A method and system for adaptive tiling and thinning of 3D geographic point clouds

Abstract

This invention provides a method and system for adaptive slicing and thinning of 3D geographic point clouds. The method includes: calculating the effective range and density of a 3D discrete point cloud, using the point cloud density as the initial slice thickness; selecting the direction with the largest effective range as the main direction, and then sequentially slicing the unordered 3D discrete points along the main direction to obtain sliced ​​point clouds; projecting these sliced ​​point clouds onto the plane where the center of the sliced ​​point cloud is located to obtain the 2D cross-sectional points of the slice; calculating the difference between the inner and outer contours when the sliced ​​point cloud is projected onto the center of the cross-section, and determining whether it exceeds a corresponding preset shape error threshold; if not, determining that the current slice layer is complete and processing the next slice layer; if so, reducing the slice thickness and re-slicing the current slice layer; sequentially calculating the convex hull of the discrete point set on the corresponding cross-sections of all sliced ​​point cloud data, and thinning the feature points; sorting all the convex hulls of the cross-sections according to the main direction, connecting each slice, and obtaining the overall thinned point cloud model.

Description

Technical Field

[0001] This invention belongs to the field of three-dimensional geographic information processing technology, specifically relating to an adaptive slicing thinning scheme for disordered three-dimensional discrete point clouds. Background Technology

[0002] With the development of 3D laser scanning technology, acquiring disordered 3D discrete point clouds is a fundamental data source for 3D spatial information processing. However, point cloud data often suffers from problems such as large data volume, excessive data redundancy, and high storage space consumption, which hinders subsequent point cloud data processing such as point cloud filtering, feature extraction, and 3D modeling. Therefore, it is necessary to thin point cloud data while ensuring its accuracy. A classic point cloud thinning method mainly uses the grid center marking method. This method meshes the original point cloud according to its range, obtains the point cloud within the grid range, and fits it to the centroid. This method is efficient, but requires temporary file storage of the mesh-trimmed point cloud, adding extra file read / write tasks. Another commonly used point cloud thinning method is the random sampling method, which includes thinning based on virtual regular grids and system-based thinning methods. These two methods are relatively simple and convenient, but they ignore point cloud feature information, cannot well preserve the information of the original data, and have lower accuracy. Some researchers have also used the Poisson disk sampling thinning method. The DEM generated from the point cloud using this method has high accuracy and good visualization effects, but its adaptability to urban terrain is relatively limited. Curvature-based compression methods can preserve good feature information in sharp and complex areas, and perform well in compressing complex models. However, overcompression can occur in areas with little curvature variation, even resulting in large holes. Furthermore, these methods are complex, requiring quadratic surface fitting and least-squares fitting of nearest neighbors, leading to low processing efficiency. Currently, a compression method based on massive scattered point clouds has emerged, combining slicing techniques from the rapid prototyping field. This method uniformly slices the point cloud and then uses the chord-height difference method to preserve feature points in each slice. However, this method uses uniform slicing, resulting in poor adaptability to slice thickness. Smooth areas may develop holes due to overcompression, while complex areas may lose important features due to excessively thick slices. In summary, existing point cloud thinning methods cannot simultaneously address both feature information and efficiency issues. Therefore, this invention proposes an adaptive point cloud slicing thinning method that considers both feature information and processing efficiency. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention proposes an adaptive slicing and thinning scheme for three-dimensional geographic point clouds.

[0004] To achieve the above objectives, the technical solution provided by this invention includes a three-dimensional geographic point cloud adaptive slicing thinning method, comprising the following steps:

[0005] Step 1, calculate the effective range and density of the three-dimensional discrete point cloud, including the following sub-steps: Step 1.1, count the maximum and minimum values ​​of the point cloud points in the X, Y and Z directions, and perform histogram to count the effective range of the point cloud, and remove drifting points and isolated points of the point cloud.

