Point cloud model optimization method and base station site selection method based on point cloud model feature extraction

Abstract

The present application relates to the technical field of space modeling and three-dimensional point cloud data processing, and particularly relates to a point cloud model optimization method and a base station point selection method based on point cloud model feature extraction. The present application first performs down-sampling on a point cloud model, then for each point, obtains a unit normal vector of a fitting plane of the point and its neighborhood points as the unit normal vector of the point; performs normal vector optimization on each point in the simplified point cloud; calculates the feature measure of each point in combination with the optimized unit normal vector of each point in the simplified point cloud, and selects points with feature measures located in a set feature interval to form a feature point cloud. The present application realizes data simplification of the point cloud model while preserving the original features of the point cloud model.

Description

Technical Field

[0001] The present invention relates to the technical field of spatial modeling and three-dimensional point cloud data processing, in particular to a point cloud model optimization method and a base station selection method based on point cloud model feature extraction. Background Art

[0002] A point cloud model is a collection of spatial points generated by laser scanning, photogrammetry or other three-dimensional measurement technologies. These points have specific coordinates (X, Y, Z) in three-dimensional space and may contain other attribute information such as color, intensity, etc. Point cloud models can accurately represent the geometry of objects or scenes and are widely used in architecture, engineering, geographic information systems (GIS), reverse engineering, and cultural heritage protection. Since point cloud data contains a large amount of three-dimensional information, processing and analyzing this data usually requires specific software tools and algorithms. Through point cloud models, users can perform operations such as visualization, measurement, modeling, and feature extraction, thereby providing support for subsequent design, analysis, and decision-making. The advantages of point cloud models lie in their high accuracy and rich information. However, due to the large amount of data, processing and storage also face certain challenges.

[0003] Point cloud scanning base stations are fixed locations set during laser scanning or 3D point cloud acquisition. These base stations serve as reference points for the scanning device, ensuring the accuracy and consistency of the scanned data. During the scanning process, each base station collects point cloud data of the surrounding environment to form a local 3D model. By performing multiple scans at different base stations and aligning and merging these scan results, a more complete and detailed 3D point cloud model can be generated. The selection of base stations is typically based on scene visibility, scanning range, and occlusion to ensure that all important details and features are covered. In practical applications, the precise positioning and data processing of base stations are crucial to improving the quality of point cloud models and the reliability of subsequent analysis.

[0004] The selection of traditional base station points for point cloud data scanning relies mainly on human judgment, with base station sites randomly selected and arranged. This method of base station selection has not been tested through computer simulation experiments and is too arbitrary. This may result in some areas not being fully scanned, resulting in missing and incomplete point cloud data. At the same time, if the angle of the selected site is inappropriate, the quality of the point cloud data will be reduced and the data overlap will be insufficient. Point clouds with low repetitive data scanned from different sites will also cause seams or discontinuities when merging point clouds. Sometimes, people set up too many base stations to avoid these problems, but in essence, the selection of base stations still relies on human judgment. Setting too many base stations will also cause unnecessary waste of resources.

[0005] BIM (Building Information Modeling) is a digital approach to building design and management. It creates a three-dimensional model of a building, integrating its geometry, physical properties, functional data, and relevant time and cost information. A BIM model is more than just a 3D visualization; it also includes rich attribute information, such as materials, structure, equipment, and construction phases. This supports all stages of a building's lifecycle, including design, construction, operation, and maintenance. BIM's core advantage lies in its collaborative capabilities, enabling multiple professional teams to collaborate in real time on the same model, promptly identifying and resolving design conflicts and improving efficiency and accuracy. BIM also enables simulation analysis, such as energy consumption analysis, structural analysis, and construction schedule simulation, helping decision makers make more informed decisions. As the construction industry continues to advance its digital transformation, BIM is becoming a standard tool for modern building design and management. However, converting BIM models into point cloud models often results in large amounts of data and extremely low processing efficiency. Summary of the Invention

[0006] In order to overcome the defects of low computational efficiency and disordered normal vectors of huge point cloud models in the above-mentioned prior art, the present invention proposes a point cloud model optimization method, which can correct the normal vectors of the point cloud model, reduce the data volume and data errors, and thus improve the point cloud computing efficiency.

