CN112598799B - Probability theory-based two-dimensional point cloud outsourcing contour processing method, device and medium - Google Patents
Probability theory-based two-dimensional point cloud outsourcing contour processing method, device and medium Download PDFInfo
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- 238000012946 outsourcing Methods 0.000 title claims abstract description 17
- 238000003672 processing method Methods 0.000 title claims abstract description 11
- 238000000034 method Methods 0.000 claims abstract description 29
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- 238000004422 calculation algorithm Methods 0.000 claims abstract description 6
- 238000004590 computer program Methods 0.000 claims description 9
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- 238000000605 extraction Methods 0.000 description 3
- 238000005096 rolling process Methods 0.000 description 3
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- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
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Abstract
The invention relates to a two-dimensional point cloud outsourcing contour processing method and device based on probability theory and a technical scheme of a medium, wherein the method comprises the following steps: s100, processing point cloud data by using a Bowyer-Watson algorithm to generate a Delaunay triangulation network; s200, calculating all side lengths of the Delaunay triangulation network, performing descending order arrangement, and calculating a length threshold value through a probability theory; and S300, circularly deleting the degradable edges by adopting an iteration method to obtain the polygon of the point cloud outsourcing contour. The beneficial effects of the invention are as follows: the probability theory method is used for calculating the core parameters, the prior knowledge or the manual intervention is not relied on, and the actual processing effect is good; the calculation result has good idempotency.
Description
Technical Field
The invention relates to the field of computer graphics and space geographic information, in particular to a two-dimensional point cloud outsourcing contour processing method, device and medium based on probability theory.
Background
The method is applied to computer graphic processing and spatial geographic information contour line generation. In the processes of computer graphics processing and spatial data analysis, it is often necessary to extract the external contours of the study object by means of discrete point sets, thereby achieving geometric modeling and effective range definition. For example, in the mapping industry, in order to obtain a topography map of a complex environment, sampling measurement is generally performed manually or by using an unmanned aerial vehicle radar, sampling position points are discrete points, and if the whole outline of a discrete point cloud is a curved strip shape, certain difficulty exists in determining the boundary of a measurement area. The traditional method adopts Delaunay triangulation and other methods to process, and the extracted external contour has deviation.
In the prior art, in the process of extracting the external contour of the two-dimensional point cloud and processing geospatial data, the curved and banded point cloud often appears, the external contour is a concave polygon, and the result obtained by directly using the Delaunay triangulation processing method is a convex polygon, so that the actual requirement is not met; the current main stream concave polygon contour extraction method comprises a rolling method, a rolling ball method, a box boundary searching method, a row-column method, a Delaunay triangle net degradation method and the like. The current concave polygon contour extraction method involves a core length parameter, such as the side length of the binding method, the radius of the rolling ball method and the like, which determines the convex-concave degree of the generated polygon, and in the actual production process, the parameters depend on priori knowledge, need manual judgment and intervention, and lack scientific and definite standards; secondly, the existing concave polygon extraction method does not have idempotent property, the calculation result has certain randomness, and for the same input, the result calculated by the same algorithm for multiple times is possibly different, so that the method is not suitable for certain strong consistency fields.
Disclosure of Invention
The invention aims to at least solve one of the technical problems in the prior art, and provides a two-dimensional point cloud outsourcing contour processing method, a device and a medium based on probability theory, which do not depend on priori knowledge or manual intervention, have good actual processing effect and have good idempotency of calculation results.
The technical scheme of the invention comprises a two-dimensional point cloud outsourcing contour processing method based on probability theory, which is characterized by comprising the following steps: s100, processing point cloud data by using a Bowyer-Watson algorithm to generate a Delaunay triangulation network; s200, calculating all side lengths of the Delaunay triangulation network according to the length, arranging the side lengths in a descending order, and calculating a length threshold value through a probability theory; and S300, circularly deleting the degradable edges by adopting an iteration method to obtain the polygon of the point cloud outsourcing contour.
