Methods, media, and equipment for 3D point cloud edge extraction based on mesh numerical fields

Abstract

This invention relates to the field of 3D scanning technology, specifically to a method for 3D point cloud edge extraction based on a mesh numerical field. The method includes: S1: adding mesh attributes to each point cloud in a 3D point cloud model; S2: thinning the 3D point cloud model by X to obtain a point cloud set P1; S3: clustering multiple mesh clusters based on mesh attributes; S4: calculating the hull convex polygon of a single mesh cluster to obtain a vertex point cloud set; S5: if X < 100% and there are no discrete meshes in the vertex point cloud set, then the vertex point cloud set is the edge of the mesh cluster point cloud; otherwise, let X = 2X and proceed to S6; if X ≥ 100%, the vertex point cloud set is the edge of the mesh cluster point cloud; S6: subtracting P1 from the 3D point cloud model to obtain P2; thinning P2 by X to obtain P3; S7: adding the points in P3 to the hull convex polygon to obtain a new vertex point cloud set, and returning to S5. This invention reduces computational load and improves edge extraction accuracy.

Description

Technical Field

[0001] This invention relates to the field of 3D scanning technology, specifically to a method for 3D point cloud edge extraction based on a grid numerical field. Background Technology

[0002] In recent years, with the rapid development of remote sensing technology, LiDAR, and 3D scanning technology, 3D point cloud models have become an important data source in many fields such as Geographic Information Systems (GIS), urban planning, architectural design, and cultural relic protection. 3D point cloud models, with their high precision and rich geometric information, have shown great potential in terrain analysis and object recognition. However, how to effectively extract useful information from these complex 3D data, especially in the case of large-scale datasets, remains a technical challenge.

[0003] In 3D point cloud data processing, it is often necessary to combine point clouds with gridded numerical fields to analyze specific attributes, such as terrain changes and illumination intensity. Each grid in the numerical field carries attribute information about the region, such as height, density, and temperature. This combination helps to better understand the 3D scene and provides a foundation for further data analysis. After cluster analysis, it is often necessary to extract the 3D point cloud edges corresponding to the clustered grids. Existing methods often rely on some characteristics of the point cloud itself and lack a mapping between these characteristics and the grid attributes. Summary of the Invention

[0004] The purpose of this invention is to provide an efficient and accurate method for edge extraction of 3D point clouds based on a mesh numerical field. The specific technical solution is as follows:

[0005] This invention provides a method for extracting the edges of a 3D point cloud based on a grid numerical field, comprising the following steps:

[0006] S1: Construct a grid data field and a 3D point cloud model for a certain region; use feature point matching or least squares method to superimpose the grid data field and the 3D point cloud model for that region, that is, attach corresponding grid attributes to each point cloud in the 3D point cloud model respectively;

[0007] S2: Thinning the 3D point cloud model yields a thinned 3D point cloud set P1, where the thinning rate is X.

[0008] S3: A clustering algorithm is used to cluster the grids in the grid data field according to the grid attributes to obtain several grid clusters;

[0009] S4: Calculate the convex polygon corresponding to a single mesh cluster using the convex hull algorithm, and obtain the vertex cloud set of the convex polygon.

[0010] S5: Judgment: When X < 100%, if all the grids corresponding to the vertex point cloud set have at least one shared edge with other grids, then the vertex point cloud set is the point cloud edge corresponding to the extracted grid cluster; otherwise, let X = 2X and go to S6; when X ≥ 100%, then the vertex point cloud set is the point cloud edge corresponding to the extracted grid cluster.

[0011] S6: Subtract the thinned 3D point cloud set P1 from the 3D point cloud model to obtain point cloud set P2; and thin point cloud set P2 according to the thinning rate X to obtain the thinned 3D point cloud set P3.

[0012] S7: Using the incremental convex hull algorithm, the points in the thinned 3D point cloud set P3 are added one by one to the outer convex polygon in S4 to update the outer convex polygon, and the updated vertex point cloud set is obtained, and then returned to S5.