[0007] In order to overcome the problem of redundant or unreasonable distribution of base station settings, the present invention also proposes a base station selection method based on point cloud model feature extraction, which uses the BIM model to predict the optimal site for point cloud collection in advance, effectively solving the problems of redundant base station settings and missing and incomplete point cloud data.

[0008] The present invention proposes a point cloud model optimization method, comprising the following steps:

[0009] S1. Downsample the point cloud model to obtain a simplified point cloud;

[0010] S2. For each point in the simplified point cloud, obtain the unit normal vector of the fitting plane between the point and its neighboring points as the unit normal vector of the point;

[0011] S3. Optimize the normal vector of each point in the simplified point cloud. The optimization goal is:

[0012]

[0013]

[0014] Among them, w q (n i , n j) represents the point p in the simplified point cloud i and point p j The weight in the normal domain, w d (p i , p j ) represents the point p in the simplified point cloud i and point p j Weight in the spatial domain; n i For point p i The unit normal vector, n j For point p j The unit normal vector of point p in the simplified point cloud is i Neighborhood point set; min means taking the minimum value; σ s is the set spatial domain Gaussian bandwidth, σ n is the set normal domain Gaussian bandwidth; exp is the exponential function;

[0015] S4. Calculate the feature measure of each point based on the optimized unit normal vector of each point in the simplified point cloud, and select points whose feature measure is within the set feature interval to form a feature point cloud.

[0016] Preferably, in step S4, the simplified point p in the point cloud is i The characteristic measure of is obtained as follows:

[0017] First, construct the points p i The neighborhood point tensor voting matrix and the neighborhood normal vector tensor voting matrix

[0018]

[0019] Among them, η(p i , p j , n j ) is the attenuation coefficient, N′(i) represents the point p i The neighborhood point set of is the center point of the neighborhood corresponding to N′(i); the superscript T represents the matrix transpose; p j Indicates p i Neighborhood points; n j For point p j The unit normal vector of ; the unit normal vector is expressed as a three-dimensional column vector;

[0020] Then the neighborhood point tensor voting matrix and the neighborhood normal vector tensor voting matrix Perform feature decomposition and extract and The three-dimensional features of The three-dimensional eigenvalues ​​of and and make The three-dimensional eigenvalues ​​of and and

[0021] Calculate point p i The characteristic measure f i :

[0022]

[0023] in, For point p i The neighborhood feature degree of Represents point p i The neighborhood point characteristic degree of the neighborhood normal vector.

[0024] Preferably, the attenuation coefficient η(p i , p j , n j ) is calculated as:

[0025]

[0026] Among them, point p j For point p i Neighborhood point, R is point p i Point to point p j The unit vector, n j Represents point p j The unit normal vector; the superscript T represents the matrix transpose, σ s is the Gaussian bandwidth; ||.||2 represents the second norm.

[0027] Preferably, point p i The neighborhood point set N(i) coincides with N′(i).

[0028] Preferably, the method for downsampling the point cloud model in S1 is: using voxel grids to segment the point cloud model, and using the voxel grids where sampling points exist as mass points to construct a simplified point cloud.

[0029] Preferably, point p i The neighborhood point set N(i) corresponds to the neighborhood of point p i is a spherical domain with a center and a radius of r1; the side length of the voxel grid of the segmented point cloud model is r, and r≤r1≤2r is set.

[0030] The present invention proposes a base station selection method based on point cloud model feature extraction, comprising the following steps:

[0031] St1. Build a BIM model of the target area and convert the BIM model into a point cloud model;

[0032] St2. Set a feature interval, execute the point cloud model optimization method according to any one of claims 1 to 6 on the point cloud model, and obtain a feature point cloud;

[0033] St3, set the number of clusters and cluster the feature point cloud;

[0034] St4. After the clustering is completed, the center of each cluster is used as the base station.

[0035] Preferably, the upper limit value of the characteristic interval is in the range of [0.3, 0.5], and the lower limit value of the characteristic interval is in the range of [0.01, 0.1].

[0036] The present invention proposes a target point selection system based on point cloud model feature extraction, comprising:

[0037] A point cloud acquisition module is used to connect to an external device to acquire a point cloud model; the external device is a modeling device or a 3D scanning device;

[0038] A point cloud optimization module stores a computer program, which is used to implement the point cloud model optimization method when executed to obtain a feature point cloud;

[0039] The clustering module is used to cluster the feature point cloud. The initial center point of the cluster is the feature point extracted from the feature point cloud. The clustering module outputs the center point of each cluster as the target point when clustering is completed.