According to the probability theory-based two-dimensional point cloud outsourcing contour processing method, S200 includes: s210, calculating all side lengths in the Delaunay triangle network, and adding the side lengths into a side length list; s220, sorting elements in the side length list in a descending order according to the length and the size; s230, taking the nth element value of the side length list as a length threshold, where N should be the largest positive integer not greater than N, n=0.05m, and m is the number of elements of the side length list.
According to the probability theory-based two-dimensional point cloud outsourcing contour processing method, S300 comprises: s310, traversing a Delaunay triangle network, and establishing topological relations of triangles, edges and points; s320, counting the sharing relation of the edges, adding the unshared edges into an unshared edge list, and adding the vertexes of the unshared edges into an unshared edge vertex list; s330, traversing the unshared edge list, if the corresponding vertex of the edge is not in the unshared edge vertex, the edge belongs to the degradable edge, and adding the degradable edge into the degradable edge list; s340, if the degradable edge list is empty, executing S360; if the degradable edge list is not empty, acquiring the longest edge in the degradable edge list; s350, if the length of the longest side is smaller than the length threshold value, S360 is executed; otherwise, removing the longest edge from the unshared edges, adding the other 2 edges of the triangle to which the longest edge belongs to the unshared edge list, adding the corresponding vertex of the longest edge to the unshared edge vertex list, emptying the degradable edge list, and returning to S330; s360, connecting the unshared edge list as an external contour edge to obtain the concave polygon.
According to the probability theory-based two-dimensional point cloud outsourcing contour processing method, the method further comprises initializing a Delaunay triangle network.
The technical scheme of the invention also comprises a two-dimensional point cloud outsourcing contour processing device based on probability theory, and the device comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and is characterized in that any one of the method steps is realized when the processor executes the computer program.
The technical solution of the present invention further comprises a computer-readable storage medium storing a computer program, characterized in that the computer program realizes any of the method steps when being executed by a processor.
The beneficial effects of the invention are as follows: the probability theory method is used for calculating the core parameters, the prior knowledge or the manual intervention is not relied on, and the actual processing effect is good; the calculation result has good idempotency.
Drawings
The invention is further described below with reference to the drawings and examples;
FIG. 1 is a schematic diagram of a triangular mesh;
FIG. 2 is a general flow chart according to an embodiment of the present invention;
FIG. 3 is a flow chart of length threshold calculation according to an embodiment of the present invention;
FIG. 4 is a flow chart of iterative deletion of a degradable edge in accordance with an embodiment of the present invention;
fig. 5 shows a schematic view of an apparatus according to an embodiment of the invention.
Detailed Description
Reference will now be made in detail to the present embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein the accompanying drawings are used to supplement the description of the written description so that one can intuitively and intuitively understand each technical feature and overall technical scheme of the present invention, but not to limit the scope of the present invention.
In the description of the present invention, a number means one or more, a number means two or more, and greater than, less than, exceeding, etc. are understood to not include the present number, and above, below, within, etc. are understood to include the present number.
In the description of the present invention, the continuous reference numerals of the method steps are used for facilitating examination and understanding, and by combining the overall technical scheme of the present invention and the logic relationships between the steps, the implementation sequence between the steps is adjusted without affecting the technical effect achieved by the technical scheme of the present invention.
In the description of the present invention, unless explicitly defined otherwise, terms such as arrangement and the like should be construed broadly, and those skilled in the art can reasonably determine the specific meaning of the terms in the present invention in combination with the specific contents of the technical scheme.
Term interpretation:
delaunay triangulation: a set of connected but non-overlapping triangles, and the circles circumscribed by the triangles do not contain any other points of this area;
sharing edges: in Delaunay triangulation, both sides of two triangles are considered;
not sharing edges/boundary edges: in the Delaunay triangle net, only one triangle edge is included, namely the boundary edge of the outermost periphery of the triangle net;
corresponding vertices: the vertexes of the triangles are A, B, C respectively, and the vertex C is the corresponding vertex of the side AB;
degradable edges: the corresponding vertex is not on an unshared edge of the boundary.
Idempotent: the same conditions are used for the same system, and the processing results obtained by one request and repeated requests are consistent.
Fig. 1 is a schematic diagram of a triangle mesh, wherein the dashed lines are shared edges, the solid lines are non-shared edges/boundary edges, AC is a degradable edge, CD is a non-degradable edge, and point B is the corresponding vertex of the AC edge.