[0006] Step 1.2: Extract the point cloud density as the initial slice thickness;

[0007] Step 2: Select the direction with the largest effective range in the X, Y, and Z directions as the main direction, and then slice the disordered 3D discrete points in the current layer along the main direction in sequence to obtain the sliced ​​point cloud, and then proceed to step 3.

[0008] Step 3: Project the slice point cloud obtained in Step 2 of this iteration onto the plane where the center of the slice point cloud is located to obtain the two-dimensional cross-sectional points of the slice;

[0009] Step 4: Calculate the difference between the inner and outer contours when the sliced ​​point cloud is projected onto the center of the section, and obtain the maximum value of the projection width w along the main direction. max Determine w max If the error is greater than the corresponding preset shape error threshold θ, then the current slice layer is considered complete. Return to step 2 to process the next slice layer until the point cloud adaptive slicing within the effective range is completed, and proceed to step 5. If the error is found, reduce the slice thickness and return to step 2 to re-slice the current slice layer.

[0010] Step 5: Calculate the convex hull of the discrete point set on the corresponding slice plane for all slice point cloud data in sequence, and determine the feature points to be thinned.

[0011] Step 6: Sort all the convex hulls of the cut surfaces according to the main direction, connect each slice, and obtain the overall thinned point cloud model.

[0012] Furthermore, in step 1.1, the maximum and minimum coordinates of the point cloud in the X, Y, and Z directions are first counted. Histograms are then plotted for the number of points in each of the X, Y, and Z directions, with intervals of Δx, Δy, and Δz. Noise points at the maximum and minimum boundaries of the three histograms are then removed to obtain the effective range of the point cloud. The formulas for calculating Δx, Δy, and Δz are as follows.

[0013] Δx=(X max -X min ) / n

[0014] Δy=(Y max -Y min ) / n

[0015] Δz=(Z max -Z min ) / n

[0016] In the formula, X max X min Y max Y min Z max Z min Let n be the maximum and minimum values ​​in the X, Y, and Z directions, and n be the dimension of the one-dimensional array in the histogram.

[0017] Furthermore, the phrase "slicing the disordered 3D discrete points sequentially along the main direction" means that the first slice layer starts from the smallest position along the main direction of the point cloud model. Start capturing The point cloud in this interval, Given the current slice thickness, after determining the current slice layer, the next slice layer is then extracted sequentially.

[0018] Furthermore, let the starting position of the current layer slice i be denoted as . The end position of the current layer slice i is denoted as cut

[0019] The center position of the point cloud is denoted as

[0020] Furthermore, set a minimum interval width. The minimum interval width is 1 / 4 of the initial slice thickness. Reduce slice thickness within the constraints.

[0021] Furthermore, after calculating the convex hull of the discrete point set on the corresponding cutting surface for each slice of point cloud data, the method for thinning the feature points is to set a threshold ε for angular feature judgment as the maximum allowable angle between two adjacent rays starting from the same endpoint. When the angle between the two rays is greater than or equal to the threshold ε, the point is retained as a feature point.

[0022] On the other hand, the present invention also provides a three-dimensional geographic point cloud adaptive slicing thinning system for implementing the three-dimensional geographic point cloud adaptive slicing thinning method described above.

[0023] Furthermore, it includes a processor and a memory, the memory being used to store program instructions, and the processor being used to call the stored instructions in the memory to execute a three-dimensional geographic point cloud adaptive tiling thinning method as described above.

[0024] Alternatively, it may include a readable storage medium storing a computer program that, when executed, implements a three-dimensional geographic point cloud adaptive slicing thinning method as described above.

[0025] Compared with the prior art, the present invention has the following advantages:

[0026] The slicing-based thinning method is simple, fast, preserves many features, and has high data accuracy. It has no requirements on the point cloud data, and the sampled points obtained by this method are uniformly distributed in Euclidean space. It eliminates the subjectivity and randomness of user-defined thresholds and can process 3D point clouds of any size. It is particularly suitable for the rapid processing of LiDAR point cloud data and subsequent 3D modeling tasks, improving production efficiency. Attached Figure Description

[0027] Figure 1 This is a flowchart of an embodiment of the present invention.