[0040] The present invention provides a storage medium storing a computer program, which is used to implement the point cloud model optimization method when executed.

[0041] The advantages of the present invention are:

[0042] (1) The present invention proposes a point cloud model optimization method, which first simplifies the point cloud by downsampling, and then extracts feature points by combining the fitting plane and normal vector to construct a feature point cloud, thereby greatly simplifying the point cloud density and retaining the point cloud features. While retaining the original features of the point cloud model, the data simplification of the point cloud model is achieved.

[0043] (2) In the present invention, the feature point extraction process relies on the normal vector optimization to calculate the feature measure of the data point. The normal vector optimization process simultaneously considers the normal domain and the spatial domain, so that the feature measure of the data point takes into account multiple features, thereby ensuring the comprehensive screening of high-characteristic data points.

[0044] (3) In this invention, the feature measure is calculated by combining the eigenvalues ​​of the dual voting matrix, taking into account the domain tensor and the neighborhood normal vector, so that the selected feature points can cover the neighborhood, thereby improving the representativeness of the feature points. In this invention, the voxel grid method is used to downsample the point cloud model, making the points in the simplified point cloud more spatially representative and the data points more evenly distributed, which facilitates subsequent calculations and processing.

[0045] (4) The present invention proposes a method for selecting base station points based on feature extraction of a point cloud model. First, the BIM model of the target is obtained and converted into a point cloud model. The point cloud model is then optimized to extract a simplified point cloud and a feature point cloud. The feature point cloud is then clustered. The number of clusters and the location of each cluster serve as the basis for selecting base station points. The present invention proposes using the BIM model to predict base station points in advance, which can reasonably reduce the workload of setting up on-site base stations and collecting point cloud data.

[0046] (5) The present invention uses the BIM model to predict the scanning base points of the point cloud model in advance, and uses machines to replace manual setting of scanning points, which can effectively reduce the excessive setting of base points and unnecessary waste of resources. Moreover, the machine replaces the manually determined scanning base points, which can comprehensively scan the entire building, and the scanned point cloud model will have a certain degree of repeatability, which greatly reduces the work of point cloud model registration. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A flow chart of a point cloud model optimization method;

[0048] Figure 2 Flowchart of the characteristic measure calculation method;

[0049] Figure 3 This is a flow chart of a base station selection method based on point cloud model feature extraction;

[0050] Figure 4 It is the BIM model drawing;

[0051] Figure 5 is the point cloud model diagram;

[0052] Figure 6 is the feature point cloud;

[0053] Figure 7 Schematic diagram of feature point cloud clustering;

[0054] Figure 8 Some base points and feature point clouds. DETAILED DESCRIPTION

[0055] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0056] Reference Figure 1 , a point cloud model optimization method proposed in this embodiment includes the following steps S1-S4.

[0057] S1. Downsample the point cloud model to obtain a simplified point cloud;

[0058] In this step, the minimum bounding box that can enclose the point cloud model can be calculated first; the maximum coordinate value X of the minimum bounding box on the X axis is defined as max , minimum coordinate value X min and side length L x ; The maximum coordinate value Y of the minimum bounding box on the Y axis max , minimum coordinate value Y min and side length L y ; The maximum coordinate value Z of the minimum bounding box on the Z axis max , minimum coordinate value Z min and side length L z ;L x =X max -X min ;L y =Y max -Y min ;L z =Z max -Z min ; Then get the mapping relationship between the point cloud model and the simplified point cloud:

[0059] (x,y,z)→(h x , h y , h z )

[0060]

[0061] Among them, (x, y, z) is the point in the point cloud model, (h x , h y , h z ) is the point (x, y, z) in the point cloud model corresponding to the point in the simplified model;

[0062] The length of the minimum bounding box of the simplified model on the X axis is recorded as D x , the length of the side on the Y axis is denoted as D y , the side length on the Z axis is denoted as D z;have:

[0063]

[0064]

[0065] Indicates rounding down. Indicates rounding up.