Fig. 2 is an overall flow chart according to an embodiment of the invention, the flow comprising: s100, processing point cloud data by using a Bowyer-Watson algorithm to generate a Delaunay triangulation network; s200, calculating all side lengths of the Delaunay triangulation network, performing descending order arrangement, and calculating a length threshold value through a probability theory; and S300, circularly deleting the degradable edges by adopting an iteration method to obtain the polygon of the point cloud outsourcing contour.
For the description of fig. 2 and 3, wherein,
lengths: side length;
len: a length threshold;
borderedge: not sharing edges;
borderPoints: vertices of the edges are not shared;
delete: degradable edges;
maxEdge: the longest side.
FIG. 3 is a length threshold calculation flow chart according to an embodiment of the present invention, comprising:
s210: calculating all side lengths in the Delaunay triangle network, and adding the side lengths into a length hs list;
s220: sorting elements in the length hs list in descending order according to the length;
s230: the number of elements in the Lengths list is m, n=m0.05, N is the largest positive integer not more than N, and the value of the Nth element in the Lengths is taken and recorded as len.
FIG. 4 is a flow chart of iterative deletion of a degradable edge according to an embodiment of the invention, comprising:
s310: traversing the Delaunay triangle network, and establishing topological relations among triangles, edges and points;
s320: counting the sharing relation of the edges, adding the unshared edges into a borderedge list, and adding the vertexes of the unshared edges into the borderPoints list;
s330: traversing the borderedge list, if the corresponding vertex of the edge is not in the borderPoints, the edge belongs to a degradable edge, and adding the degradable edge into the delete list;
s340: if the delete list is empty, go to S360; if the delete list is not empty, acquiring the longest edge maxEdge;
s350: if the length of the maxEdge is smaller than len in step S230, the process proceeds to step S360; otherwise, removing the maxEdge from the borderEdges, adding the other 2 sides of the triangle to which the maxEdge belongs to the borderEdge list, adding the corresponding vertex of the maxEdge to the borderPoints list, emptying the delets list, and entering S330;
s360: the borderedge list is an external contour edge, and the concave polygon can be obtained by connecting the external contour edges.
Fig. 5 shows a schematic view of an apparatus according to an embodiment of the invention. The apparatus comprises a memory 100 and a processor 200, wherein the processor 200 stores a computer program for executing: processing the point cloud data by using a Bowyer-Watson algorithm to generate a Delaunay triangulation network; calculating all side lengths of the Delaunay triangular network, performing descending order arrangement, and calculating a length threshold value through a probability theory; and circularly deleting the degradable edges by adopting an iteration method to obtain the polygon of the point cloud outsourcing contour. Wherein the memory 100 is used for storing data.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of one of ordinary skill in the art without departing from the spirit of the present invention.
Claims (3)
1. A two-dimensional point cloud outsourcing contour processing method based on probability theory is characterized by comprising the following steps:
s100, processing point cloud data by using a Bowyer-Watson algorithm to generate a Delaunay triangulation network;
s200, calculating all side lengths of the Delaunay triangulation network, performing descending order arrangement, and calculating a length threshold value through a probability theory;
s300, circularly deleting degradable edges by adopting an iteration method to obtain polygons of the point cloud outsourcing contour;
the S200 includes:
s210, calculating all side lengths in the Delaunay triangle network, and adding the side lengths into a side length list;
s220, sorting elements in the side length list in a descending order according to the length and the size;
s230, taking an N element value of the side length list as a length threshold, wherein N is the largest positive integer not greater than N, n=0.05m, and m is the element number of the side length list;
the S300 includes:
s310, traversing a Delaunay triangle network, and establishing topological relations of triangles, edges and points;
s320, counting the sharing relation of the edges, adding the unshared edges into an unshared edge list, and adding the vertexes of the unshared edges into an unshared edge vertex list;
s330, traversing the unshared edge list, if the corresponding vertex of the edge is not in the unshared edge vertex, the edge belongs to the degradable edge, and adding the degradable edge into the degradable edge list;
s340, if the degradable edge list is empty, executing S360; if the degradable edge list is not empty, acquiring the longest edge in the degradable edge list;
s350, if the length of the longest side is smaller than the length threshold value, S360 is executed; otherwise, removing the longest edge from the unshared edges, adding the other 2 edges of the triangle to which the longest edge belongs to the unshared edge list, adding the corresponding vertex of the longest edge to the unshared edge vertex list, emptying the degradable edge list, and returning to S330;
s360, connecting the unshared edge list as an external contour edge to obtain the concave polygon.