[0013] Optionally, the following further exists between S3 and S4:

[0014] Each grid in the grid data field is numbered to obtain the set of grid numbers Setg for each grid in a single grid cluster. Then, based on the set of numbers Setg, all point clouds within the single grid cluster are retrieved from the thinned 3D point cloud set P1, forming the point cloud set P of that grid cluster. t ;

[0015] In step S7, if the points in the thinned 3D point cloud set P3 are inside the circumferential convex polygon, then there is no need to update the circumferential convex polygon. If the points in the thinned 3D point cloud set P3 are outside the circumferential convex polygon, then it is necessary to update the circumferential convex polygon in S4 by inserting new edges or vertices.

[0016] Optionally, S4 includes:

[0017] S4.1 Construct a coordinate projection mapping function, and use the coordinate projection mapping function to map the point cloud set P t The point cloud coordinates in the image are projected from spatial coordinates to planar coordinates, resulting in a point cloud set P represented in planar coordinates. tf ;

[0018] S4.2. Obtain the point cloud set P using the convex hull algorithm. tf The set of vertices P of the circumscribed convex polygon vertex ;

[0019] S4.3 Construct the inverse function of the coordinate projection mapping function, and use the inverse function of the coordinate projection mapping function to map the vertex set P. vertex The point cloud coordinates in the image are projected from planar coordinates to spatial coordinates to obtain a set of vertex point clouds P represented in spatial coordinates. vk .

[0020] Optionally, S4.2 includes:

[0021] S4.2.1. Prepare data, let point cloud set P. tf The planar coordinates of the midpoint cloud are represented as (x i ,y i ), where i is the point cloud set P tf Point cloud numbering in the data;

[0022] S4.2.2 Find the lowest point and the point cloud set P. tf The point with the lowest y-value is used as the starting point for constructing the convex hull; if there are multiple such points, the point with the smallest x-value is selected as the starting point, where the coordinates of the starting point are (x0, y0).

[0023] S4.2.3 Calculate the vectors, specifically the starting point and the point cloud set P. tf The vector v between points other than the starting point i ;

[0024] S4.2.4 Calculate the polar angle and calculate the value of each vector v. i The polar angle relative to the positive x-axis, where: the polar angle is the counterclockwise angle from the positive x-axis to the vector;

[0025] S4.2.5 Sorting: Sort all non-starting point points in ascending order of polar angle. If two or more points have the same polar angle, the point farther from the starting point should be ranked first.

[0026] S4.2.6 Constructing the convex hull: The Graham scan method or Jarvis wrapping method is used to construct the convex hull, i.e., to enclose the convex polygon, and the vertex cloud set P of the enclosing convex polygon is obtained. vertex .

[0027] Optionally, in S4.2.4, the specific formula for calculating the polar angle is as follows:

[0028] θ i =atan2(y i -y0,x i -x0);

[0029] Where: θ i For v i The polar angle with the positive x-axis.

[0030] Optionally, the process between S4 and S5 further includes:

[0031] Extract the mesh number of each point cloud in the vertex point cloud set, remove duplicate mesh numbers to obtain the set of mesh numbers of the vertex point cloud, and the mesh corresponding to the set of mesh numbers of the vertex point cloud is all the meshes corresponding to the vertex point cloud set.

[0032] Optionally, in S2, X ≤ 10%.

[0033] The present invention also provides a readable storage medium storing computer program instructions, which, when executed by a processor, implement the three-dimensional point cloud edge extraction method based on a grid numerical field as described above.

[0034] The present invention also provides an electronic device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, wherein the computer program instructions are executed by the processor to perform the three-dimensional point cloud edge extraction method based on a grid numerical field as described above.

[0035] This invention overlays a grid numerical field onto a 3D point cloud model, endowing the point cloud points with grid attribute information, thus achieving accurate edge extraction based on attributes. This combination effectively enhances the edge extraction capability for complex terrains and structures, and is applicable to multiple fields such as geographic information systems and environmental monitoring.

[0036] By introducing an incremental convex hull algorithm, the method avoids recalculating the convex hull for the entire point set each time. Instead, it dynamically maintains the convex hull set and gradually adds new points for updating. This approach significantly reduces computational load, especially in multi-iterative calculations, where it can substantially shorten processing time and improve efficiency.