[0066] S2, calculate the normal vector of each sampling point in the simplified point cloud;

[0067] First, set the radius r1, which is greater than the side length r of the voxel grid. Specifically, set r≤r1≤2r; define the simplified point cloud with point p i The area with the center and radius r1 is point p i The first neighborhood of , simplifies the point p in the point cloud that is in the first neighborhood j All points are p i The first neighboring point of

[0068] Then the fitting point p i and all its first neighboring points p j The plane of the point p is obtained by taking the unit normal vector of the plane as the point p. i Specifically, in this step, first define the fitting point p i and all its first neighboring points p j The set of is denoted as G(i), let g i ∈G(i), calculate the center of mass and the covariance matrix M i , then the covariance matrix M i Perform eigendecomposition to obtain M i The three-dimensional eigenvalues ​​of and and M i The three-dimensional feature vector of and

[0069]

[0070] make but The corresponding eigenvector That is point p i The normal vector of , taking the unit value of the normal vector, is the unit normal vector.

[0071] It is worth noting that the currently obtained unit normal vector is ambiguous, that is, only the line where the normal vector is located is obtained, but the direction of the end of the line is not determined to be the direction of the normal vector; in this case, traverse the points in the simplified point cloud, specify the normal vector direction of the first point p1, and then calculate the normal vector direction of the first point p1 according to n. i·n i+1 The condition of ≤0 determines the direction of the unit normal vector of each point in the simplified point cloud in turn; n i For point p i The unit normal vector, n i+1 For point p i+1 Unit normal vector; this step can be specifically done using Kd-tre e The search algorithm traverses the simplified point cloud.

[0072] S3, optimize and simplify the unit normal vector of each point in the point cloud;

[0073] Click p i The optimization goal of the unit normal vector is:

[0074]

[0075] Among them, w q (n i , n j ) represents the point p in the simplified point cloud i and point p j The weight in the normal domain, w d (p i , p j ) represents the point p in the simplified point cloud i and point p j Weight in the spatial domain; n i For point p i The unit normal vector, n j For point p j The unit normal vector of point p in the simplified point cloud is i The first neighborhood point set of ; min means taking the minimum value; σ s is the set spatial domain Gaussian bandwidth, σ n The Gaussian bandwidth of the normal domain is set, and the specific setting σ s and σ s The value is r1; exp is the exponential function.

[0076] S4. Calculate the feature measure of each point based on the optimized unit normal vector of each point in the simplified point cloud, and select points whose feature measure is within the set feature interval to form a feature point cloud.

[0077] Specifically, the unit normal vector is a column vector representing the direction, with point p j As an example, the unit normal vector n j ={characteristic value of the angle with the x-axis; characteristic value of the angle with the y-axis; characteristic value of the angle with the z-axis} T .

[0078] Reference Figure 2 , S4 specifically includes the following sub-steps:

[0079] S41, constructing a neighborhood point tensor voting matrix;

[0080]

[0081] Among them, p i represents the i-th sampling point in the simplified point cloud, For p i The neighborhood point tensor voting matrix; N′(i) represents the point p i The set of points in the second neighborhood divided by the center and radius r2; j Indicates p i Neighborhood points; n j For point p j The unit normal vector of ; For p i The center point of the second neighborhood of η(p i , p j , n j ) is the attenuation coefficient;

[0082]

[0083] Where R is point p i Point to point p j The unit vector, n j Represents point p j The unit normal vector; the superscript T represents the matrix transpose, σ s is the Gaussian bandwidth; ||.||2 represents the second norm; Represents point p i and p j The square of the distance.

[0084] S42, constructing a neighborhood normal vector tensor voting matrix;

[0085]

[0086] in, For point p i The neighborhood normal vector tensor voting matrix of n j For point p j The unit normal vector of .

[0087] S43. Extraction The characteristic value of

[0088] because are all symmetric semi-positive matrices, they can be eigendecomposed and expressed as spectral components

[0089]

[0090] in The matrices The eigenvalues ​​and eigenvectors of , and the eigenvalues ​​are arranged in ascending order, that is, The matrices The eigenvalues ​​and eigenvectors of , the same eigenvalues ​​are arranged in ascending order, that is

[0091] S44. Calculate point p based on the dual tensor voting matrix i Feature measurement;

[0092]

[0093] in, For point p i The neighborhood feature degree of Represents point p i Neighborhood point characteristic degree of the neighborhood normal vector;

[0094] Calculate point p i Feature measure f i Then compare it with the set threshold μ and extract f i Points with μ ≥ μ are considered as feature points.