[0028] Figure 2 This is a schematic diagram illustrating the principle of the histogram statistical method for removing point cloud noise according to an embodiment of the present invention.

[0029] Figure 3 This is a schematic diagram illustrating the adaptive slicing principle of an embodiment of the present invention.

[0030] Figure 4 This is a schematic diagram of the radial projection width according to an embodiment of the present invention.

[0031] Figure 5 This is a schematic diagram illustrating the calculation of the convex hull using the roll-wrapping method according to an embodiment of the present invention.

[0032] Figure 6 This is a schematic diagram illustrating the principle of removing redundant points within the convex hull in an embodiment of the present invention.

[0033] Figure 7 This is an example diagram showing the thinning result of the method in an embodiment of the present invention. Detailed Implementation

[0034] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0035] This invention first performs adaptive slicing on disordered 3D discrete point clouds to extract the convex hull point set of the cross-section outer contour. Then, it uses the wrapping method to thin out each convex hull point set to retain feature points. Finally, it connects the convex hulls together in the order of slicing to complete the adaptive point cloud slicing and thinning.

[0036] like Figure 1 As shown in the figure, this invention provides an adaptive slicing thinning method for disordered 3D geographic discrete point clouds, the process of which includes the following steps:

[0037] Step 1: Obtain the effective range and density of the disordered 3D discrete points, and initialize the current slice layer label i=1.

[0038] This invention further provides a suggested implementation method for step 1:

[0039] Step 1.1: Calculate the maximum and minimum values ​​of the point cloud coordinates in the X, Y, and Z directions, and perform histogram calculation to determine the effective range of the point cloud, removing drifting points and isolated points.

[0040] See Figure 2 In this embodiment, the maximum and minimum coordinates of the point cloud in the X, Y, and Z directions are first counted. Histograms are then plotted for the number of points in each of the X, Y, and Z directions, with intervals of Δx, Δy, and Δz. Noise points at the maximum and minimum boundaries of the three histograms are then removed to obtain the effective range of the point cloud. The formulas for calculating Δx, Δy, and Δz are as follows:

[0041] Δx=(X max -X min ) / n

[0042] Δy=(Y max -Y min ) / n (1)

[0043] Δz=(Z max -Z min ) / n

[0044] In the formula, X max X min Y max Y min Z max Z min Let n represent the maximum and minimum values ​​in the X, Y, and Z directions, and let n be the dimension of the one-dimensional array in the histogram. In this embodiment, n is set to 1000.

[0045] Step 1.2, Extract point cloud density: In specific implementation, it is preferred to use the existing point cloud density calculation method to obtain the point cloud density, that is, to obtain the average density mean of the discrete point set as the initial slice thickness;

[0046] For example, octrees can be used to calculate the surface density Sd and volume density Vd by determining the optimal octree level corresponding to a given nearest neighbor radius. The specific formula is as follows:

[0047] Sd=N / (π×R 2 (2)

[0048]

[0049] In the formula, N represents the number of nearest neighbors, and R represents the radius of the spherical neighborhood of the nearest neighbor. It is worth noting that a spherical neighborhood with no neighbors will have ineffective density.

[0050] Step 2: Select the direction with the largest effective range in the X, Y, and Z directions as the main direction, and slice the disordered 3D discrete points into the current layer sequentially along the main direction, then proceed to Step 3. The average density obtained in Step 1.2 is used as the thickness of the first slice, and the thickness of subsequent slices is set according to the adjustment results from the previous iteration in Step 4.