[0037] Clustering algorithms are used to cluster the mesh, allowing edge extraction operations to be focused on specific regions, avoiding global processing. Simultaneously, a thinning algorithm simplifies the original 3D point cloud model, reducing redundant point cloud data and accelerating subsequent computations. Furthermore, a dynamic adjustment mechanism for the thinning rate ensures that even at high thinning rates, sufficient key edge points are retained, improving overall processing efficiency.

[0038] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0039] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0040] Figure 1 This is a schematic diagram of a three-dimensional point cloud model in an embodiment of the present invention;

[0041] Figure 2 This is a schematic diagram of the grid clusters obtained by clustering in an embodiment of the present invention. Detailed Implementation

[0042] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, the present invention can be implemented in many different ways as defined and covered by the claims.

[0043] In one embodiment, see Figure 1 and Figure 2 A method for extracting the edges of 3D point clouds based on a grid numerical field includes the following steps:

[0044] S1: Construct a grid data field and a 3D point cloud model for a certain region; superimpose the grid data field and the 3D point cloud model for the region using feature point matching or least squares method, that is, attach corresponding grid attributes to each point cloud in the 3D point cloud model; the grid attributes include any one of temperature, humidity, altitude, light intensity, pollution concentration, and other physical or abstract quantities and grid number.

[0045] A gridded data field is a structured method for representing spatial data. It organizes data by dividing a continuous space into a series of discrete units (i.e., grids). Each grid unit carries information about specific attributes within that area. These attributes can be physical quantities (such as temperature, humidity, and altitude) or abstract quantities (such as light intensity and pollution concentration). Gridded data fields are widely used in Geographic Information Systems (GIS), weather forecasting, environmental monitoring, urban planning, and many other fields. The grids mentioned include both regular and irregular grids. Grid attributes can be represented using floating-point numbers. Each grid has its own unique identifier, typically a positive integer.

[0046] A 3D point cloud terrain model is a digital model that represents a terrain surface using three-dimensional coordinates. It consists of a large number of points, each with precise 3D coordinates (X, Y, Z), and these points collectively depict the 3D shape of the terrain. The 3D coordinates refer to coordinates in a spatial Cartesian 3D coordinate system, and the reference ellipsoid can be determined according to the actual situation.

[0047] The process of overlaying a 3D point cloud terrain model with a grid data field refers to matching each point in the 3D point cloud model with a grid cell in its corresponding grid data field, thereby assigning each point cloud point the attribute information carried by the grid cell. The main purpose of this process is to combine the geometric information of the point cloud model with the attribute information of the grid data field to achieve more comprehensive data analysis and more accurate decision support. One grid may correspond to multiple point clouds.

[0048] The overlay of the 3D point cloud terrain model with the grid data field can be achieved by solving the transformation matrix using techniques such as feature point matching or least squares method; alternatively, the method described in invention patent ZL202011110192.3 can be used. The ultimate goal is to establish a mapping relationship between the grid and the point cloud, that is, to add an attribute Sp to the point cloud, which represents the corresponding grid number.

[0049] S2: Thinning the 3D point cloud model to obtain the thinned 3D point cloud set P1, where: the thinning rate is X, X≤10%.

[0050] S3: A clustering algorithm is used to cluster the grids in the grid data field according to their grid attributes to obtain several grid clusters. The grid attributes are selected based on actual needs. In this embodiment, the grid attribute is light intensity; therefore, several grids with light intensities greater than a certain threshold are selected for clustering. The desired effect is that the light intensities of all grids within this cluster are greater than this threshold, and these grids are all connected together.

[0051] Between S3 and S4, there is also:

[0052] Each grid in the grid data field is numbered to obtain the set of grid numbers Setg for each grid in a single grid cluster. Then, based on the set of numbers Setg, all point clouds within the single grid cluster are retrieved from the thinned 3D point cloud set P1, forming the point cloud set P of that grid cluster. t ;

[0053] In step S7, if the points in the thinned 3D point cloud set P3 are inside the outer convex polygon, then there is no need to update the outer convex polygon. If the points in the thinned 3D point cloud set P3 are outside the outer convex polygon, then it is necessary to update the outer convex polygon in S4 by inserting new edges or vertices.

