, and recording it as the third distance of the superpixel cluster; In the clustering, analyzing the information entropy of the positional distribution between each superpixel cluster and all other superpixel clusters; According to the third distance and the information entropy, obtaining the distribution factor of the clustering; Analyzing the volume difference situation and volume distribution situation of the superpixel clusters in the clustering, and obtaining the noise influence factor of the clustering, including: Obtaining the superpixel cluster with the largest volume in the clustering, and recording it as the first cluster; Calculating the average volume ratio of all the remaining superpixel clusters in the clustering to the first cluster, and obtaining the volume difference situation of the superpixel clusters in the clustering; Obtaining the two superpixel clusters closest to the volume of each superpixel cluster in the clustering, and recording them as the close clusters; Connecting the centroid of the superpixel cluster to the centroids of its corresponding two close clusters respectively, and recording the angle between the two centroid connection lines as the second angle; Analyzing the difference between the second angle of each superpixel cluster in the clustering and the second angles of other superpixel clusters, and combining with the volume of the superpixel cluster, obtaining the volume distribution situation of the superpixel clusters in the clustering; Combining the volume difference situation and the volume distribution situation, and obtaining the noise influence factor of the clustering.

2. The three-dimensional model construction method for building structure design based on spatial point cloud data according to claim 1, characterized in that, Dividing the spatial point cloud data into data points of different grid surfaces, including: Performing surface reconstruction on the spatial point cloud data using a triangular grid model, dividing the grids in the triangular grid algorithm into multiple grid surfaces, and then using a region growing algorithm to segment the grid surfaces, so as to divide the spatial point cloud data into data points of different grid surfaces.

3. The three-dimensional model construction method for building structure design based on spatial point cloud data according to claim 2, wherein Projecting the data points in each grid surface, and obtaining a projection plane corresponding to each grid surface, including: Project all data points in each grid face onto a plane perpendicular to its viewing angle to obtain a projection plane corresponding to each grid face, where the data value of the projected data point in the projection plane is the height value between the data point corresponding to the projected data point and the projection plane.

4. The three-dimensional model construction method for building structure design based on spatial point cloud data according to claim 3, characterized in that, Analyze the distribution characteristics of the projected data points in the projection plane, including: In the projection plane, obtain the first minimum distance between all projected data points and other projected data points, and denote the maximum value among all the first minimum distances as the first distance. For each projected data point in the projection plane, draw a circle in the projection plane with the projected data point as the center and the first distance as the radius to obtain the surrounding area of the projected data point. Analyze the density of the projected data points in the surrounding area to obtain the distribution characteristics of the projected data points in the projection plane.

5. The three-dimensional model construction method for building structure design based on spatial point cloud data according to claim 4, wherein Analyze the numerical characteristics of the projected data points in the projection plane, including: In the surrounding area, obtain the second minimum distance between all projected data points and other projected data points, and denote the maximum value among all the second minimum distances as the second distance. For any projected data point in the surrounding area, if its data value is the largest within the second distance range, then denote this projected data point as an extreme value point, and obtain all extreme value points in the surrounding area. Analyze the distance and numerical difference between any two of the extreme value points to obtain the numerical characteristics of the projected data points in the projection plane.

6. The three-dimensional model construction method of building structure design based on spatial point cloud data according to claim 1, characterized in that In the clustering, analyze the information entropy of the position distribution between each superpixel cluster and all other superpixel clusters, including: Extend horizontally through the centroid of the superpixel cluster to obtain the extension line of the superpixel cluster. In the clustering, connect the centroids between the superpixel cluster and each other superpixel cluster to obtain the length of the centroid connection line, denoted as the fourth distance, and obtain the angle of the clockwise direction between the centroid connection line and the extension line, denoted as the first angle. Calculate the product of the fourth distance and the first angle between the superpixel cluster and each other superpixel cluster to obtain the information entropy of the superpixel cluster.

7. The method for constructing a three-dimensional model of building structure design based on spatial point cloud data according to claim 1, characterized in that According to the third distance and the information entropy, obtain the distribution factor of the clustering, including: Analyze the degree of dispersion between the third distances corresponding to all the superpixel clusters in the clustering, and analyze the degree of dispersion between the information entropies corresponding to all the superpixel clusters in the clustering to obtain the distribution factor of the clustering.

8. The three-dimensional model construction method of building structure design based on spatial point cloud data according to claim 1, characterized in that According to the noise influence degree, perform noise reduction processing on the spatial point cloud data, including: Perform noise reduction processing on the spatial point cloud data based on the voxel grid filtering algorithm, and adjust the voxel size in the voxel grid filtering algorithm according to the noise influence degree corresponding to each projection plane, and perform noise reduction processing on the spatial point cloud data corresponding to each projection plane.

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