[0051] The present invention further provides a suggested method for implementing step 2:

[0052] When performing step 2 for the first time, let the current slice layer i = 1, and let the first slice be located at the minimum position of the principal direction coordinates of the point cloud model, denoted as . For example Figure 3 The principal direction is z. At the point cloud model where the z-coordinate is minimum in the principal direction, the plane perpendicular to this direction is the XY plane. Slicing is performed parallel to the XY plane, with an initial slice thickness... The mean density obtained in step 1.2 That is, from the minimum position of the main direction of the point cloud model Start capturing Let the maximum position of the point cloud within this interval be denoted as . And calculate the minimum interval width for This serves as a constraint for subsequent adjustments to the slice thickness.

[0053] The principle of slicing is as follows Figure 3 As shown. In this embodiment, the thickness of the slice is first determined. This invention uses the average density of the discrete point set as the thickness of the first slice, denoted as... Minimum interval width is taken The first slice is located at the minimum position of the principal direction (e.g., the Z-axis) of the point cloud model, denoted as... Slice layer i = 1.

[0054] The initial position of the second slice is The subsequent execution of step 4 will adjust the current slice layer i = i + 1. The third slice will then be performed after the second slice determination, starting from the position of the second slice, with an initial position... The same principle applies to subsequent iterations of slicing; the end position of the slice can be determined based on the adjusted current slice thickness.

[0055] Therefore, the starting position of the current layer slice i can be denoted as: The end position of the current layer slice i can be denoted as

[0056] Step 3: Project the point cloud of the current slice layer i onto the plane where the center of the point cloud is located to obtain the two-dimensional cross-sectional point of the slice, that is, project the current three-dimensional point cloud of the slice onto the two-dimensional plane where the slice direction is located.

[0057] Projecting the point cloud of the slice to the center of the cross section, that is, calculating the centroid of all points on the i-th slice, refers to calculating the centroid of all points on the i-th slice with a certain thickness. When performing point cloud slicing, the plane position where the thickness center is located (in this example, it is parallel to the XY plane) is denoted as The plane at this location can also be represented by equation (2). Here, A, B, C, and D are known constants, and A, B, and C are not simultaneously zero. (A, B, C) can form a normal vector n of this plane. The coordinates of the projection point can be obtained from equation (3):

[0058] Ax + By + Cz + D = 0 (2)

[0059]

[0060] In the formula, the coordinates of the spatial point cloud p(x0,y0,z0) are known, and the plane P(A,B,C,D) at the center position of the given slice thickness is known, so the coordinates of the projection point p0(x,y,z) can be calculated.

[0061] Step 4: Calculate the difference between the inner and outer contours when the sliced ​​point cloud is projected onto the center of the section, and obtain the maximum value of the projection width w along the main direction. max As a parameter to control the slice thickness, adaptive slicing is performed.

[0062] In this embodiment, after projecting the current 3D slice point cloud onto the 2D plane containing the slicing direction in step 3, the radial width of the inner and outer contour points is calculated, and it is determined whether it is greater than the shape error threshold. This is to determine whether adaptive adjustment of the slice thickness is needed. If not, the next layer of slicing is performed directly on the disordered 3D discrete points along the main direction. Specifically, the difference between the inner and outer contours when the slice point cloud of the current slice layer i is projected onto the center of the cross section is calculated, i.e., the maximum value of the projection width w along the main direction (such as the Z-axis direction). max As a parameter to control slice thickness, adaptive slicing is performed. The projection width is illustrated below. Figure 4 As shown, after slicing and projecting the discrete point set, the calculation of the point cloud is transformed from three-dimensional to two-dimensional, which reduces the difficulty of the calculation.

[0063] This invention further provides a suggested implementation method for step 4:

[0064] Step 4.1, determine w max Is it greater than the given shape error threshold θ?

[0065] If w max If ≤θ, it can be determined that the current slice layer is complete. Let i = i + 1. Return to step 2 to process the next current slice layer i, until... When the maximum coordinate in the main direction is greater than or equal to the maximum coordinate, the point cloud adaptive slicing is completed, and the process proceeds to step 5. In specific implementation, the user can preset the value of the shape error threshold θ; in this embodiment, θ is set to 2 mm.

