CN117292081A - Storage yard volume calculation method based on three-dimensional reconstruction and terminal equipment - Google Patents
Storage yard volume calculation method based on three-dimensional reconstruction and terminal equipment Download PDFInfo
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Abstract
The invention provides a storage yard volume calculation method and terminal equipment based on three-dimensional reconstruction, wherein the method comprises the following steps: collecting yard point cloud data by using a plurality of laser radars arranged in a yard; performing external parameter calibration on the laser radar, and determining external parameters of the laser radar; setting the laser radars in the same IP network, and unifying the point cloud data acquired by different laser radars to the same coordinate system according to the external parameters of the laser radars, so that the point cloud data acquired by a plurality of laser radars are spliced together to obtain a yard laser point cloud; performing patch segmentation on the laser point cloud of the storage yard, and extracting a storage model; and constructing a Delaunay triangle network, and calculating the storage yard volume. According to the invention, an instrument is not required to be operated by personnel, a storage yard model can be monitored and updated in real time, automatic and normalized point cloud data acquisition is realized, the method is particularly suitable for automatic volume measurement and calculation of a large storage yard, and the efficiency and the accuracy of three-dimensional reconstruction and volume calculation of the storage yard can be greatly improved.
Description
Technical Field
The invention belongs to the technical field of bulk volume measurement, and particularly relates to a bulk volume calculation method based on three-dimensional reconstruction and terminal equipment.
Background
In recent years, the country is continuously pushing intelligent mine construction, wherein mine storage yard warehouse management is a key ring, and the method for grasping the storage yard situation and acquiring the storage yard volume in real time has very important significance for equipment personnel management and warehouse scheduling.
The traditional yard volume measurement method mainly adopts manual handheld total stations, RTKs and other instruments to extract characteristic points for manual monitoring, but the method has the advantages of low operation efficiency, high labor cost, large measurement blind area, incapability of realizing complete modeling for large material piles, slow measurement speed of manually operated instruments and difficulty in realizing real-time increment update of the yard volume.
The invention patent application of China with the application number of CN115115778A provides a large storage yard volume inventory method based on three-dimensional reconstruction and point cloud segmentation, which uses an unmanned plane to collect a overlook image of a target storage yard, utilizes a three-dimensional reconstruction algorithm to generate a point cloud P in a three-dimensional space from the overlook image, is used for representing a real storage yard scene, adopts the point cloud segmentation based on normal vector to divide material piles in the point cloud P, calculates the volume of each material pile, and accordingly performs volume inventory of large storage yard materials. However, the method needs to use the unmanned aerial vehicle to perform omnibearing scanning on the stockpiles, collect overlook images of the target yard, has higher requirements on the flying hands, needs the professional flying hands to operate the unmanned aerial vehicle, has high labor cost, has low collection speed, cannot monitor and update the yard model in real time, and is difficult to realize yard normalized modeling; meanwhile, the method requires calculating each point P in the point cloud P i The normal vector information of (2) greatly increases the computational complexity and the time overhead, has lower efficiency, and the method mainly depends on the point P i Dividing material points and ground points according to normal vector information of (a) and (b) forThe outliers are sensitive, the outliers in the yard point cloud data cannot be effectively processed, the anti-interference performance is weak, and the robustness is weak; in addition, in the point cloud scanning process, the method needs to perform omnibearing scanning, acquires overlook images of the target storage yard, and has large data volume and lower modeling efficiency.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a storage yard volume calculation method and terminal equipment based on three-dimensional reconstruction, which can monitor and update a storage yard model in real time, realize automatic and normalized point cloud data acquisition, are suitable for automatic volume measurement and calculation of a large storage yard, and can greatly improve the efficiency and the accuracy of three-dimensional reconstruction and volume calculation of the storage yard.
In order to solve the technical problems, the invention adopts the following technical scheme:
the storage yard volume calculation method based on three-dimensional reconstruction is characterized by comprising the following steps of:
s1, collecting yard point cloud data by using a plurality of laser radars arranged in a yard;
s2, performing external parameter calibration on the laser radar, and determining external parameters of the laser radar; the external parameters comprise pitch angle pitch, roll angle roll and yaw angle yaw of the laser radar;
s3, arranging the laser radars in the same IP network, and unifying point cloud data acquired by different laser radars to the same coordinate system according to external parameters of the laser radars, so that the point cloud data acquired by a plurality of laser radars are spliced together to obtain a yard laser point cloud; the same coordinate system is used for selecting one laser radar as a standard, and the point cloud data of all the laser radars are unified under the coordinate system of the laser radar;
s4, dividing the laser point cloud of the storage yard to obtain a laser point cloud set of a material pile, the ground and a dome, and extracting a storage model;
s5, constructing a Delaunay triangle network according to the stacking model;
s6, taking each triangle in the Delaunay triangle network as a bottom surface, taking the average value of the three-point-to-plane distances corresponding to the vertexes of the triangles as a height, and calculating the storage yard volume as the sum of the volumes of all the triangular prisms.