[0054] S4: Calculate the convex polygon corresponding to the mesh cluster using the convex hull algorithm, and obtain the vertex cloud set of the convex polygon.

[0055] S4 includes:

[0056] S4.1 Construct a coordinate projection mapping function, and use the coordinate projection mapping function to map the point cloud set P t The point cloud coordinates in the image are projected from spatial coordinates to planar coordinates, resulting in a point cloud set P represented by planar coordinates (x, y). tf ;

[0057] S4.2. Obtain the point cloud set P using the convex hull algorithm. tf The set of vertices P of the circumscribed convex polygon vertex ;

[0058] S4.2 includes:

[0059] S4.2.1. Prepare data, let point cloud set P. tf The planar coordinates of the midpoint cloud are represented as (x i ,y i ), where i is the point cloud set P tf Point cloud numbering in the data;

[0060] S4.2.2 Find the lowest point and the point cloud set P. tf The point with the lowest y-value is used as the starting point for constructing the convex hull; if there are multiple such points, the point with the smallest x-value is selected as the starting point, where the coordinates of the starting point are (x0, y0).

[0061] S4.2.3 Calculate the vectors, specifically the starting point and the point cloud set P. tf The vector v between points other than the starting point i ;

[0062] S4.2.4 Calculate the polar angle and calculate the value of each vector v. i The polar angle relative to the positive x-axis, where: the polar angle is the counterclockwise angle from the positive x-axis to the vector;

[0063] The specific formula for calculating the polar angle is as follows:

[0064] θ i =atan2(y i -y0,x i -x0);

[0065] Where: θ i For v i The polar angle with the positive x-axis.

[0066] S4.2.5 Sorting: Sort all non-starting point points in ascending order of polar angle; where: if two or more points have the same polar angle, the point farther from the starting point should be ranked first.

[0067] S4.2.6 Constructing the convex hull: The Graham scan method or Jarvis wrapping method is used to construct the convex hull, i.e., to enclose the convex polygon, and the vertex cloud set P of the enclosing convex polygon is obtained. vertex .

[0068] In S4.2.6, the specific steps for using the Graham scanning method are as follows:

[0069] Select point cloud set P tf The two points with the smallest polar angle are used as the initial stack;

[0070] Add point cloud sets P one by one in polar angle order. tf Points outside the initial stack;

[0071] For each newly added point p i Check if the last three points on the top of the stack form a left turn, i.e., the area of ​​the triangle formed is greater than 0. If not, pop the top of the stack until the left turn condition is met; then set p... i Push it onto the stack;

[0072] After all points have been processed, the remaining points in the stack constitute the vertex set P of the convex hull. vertex .

[0073] S4.3 Construct the inverse function of the coordinate projection mapping function, and use the inverse function of the coordinate projection mapping function to map the vertex set P. vertex The point cloud coordinates in the image are projected from planar coordinates to spatial coordinates to obtain a set of vertex point clouds P represented in spatial coordinates. vk .

[0074] The following also applies between S4 and S5:

[0075] Extract the mesh number of each point cloud in the vertex point cloud set, remove duplicate mesh numbers to obtain the set of mesh numbers of the vertex point cloud, and the mesh corresponding to the set of mesh numbers of the vertex point cloud is all the meshes corresponding to the vertex point cloud set.

[0076] S5: Judgment: When X < 100%, if all grids corresponding to the vertex point cloud set have at least one shared edge with other grids, then the vertex point cloud set is the point cloud edge corresponding to the extracted grid cluster; otherwise, let X = 2X and proceed to S6; when X ≥ 100%, then the vertex point cloud set is the point cloud edge corresponding to the extracted grid cluster. The method of identifying whether a grid has a shared edge with other grids can be based on image recognition.