[0066] If w max If the value is greater than θ, proceed to step 4.2;

[0067] Step 4.2, at this point if Then reduce the current slice thickness, making the slice thickness... Return to step 2 and readjust the current slice layer i, i.e., adjust it according to the new slice thickness. Determine the maximum cut position Subsequent slicing of other layers will also be done according to the current slice thickness. Proceed. Until step 4.1 is re-entered to determine w. max ≤θ, or reach

[0068] If at this time This indicates that the current slice thickness is already at its minimum, so we can directly set i = i + 1. Return to step 2 to process the current slice layer i until... If the maximum coordinate in the main direction is greater than or equal to the maximum coordinate, then the point cloud adaptive tiling is completed, and proceed to step 5;

[0069] The significance of this step is that when w max >θ indicates that the point cloud model has obvious feature changes. Here, it is necessary to reduce the slice thickness and repeat the above steps to perform the projection width and shape error judgment process, so as to make all slice point clouds meet the threshold range as much as possible.

[0070] Step 5: Calculate the convex hull of the discrete point set on the slice plane of all slice point cloud data in sequence; and determine the feature points.

[0071] Step 5.1: First, initialize i=1, and use the convolute method to connect the convex hull points of the slice point cloud to obtain the boundary contour.

[0072] The problem involves calculating the convex hull of discrete point sets on the projection plane of all sliced ​​data along the main direction of the point cloud, which is essentially transforming the problem into finding the initial outer contour of the point cloud slices in a two-dimensional plane. For example... Figure 5As shown, a plane coordinate system for the section is established using two coordinate axes perpendicular to the main direction (such as the X-axis and Y-axis). Then, the convex hull of each section is obtained using the classic Jarris wrapping method. A ray is drawn from the point with the smallest y-coordinate in the discrete point set of the section. With this point as the center, the ray is rotated counterclockwise until it contacts a point in the point set. This point is set as the second point of the convex hull. Then, with this point as the starting point and the current ray direction as the reference, the ray is rotated counterclockwise to find the next point in the point set that passes through the ray. Finally, this process is repeated until the starting point is returned. This obtains the convex hull of the discrete point set in the slice point cloud.

[0073] Step 5.2: A threshold ε is given for angular feature judgment, and this value is used as the maximum allowable angle between two adjacent rays originating from the same endpoint; according to the angle d between the two rays... i Sampling points are added based on the principle that the threshold ε must be greater than or equal to d. i If the angle between the points is greater than or equal to ε, the point is retained as a feature point. The nearest neighbor query method is used to iterate through the next point, checking if the angle between the rays connecting the endpoints is within a set threshold. If it is less than the threshold, the point is not added to the point set, i.e., d... i If the value is less than ε, remove the point as a redundant point. This yields the inflection point with the greatest change.

[0074] The specific implementation of step 5.2 is as follows: Figure 6 As shown, the appropriate angle threshold ε should be selected during implementation, depending on the specific application scenario and data characteristics. Starting from the starting point, take three adjacent points: p1, p2, p3, and calculate... and If the angle α between the two rays is greater than or equal to ε, then p2 is retained as a feature point. From p3, we find the nearest point p4, and set p1 = p2, p2 = p3, p3 = p4, and recalculate. and The angle between the points; if α < ε, then delete p2, query the nearest point p4 from p3, let p2 = p3, p3 = p4, and repeat the calculation. and The angle between the two points; until p4 is no longer found, the feature points on the scan line have been processed.

[0075] Step 5.3: Let i = i + 1, and repeat steps 5.1 and 5.2 until the entire set of sampling points is generated.

[0076] After generating all convex hulls and scan lines, iterate through steps 5.1 and 5.2 to complete the point cloud thinning for each slice.

[0077] Step 6: Sort all the convex hulls of the cut surfaces according to the main direction, connect each slice, and obtain the overall thinned point cloud model and output it.