Preferably, in step S2, a TFAC automatic external parameter calibration algorithm is used to perform external parameter calibration on the laser radar.
Further, in the step S3, voxel division is performed on the storage yard laser point cloud, and a voxel centroid P is adopted centroid (x centroid ,y centroid ,z centroid ) Instead of all points within a voxel, wherein the voxel centroid P centroid (x centroid ,y centroid ,z centroid ) Is that
(x i ，y i ，z i ) The coordinate of the ith point in the voxel is given, and m is the number of all points in the voxel.
The traditional bulk inventory method of the stock yard materials directly aims at all points P in the stock yard point cloud P i The calculation is carried out, the calculated amount of the method is too large, and the data operation efficiency is low; the invention carries out down sampling on the points in the voxels, replaces all the points in the voxels with the voxel mass centers, reserves the original information as much as possible, reduces the calculated amount while ensuring the accurate sampling result, has better representation effect on the original data, and improves the measurement efficiency of the three-dimensional reconstruction and the volume calculation of the subsequent storage yard.
Preferably, in the step S4, a RANSAC plane segmentation algorithm is used to segment the yard laser point cloud.
Compared to computing each point P in the point cloud P i Normal vector information of (2) for each point P i The RANSAC algorithm is an outlier detection method, is little influenced by outliers (discrete points), can effectively process outliers in the storage yard point cloud data, and has stronger robustness; and the RANSAC algorithm does not need to calculate the normal vector information of each point in the point cloud in advance, and can directly use the geometric information of the point cloud to carry out simulation fitting and segmentation.
Further, when the steps areWhen the plurality of lidars in S1 are disposed at one side of the yard, the method further comprises, between steps S4 to S5: obtaining a plane corresponding to the maximum tangent plane of the stacking body as ax+by+cz+d=0, and calculating a three-dimensional space point cloud (x 0 ,y 0 ,z 0 ) Regarding symmetry points (x ', y ', z ') of the plane ax+by+cz+d=0, simulating one side of the stockpile, which is shielded, through point cloud mirroring, and reconstructing a complete stockpile model;
wherein A, B, C, D are plane coefficients,
aiming at the characteristic that the stockpile presents symmetry caused by a vertical blanking mode of a storage yard, a laser radar is adopted to scan one side of the stockpile, a point cloud mirror image method is used for simulating three-dimensional reconstruction of the side stockpile, the target storage yard is not required to be scanned in all directions, point cloud data of each scanning angle are output, and then point cloud data fusion is carried out on the point cloud data of each scanning angle, so that the calculation amount and time cost are greatly reduced, and the modeling efficiency is improved.
Preferably, in the step S5, the specific implementation process of constructing the Delaunay triangulation network includes:
s51, defining a super triangle containing all calculation areas according to the point cloud coordinate distribution, taking out a triangle ABC from a triangle set, wherein the vertex of the triangle ABC is A (x 1, y 1), B (x 2, y 2), C (x 3, y 3), and calculating the circumscribed circle of the triangle ABC as follows:
wherein the triangle set is a triangle formed by the point cloud point set and meeting Delaunay condition, (x) 0 ，y 0 ) The coordinates of the circle center of the circumscribed circle of the triangle ABC are the radius of the circumscribed circle of the triangle ABC;
s52, inserting a point P into the triangle ABC, acquiring and deleting all triangles M in the triangle set to form a cavity, and connecting the point P with the cavity nodes to form a new triangle grid, wherein the circumscribed circle of the triangle M comprises the point P;
and S53, updating the data structure, filling the deleted triangle data with the newly generated triangle mesh, returning to the step S51, and repeatedly executing the steps S51 to S53 until all points are interpolated to construct the Delaunay triangle mesh.
Further, between the step S5 and the step S6, the method further includes: and (3) repairing the point cloud holes of the Delaunay triangular net to recover the surface shape of the model.
In the technical field of bulk measurement, for a three-dimensional reconstructed storage yard model, due to the defects of complex shape of an object or a measurement mode, holes often exist in three-dimensional point cloud data, but the holes are not processed by a traditional storage yard material volume checking method, so that the quality of a curved surface after model reconstruction is seriously affected; according to the invention, the point cloud hole repair is carried out on the constructed Delaunay triangle network, so that the surface shape of the storage yard model is recovered, and the quality and the precision of the subsequent storage yard three-dimensional reconstruction model can be greatly improved.