[0095] Generally, points on the corners of the point cloud model (such as corners, endpoints, and edges) will show more significant feature measures f i , making it easier to be judged as a feature point. In specific implementation, by setting a threshold, the ratio of feature points can also be controlled, so that feature points with larger feature measures are retained first, further simplifying the point cloud.

[0096] In subsequent experiments, the feature measure f corresponding to the inflection point of the point cloud model i is 0.25; in this way, in order to ensure that all feature points are screened, the feature measure f i The screening interval is set to [0.05, 0.4], which is equivalent to setting μ = 0.05; the setting of 0.4 can clean outliers.

[0097] Reference Figure 3 The present embodiment proposes a base station selection method based on point cloud model feature extraction, comprising the following steps:

[0098] St1, first build the building information model (BIM model) of the target area and convert the building information model into a point cloud model;

[0099] Specifically, in this step, you can import the BIM model into Revit 2020, install the Obj plug-in, and export the imported BIM model to an Obj model through the Obj plug-in; then import the Obj model into the software CloudCompare to convert it into a point cloud model in .pcd or .ply format. Both formats can be interpreted and read by Python, which facilitates subsequent point cloud processing operations.

[0100] St2. Execute the above steps S1-S4 on the point cloud model to obtain the feature point cloud.

[0101] In specific implementation, the feature point cloud can be selected in a targeted manner by setting the feature interval in step S4. For example, the points on the corners of the point cloud model will show more significant feature measurements, and the feature measurement corresponding to the corner is 0.25, so the feature interval can be set to [0.05, 0.4]. This feature interval can include most of the feature points in the simplified point cloud, and the points selected according to the set interval have a lower density in the simplified point cloud.

[0102] Step 3: Set the number of clusters and cluster the feature point cloud. In this step, the K-Means clustering algorithm can be used.

[0103] St4. After the clustering is completed, the center of each cluster is used as the base station.

[0104] The above-mentioned base station selection method based on point cloud model feature extraction is verified in conjunction with specific embodiments below.

[0105] The BIM model constructed in this embodiment is as follows Figure 4 As shown, the corresponding point cloud model is as follows Figure 5 As stated.

[0106] Set the voxel grid side length r = 3, r1 = r2 = 5; set the spatial domain Gaussian bandwidth σ s and the normal domain Gaussian bandwidth σ n The first neighborhood radius r1 is used; that is, σ s =σ n =5.

[0107] Table 1. Feature measurement values ​​of various points

[0108]

[0109] It can be seen that the feature measures of the feature points (inflection points and edge points) expected to be selected in this embodiment are 0.251010 and 0.063336 respectively, and the feature measure of the plane point is 0.000005; therefore, in this embodiment, the feature interval of the feature point cloud screened in S4 is set to [0.05, 0.400], and the points with feature measures within the interval [0.05, 0.400] are extracted from the simplified point cloud to formFigure 6 The feature point cloud shown contains 6913 points, which can fully reflect the Figure 5 Point cloud model features shown.

[0110] After clustering the feature point cloud in this embodiment, the clusters of points in the feature point cloud are as follows: Figure 7 As shown, feature points of the same color belong to the same cluster.

[0111] In this embodiment, some base stations obtained after the feature point cloud clustering is completed are Figure 7 The corresponding relationship of the midpoint set A is as follows Figure 8 As shown in Figure 2, any point in the point cloud can be covered by two or more base station locations, ensuring the uniformity and effectiveness of the base station location layout.

[0112] Of course, it will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, but also encompasses the same or similar structures that can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and it is intended that all variations that fall within the meaning and range of equivalents of the claims be encompassed within the present invention. Any reference signs in the claims should not be construed as limiting the claim to which they relate.

[0113] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0114] The technology, shape, and structure not described in detail in the present invention are all well-known technologies.