[0078] See Figure 7 This is the compression effect of geographic point clouds with a shape error of 2mm. It retains feature points containing large curvature information, reduces point redundancy in flat areas, and achieves a relatively balanced overall point cloud distribution. It achieves good thinning effect and high thinning efficiency.

[0079] In specific implementation, the method proposed in the technical solution of this invention can be automatically executed by those skilled in the art using computer software technology. System devices for implementing the method, such as computer-readable storage media storing the corresponding computer program of the technical solution of this invention and computer equipment including the computer program running the corresponding computer program, should also be within the protection scope of this invention.

[0080] In some possible embodiments, a three-dimensional geographic point cloud adaptive tiling thinning system is provided, including a processor and a memory. The memory is used to store program instructions, and the processor is used to call the stored instructions in the memory to execute a three-dimensional geographic point cloud adaptive tiling thinning method as described above.

[0081] In some possible embodiments, a three-dimensional geographic point cloud adaptive tiling thinning system is provided, including a readable storage medium on which a computer program is stored. When the computer program is executed, it implements a three-dimensional geographic point cloud adaptive tiling thinning method as described above.

[0082] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A method for adaptive slicing and thinning of three-dimensional geographic point clouds, characterized in that, Includes the following steps: Step 1, calculate the effective range and density of the 3D discrete point cloud, including the following sub-steps, Step 1.1: Calculate the maximum and minimum values ​​of the point cloud coordinates in the X, Y, and Z directions, and perform histogram calculation to determine the effective range of the point cloud, removing drifting points and isolated points. Step 1.2: Extract the point cloud density as the initial slice thickness; Step 2: Select the direction with the largest effective range in the X, Y, and Z directions as the main direction, and then slice the disordered 3D discrete points in the current layer along the main direction in sequence to obtain the sliced ​​point cloud, and then proceed to step 3. Step 3: Project the slice point cloud obtained in Step 2 of this iteration onto the plane where the center of the slice point cloud is located to obtain the two-dimensional cross-sectional points of the slice; Step 4: Calculate the difference between the inner and outer contours when the sliced ​​point cloud is projected onto the center of the section, and obtain the maximum value of the projection width w along the main direction. max Determine w max If the error is greater than the corresponding preset shape error threshold θ, then the current slice layer is considered complete. Return to step 2 to process the next slice layer until the point cloud adaptive slicing within the effective range is completed, and proceed to step 5. If the error is found, reduce the slice thickness and return to step 2 to re-slice the current slice layer. Step 5: Calculate the convex hull of the discrete point set on the corresponding slice plane for all slice point cloud data in sequence, and determine the feature points to be thinned. This includes setting thresholds for angular feature judgment. As the maximum allowable angle between two adjacent rays originating from the same endpoint, when the angle between the two rays is greater than or equal to the threshold... This point is retained as a feature point. Step 6: Sort all the convex hulls of the cut surfaces according to the main direction, connect each slice, and obtain the overall thinned point cloud model.

2. The adaptive slicing and thinning method for three-dimensional geographic point clouds as described in claim 1, characterized in that: In step 1.1, the point cloud is first counted. X , Y , Z The coordinates of the maximum and minimum values ​​in the three directions, for X , Y , Z The number of points in each of the three directions was counted using histograms. , , The interval is set, and then the noise points at the two largest and smallest boundaries in the three histograms are removed to obtain the effective range of the point cloud; , , The calculation formula is as follows: In the formula, , , , , , for X , Y , Z Maximum and minimum values ​​in the direction, n This represents the dimension of the one-dimensional array in the histogram.

3. The adaptive slicing and thinning method for three-dimensional geographic point clouds as described in claim 1, characterized in that: The phrase "slicing the unordered 3D discrete points sequentially along the main direction into the current layer" refers to the first slice layer starting from the smallest position d along the main direction of the point cloud model.

1. Start capturing up to d 1+ d The point cloud in this interval, d Given the current slice thickness, after determining the current slice layer, the next slice layer is then extracted sequentially.