2. A probability theory-based two-dimensional point cloud outsourcing contour processing device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method steps of claim 1 when executing the computer program.
3. A computer-readable storage medium storing a computer program, characterized in that the computer program realizes the method steps of claim 1 when being executed by a processor.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105321168A (en) * | 2015-04-29 | 2016-02-10 | 贵州省水利水电勘测设计研究院 | Method for automatically mapping field ridge of mountain area in three-dimensional laser point clouds |
| WO2016109768A1 (en) * | 2014-12-31 | 2016-07-07 | Robert Bosch Gmbh | System and method of shadow effect generation for concave objects with dynamic lighting in three-dimensional graphics |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016109768A1 (en) * | 2014-12-31 | 2016-07-07 | Robert Bosch Gmbh | System and method of shadow effect generation for concave objects with dynamic lighting in three-dimensional graphics |
| CN105321168A (en) * | 2015-04-29 | 2016-02-10 | 贵州省水利水电勘测设计研究院 | Method for automatically mapping field ridge of mountain area in three-dimensional laser point clouds |
Non-Patent Citations (2)
| Title |
|---|
| 一种基于Delaunay三角网边长阈值与激光点云的建筑立面结构提取方法;靳鑫洋等;《地理与地理信息科学》;第35卷(第5期);第80-84页 * |
| 基于约束Delaunay三角网的线、面群目标分布边界计算;禄小敏;闫浩文;王中辉;甘治国;;测绘工程(05);第37-41页 * |
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Inventor after: Zhang Zhiao Inventor after: Zhao Zili Inventor after: Zhang Xiupeng Inventor after: Liu Jidong Inventor after: Gong Dailong Inventor after: Xu Mingsheng Inventor after: Ma Yan Inventor after: Wang Yajun Inventor after: Zhang Haobin Inventor before: Zhao Zili Inventor before: Gong Dailong Inventor before: Zhang Zhiao Inventor before: Zhang Xiupeng Inventor before: Liu Jidong Inventor before: Xu Yafeng Inventor before: Xu Mingsheng Inventor before: Cheng Zhiping Inventor before: Wang Yajun Inventor before: Zhang Haobin |
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Correction item: Inventor Correct: Zhang Zhiao|Zhao Zili|Zhang Xiupeng|Liu Jidong|Gong Yilong|Xu Mingsheng|Ma Yan|Wang Yajun|Zhang Haobin False: Zhang Zhiao|Zhao Zili|Zhang Xiupeng|Liu Jidong|Gong Yilong|Xu Mingsheng|Equinitis|Wang Yajun|Zhang Haobin Number: 04-01 Volume: 40 |
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Correction item: Inventor Correct: Zhang Zhiao|Zhao Zili|Zhang Xiupeng|Liu Jidong|Gong Yilong|Xu Mingsheng|Ma Yan|Wang Yajun|Zhang Haobin False: Zhang Zhiao|Zhao Zili|Zhang Xiupeng|Liu Jidong|Gong Yilong|Xu Mingsheng|Equinitis|Wang Yajun|Zhang Haobin Number: 08-02 Page: The title page Volume: 40 Correction item: Inventor Correct: Zhang Zhiao|Zhao Zili|Zhang Xiupeng|Liu Jidong|Gong Yilong|Xu Mingsheng|Ma Yan|Wang Yajun|Zhang Haobin False: Zhang Zhiao|Zhao Zili|Zhang Xiupeng|Liu Jidong|Gong Yilong|Xu Mingsheng|Equinitis|Wang Yajun|Zhang Haobin Number: 08-02 Volume: 40 |
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