Claims

1. A point cloud model optimization method, characterized in that: The following steps are involved: S1. Downsample the point cloud model to obtain a simplified point cloud; S2. For each point in the simplified point cloud, obtain the unit normal vector of the fitting plane between the point and its neighboring points as the unit normal vector of the point; S3. Optimize the normal vector of each point in the simplified point cloud. The optimization goal is: Among them, w q (n i ,n j ) represents the point p in the simplified point cloud i and point p j The weight in the normal domain, w d (p i ,p j ) represents the point p in the simplified point cloud i and point p j Weight in the spatial domain; n i For point p i The unit normal vector, n j For point p j The unit normal vector of point p in the simplified point cloud is i Neighborhood point set; min means taking the minimum value; σ s is the set spatial domain Gaussian bandwidth, σ n is the set normal domain Gaussian bandwidth; exp is the exponential function; S4, calculating the feature measure of each point based on the optimized unit normal vector of each point in the simplified point cloud, and selecting points whose feature measure is within the set feature interval to form a feature point cloud; The simplified point p in the point cloud in step S4 i The characteristic measure of is obtained as follows: First, construct the points p i The neighborhood point tensor voting matrix and the neighborhood normal vector tensor voting matrix Among them, η(p i ,p j ,n j ) is the attenuation coefficient, N'(i) represents the point p i The neighborhood point set of is the center point of the neighborhood corresponding to N'(i); the superscript T indicates the matrix transpose; p j Indicates p i Neighborhood points; n j For point p j The unit normal vector of ; the unit normal vector is expressed as a three-dimensional column vector; Then the neighborhood point tensor voting matrix and the neighborhood normal vector tensor voting matrix Perform feature decomposition and extract and The three-dimensional features of The three-dimensional eigenvalues ​​of and and make The three-dimensional eigenvalues ​​of and and Calculate point p i The characteristic measure f i : in, For point p i The neighborhood feature degree of Represents point p i The neighborhood point characteristic degree of the neighborhood normal vector.

2. The point cloud model optimization method according to claim 1, wherein: Attenuation coefficient η(p i ,p j ,n j ) is calculated as: Among them, point p j For point p i Neighborhood point, R is point p i Point to point p j The unit vector, n j Represents point p j The unit normal vector; the superscript T represents the matrix transpose, σ s is the Gaussian bandwidth; ||.||2 represents the second norm.

3. The point cloud model optimization method according to claim 1, wherein: Click p i The neighborhood point set N(i) coincides with N'(i).

4. The point cloud model optimization method according to claim 1, wherein: The method for downsampling the point cloud model in S1 is: using voxel grids to segment the point cloud model, and using the voxel grids with sampling points as mass points to construct a simplified point cloud.

5. The point cloud model optimization method according to claim 4, characterized in that: Click p i The neighborhood point set N(i) corresponds to the neighborhood of point p i is a spherical domain with the center and radius r1; The side length of the voxel grid of the segmented point cloud model is r, and r≤r1≤2r is set.

6. A method for selecting base point points based on point cloud model feature extraction using the point cloud model optimization method according to any one of claims 1 to 5, characterized in that: The following steps are involved: St1. Build a BIM model of the target area and convert the BIM model into a point cloud model; St2. Set a feature interval, and execute the point cloud model optimization method according to any one of claims 1 to 5 on the point cloud model to obtain a feature point cloud; St3, set the number of clusters and cluster the feature point cloud; St4. After the clustering is completed, the center of each cluster is used as the base station.

7. The base point selection method based on point cloud model feature extraction according to claim 6, characterized in that: The upper limit of the characteristic interval ranges from [0.3, 0.5], and the lower limit of the characteristic interval ranges from [0.01, 0.1].

8. A target point selection system based on point cloud model feature extraction, characterized in that: include: A point cloud acquisition module is used to connect to an external device to acquire a point cloud model; the external device is a modeling device or a 3D scanning device; a point cloud optimization module storing a computer program, wherein the computer program is used to implement the point cloud model optimization method according to any one of claims 1 to 5 to obtain a feature point cloud when executed; Clustering module, used to cluster the feature point cloud, and the initial center point of the cluster is the feature point extracted from the feature point cloud; The clustering module outputs the center point of each cluster as the target point when clustering is completed.

9. A storage medium, characterized in that: A computer program is stored, and when the computer program is executed, it is used to implement the point cloud model optimization method according to any one of claims 1 to 5.

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