4. The adaptive slicing and thinning method for three-dimensional geographic point clouds as described in claim 1, characterized in that: Let the starting position of the current layer slice i be denoted as . The end position of the current layer slice i is denoted as The center position of the slice point cloud is denoted as .

5. The adaptive slicing and thinning method for three-dimensional geographic point clouds as described in claim 1, characterized in that: Set minimum interval width d .min The minimum interval width is 1 / 4 of the initial slice thickness. d .min To reduce slice thickness within constraints, the specific implementation steps are as follows: Step 4.1, determine w max Is it greater than the given shape error threshold θ? If w max θ determines that the current slice layer is complete. Let i = i + 1, d i = d i-1 + d Return to step 2 to process the next current slice layer i, until d. i If the maximum coordinate in the main direction is greater than or equal to the maximum coordinate, then the point cloud adaptive tiling is completed, and proceed to step 5; If w max If the value is greater than θ, proceed to step 4.2; Step 4.2, at this point if d > d .min Then reduce the current slice thickness, making the slice thickness... d = d - d .min Return to step 2 and readjust the current slice layer i according to the new slice thickness. d Determine the maximum cut position d i+1 = d i + d Subsequent slicing of other layers will also be done according to the current slice thickness. d Continue until step 4.1 is re-entered to determine w. max θ, or reach d d .min ; If at this time d d .min This indicates that the current slice thickness is already at its minimum, so we can directly set i = i + 1, d i = d i-1 + d Return to step 2 to process the current slice layer i until d. i If the maximum coordinate in the main direction is greater than or equal to the maximum coordinate, then the point cloud adaptive tiling is completed, and proceed to step 5.

6. A three-dimensional geographic point cloud adaptive slicing thinning method as described in claim 1, 2, 3, 4, or 5, characterized in that: After calculating the convex hull of the discrete point set on the corresponding slice plane for each slice of point cloud data, the method for thinning feature points is as follows: The specific implementation method adopts the following steps. Step 5: Calculate the convex hull of the discrete point set on the slice plane for all slice point cloud data in sequence; and determine the feature points. Step 5.1: First, initialize i=1, and use the convolution method to connect the convex hull points of the slice point cloud to obtain the boundary contour; This includes finding the convex hull of discrete point sets on the projection plane of all slice data according to the order of slicing the point cloud along the main direction, and transforming it into the problem of finding the initial outer contour of the point cloud slice in a two-dimensional plane. Step 5.2: Provide the threshold for angle feature judgment. This value is taken as the maximum allowable angle between two adjacent rays originating from the same endpoint; according to the angle between the two rays It must be greater than or equal to the threshold. The sampling point is added according to the principle of adding sampling points. The nearest point query method is used to traverse the next point. The angle between the rays connecting the endpoints is checked to see if it is within the set threshold range. If it is less than the set threshold, it is not added to the point set and the point is deleted as a redundant point. This yields the inflection point with the greatest change. Step 5.3: Let i = i + 1, and repeat steps 5.1 and 5.2 until the entire set of sampling points is generated; After generating all convex hulls and scan lines, iterate through steps 5.1 and 5.2 to complete the point cloud thinning for each slice.

7. A three-dimensional geographic point cloud adaptive slicing thinning system, characterized in that: This method is used to implement an adaptive slicing and thinning method for three-dimensional geographic point clouds as described in any one of claims 1-6.

8. The three-dimensional geographic point cloud adaptive slicing thinning system according to claim 7, characterized in that: It includes a processor and a memory, the memory being used to store program instructions, and the processor being used to call the stored instructions in the memory to execute a three-dimensional geographic point cloud adaptive tiling thinning method as described in any one of claims 1-6.

9. The three-dimensional geographic point cloud adaptive slicing thinning system according to claim 7, characterized in that: It includes a readable storage medium on which a computer program is stored, and when the computer program is executed, it implements a three-dimensional geographic point cloud adaptive slicing thinning method as described in any one of claims 1-6.

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