[0077] S6: Subtract the thinned 3D point cloud set P1 from the 3D point cloud model to obtain point cloud set P2; and thin point cloud set P2 according to the thinning rate X to obtain the thinned 3D point cloud set P3.

[0078] S7: Using the incremental convex hull algorithm, the points in the thinned 3D point cloud set P3 are added one by one to the outer convex polygon in S4 to update the outer convex polygon, and the updated vertex point cloud set is obtained, and then returned to S5.

[0079] In S7, if the points in the thinned point cloud set P3 are inside the circumscribed convex polygon, then there is no need to update the circumscribed convex polygon. If the points in the thinned point cloud set P3 are outside the circumscribed convex polygon, then it is necessary to update the circumscribed convex polygon by inserting new edges or vertices.

[0080] A common method to determine whether a point is inside a convex hull is to use the ray casting method or the triangle covering method.

[0081] The ray-crossing method involves emitting a ray from the point to be measured in any direction and calculating the number of times this ray intersects the edge of the convex hull. If the number of intersections is even (including zero), the point is outside the convex hull; if it is odd, the point is inside the convex hull.

[0082] The specific steps are as follows:

[0083] Choose a direction to emit a ray from the point to be measured.

[0084] Calculate the number of intersections between the ray and all edges that form the convex hull.

[0085] If the number of intersection points is odd, the point is inside the convex hull; if the number is even, the point is outside the convex hull.

[0086] The triangle covering method involves decomposing the convex hull into a series of triangles and then checking whether the point to be measured is inside any of these triangles. This method is applicable to convex hulls in three-dimensional space.

[0087] In the two-dimensional case, a simpler method can be used to determine whether a point is inside a polygon. This can be done by checking the orientation of each triangle; specifically, checking if there exists at least one triangle such that the point being tested is to its left (counter-clockwise). If this condition is met for all triangles, then the point is inside the convex hull.

[0088] This embodiment also provides a readable storage medium storing computer program instructions, which, when executed by a processor, implement the three-dimensional point cloud edge extraction method based on a grid numerical field as described above.

[0089] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0090] This embodiment also includes an electronic device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, wherein the computer program instructions are executed by the processor to perform the three-dimensional point cloud edge extraction method based on the grid numerical field as described above.

[0091] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0092] The electronic device can be a mobile phone, desktop computer, laptop, handheld computer, cloud server, or other computing device. The electronic device may include, but is not limited to, processors and memory. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0093] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0094] The memory can be used to store the computer program and / or modules. The processor implements the computer program by running or executing the computer program and / or modules stored in the memory, and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0095] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0096] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for edge extraction of 3D point clouds based on a grid numerical field, characterized in that, Includes the following steps: S1: Construct a grid data field and a 3D point cloud model for a certain region; use feature point matching or least squares method to superimpose the grid data field and the 3D point cloud model for that region, that is, attach corresponding grid attributes to each point cloud in the 3D point cloud model respectively; S2: Thin the 3D point cloud model to obtain a thinned 3D point cloud set. P 1 Where: the dilution rate is X; S3: A clustering algorithm is used to cluster the grids in the grid data field according to the grid attributes to obtain several grid clusters; S4: Calculate the convex polygon corresponding to a single mesh cluster using the convex hull algorithm, and obtain the vertex cloud set of the convex polygon. S4 includes: S4.1 Construct a coordinate projection mapping function, and use the coordinate projection mapping function to map the point cloud set. The point cloud coordinates are projected from spatial coordinates to planar coordinates, resulting in a point cloud set represented in planar coordinates. ; S4.

2. Obtain the point cloud set using the convex hull algorithm. The set of vertices of the circumferential convex polygon ; S4.3 Construct the inverse function of the coordinate projection mapping function, and use the inverse function of the coordinate projection mapping function to map the vertex set. The point cloud coordinates in the image are projected from planar coordinates to spatial coordinates to obtain a set of vertex point clouds represented in spatial coordinates. ; S4.2 includes: S4.2.

1. Prepare data and set up a point cloud dataset. The planar coordinates of the midpoint cloud are represented as follows: ,in It is a collection of point clouds Point cloud numbering in the data; S4.2.2 Find the lowest point and find the point cloud set. The lowest The point with the smallest x-value is used as the starting point for constructing the convex hull; if there are multiple such points, the point with the smallest x-value is selected as the starting point, where the coordinates of the starting point are: ; S4.2.3 Calculate the vectors, specifically the starting point and the point cloud set. Vectors between points other than the starting point ; S4.2.4 Calculate the polar angle and calculate the polar angle for each vector. The polar angle relative to the positive x-axis, where: the polar angle is the counterclockwise angle from the positive x-axis to the vector; S4.2.5 Sorting: Sort all non-starting point points in ascending order of polar angle. If two or more points have the same polar angle, the point farther from the starting point should be ranked first. S4.2.6 Constructing the convex hull: The Graham scan method or Jarvis wrapping method is used to construct the convex hull, i.e., to enclose the convex polygon, and the vertex cloud set of the enclosing convex polygon is obtained. ; S5: Judgment: When X < 100%, if all the grids corresponding to the vertex point cloud set have at least one shared edge with other grids, then the vertex point cloud set is the point cloud edge corresponding to the extracted grid cluster; otherwise, let X = 2X and go to S6; when X ≥ 100%, then the vertex point cloud set is the point cloud edge corresponding to the extracted grid cluster. S6: Subtract the thinned 3D point cloud set from the 3D point cloud model. P 1 , obtain point cloud set P 2 And for point cloud sets P 2 Thinning is performed according to the thinning rate X, resulting in a further thinned 3D point cloud set. P 3 ; S7: Use the incremental convex hull algorithm to further thin the 3D point cloud set. P 3 The points in S4 are added one by one to the outer convex polygon in S4 to update the outer convex polygon, and the updated vertex point cloud set is obtained, and then returned to S5.

2. The method for extracting three-dimensional point cloud edges based on a grid numerical field according to claim 1, characterized in that, Between S3 and S4, there also includes: Each grid in the grid data field is numbered to obtain the set of grid numbers Setg in a single grid cluster; and the number set Setg is then used to create a 3D point cloud set after thinning. P 1 The point cloud of a single grid cluster is retrieved and formed into a set of point clouds for that grid cluster. ; In S7, if the three-dimensional point cloud set is further thinned... P 3 If a point in the set is located inside the convex bounding polygon, then there is no need to update the convex bounding polygon. However, if the 3D point cloud set is further thinned... P 3 If a point in S4 is located outside the circumscribed convex polygon, then the circumscribed convex polygon in S4 needs to be updated by inserting new edges or vertices.

3. The method for extracting the edges of a 3D point cloud based on a grid numerical field according to claim 2, characterized in that, In S4.2.4, the specific formula for calculating the polar angle is as follows: ； in: for The polar angle with the positive x-axis.

4. The method for extracting three-dimensional point cloud edges based on a mesh numerical field according to any one of claims 2-3, characterized in that, Between S4 and S5, there is also: Extract the mesh number of each point cloud in the vertex point cloud set, remove duplicate mesh numbers to obtain the set of mesh numbers of the vertex point cloud, and the mesh corresponding to the set of mesh numbers of the vertex point cloud is all the meshes corresponding to the vertex point cloud set.

5. The method for extracting three-dimensional point cloud edges based on a mesh numerical field according to any one of claims 1-3, characterized in that, The grid attributes include any one of temperature, humidity, altitude, light intensity, pollution concentration, and other physical or abstract quantities, as well as the grid number.

6. The method for extracting three-dimensional point cloud edges based on a mesh numerical field according to any one of claims 1-3, characterized in that, In S2, X ≤ 10%.

7. A readable storage medium, characterized in that, It stores computer program instructions, which, when executed by a processor, implement the three-dimensional point cloud edge extraction method based on a grid numerical field as described in any one of claims 1 to 6.

8. An electronic device, characterized in that, include: The method for edge extraction of three-dimensional point clouds based on a grid numerical field as described in any one of claims 1 to 6 includes at least one processor, at least one memory, and computer program instructions stored in the memory, which are executed by the processor when the computer program instructions are executed.

Images