As an inventive concept, the present invention also provides a terminal device including:
one or more processors;
and a memory having one or more programs stored thereon, which when executed by the one or more processors cause the one or more processors to implement the steps of the above-described method of the present invention.
A computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the above-described method of the invention.
Compared with the prior art, the invention has the following beneficial effects:
(1) According to the invention, by means of the fixed laser radar calibration and the storage yard three-dimensional reconstruction method, the storage yard model can be monitored and updated in real time, automatic and normalized point cloud data acquisition is realized, the problems that an unmanned aerial vehicle inspection method has high requirements on personnel operation, is low in acquisition speed and is difficult to update the reconstruction model in real time are solved, meanwhile, the defects that the volume calculation is performed mainly by manually operating an instrument to extract characteristic points in the mining field of China, the measurement efficiency is low, the coverage area is small and the labor cost is high are overcome, and the method is suitable for various large, medium and small storage yards, and is particularly suitable for automatic volume measurement and calculation of the large storage yards.
(2) Aiming at the characteristic that the stockpile presents symmetry caused by a vertical blanking mode of a storage yard, a laser radar is adopted to scan one side of the stockpile, a point cloud mirror image method is used for simulating three-dimensional reconstruction of the side stockpile, the target storage yard is not required to be scanned in all directions, point cloud data of each scanning angle are output, and then point cloud data fusion is carried out on the point cloud data of each scanning angle, so that the calculation amount and time cost are greatly reduced, and the modeling efficiency is improved.
(3) The method has the advantages that point cloud hole repair is carried out based on the Delaunay triangle network, the surface shape of the storage yard model is recovered, the storage yard volume calculation is carried out, the efficiency and the precision of the three-dimensional reconstruction and the volume calculation of the storage yard are greatly improved, and the defects of low efficiency and weak robustness of the existing storage yard volume measurement mode are overcome.
Drawings
FIG. 1 is a flow chart of a method for calculating a storage yard volume based on three-dimensional reconstruction according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a radar point cloud data splicing result according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a point cloud voxelized downsampling result according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a point cloud segmentation result according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of the result of pile extraction according to an embodiment of the present invention;
FIG. 6 is a graphical illustration of the mirroring results of a stockpile according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of the result of constructing Delaunay triangulation network according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a point cloud hole repair result according to an embodiment of the present invention;
FIG. 9 is a diagram showing the results of the volumetric calculation according to the embodiment of the present invention.
Detailed Description
Example 1
As shown in fig. 1, embodiment 1 of the present invention provides a method for calculating a storage yard volume based on three-dimensional reconstruction, which includes the following steps:
step S1, collecting yard point cloud data by using a plurality of laser radars arranged in a yard.
According to the invention, a plurality of laser radars can be arranged on two sides of a storage yard, and also can be arranged on the same side of the storage yard, and then the laser radars are remotely controlled to scan point cloud data of the storage yard. If a plurality of laser radars are arranged on two sides of the storage yard, point cloud data of the whole storage yard can be acquired; if a plurality of laser radars are arranged on the same side of the storage yard, only the point cloud data on one side of the storage yard can be scanned, and then the storage yard on the other side is required to be simulated through point cloud mirror images, so that a complete storage yard is constructed.
In this embodiment 1, according to the radar view angle, appropriate points are selected at two ends and a middle position of a discharging track at one side of a storage yard, a laser radar is installed, the laser radar is remotely controlled, point cloud data at one side of the storage yard is scanned, and the scanning range of the laser radar is ensured to cover the whole storage yard.
In the 3D modeling of the fields of urban planning, hydraulic engineering, environmental monitoring, mine measurement and the like, point cloud data are needed to be used for constructing a model, the point cloud data can only be obtained through a laser radar, but in the prior art, an unmanned plane is used for carrying the laser radar to scan the whole field, if the data are required to be acquired at intervals, personnel are needed to control the unmanned plane to scan the whole field, the data updating is troublesome, and the acquired point cloud data amount is huge; in this embodiment 1, a plurality of radars are installed on one side of a yard, and the characteristics of symmetry are presented to a stockpile caused by a vertical blanking mode of the yard, so that only data on one side of the yard need to be collected, then data of different radars are spliced, the stockpile on the other side is simulated through mirror image, no omnibearing scanning is required, the data volume is greatly reduced, and the laser radars can be remotely controlled to re-scan the yard at intervals to update data in real time.
And S2, performing external parameter calibration on a plurality of laser radars by adopting a TFAC (Target-Free Automatic Calibration) automatic external parameter calibration algorithm, and determining external parameters of the laser radars, wherein the external parameters comprise pitch angle, roll angle and yaw angle of the laser radars.
And S3, setting the laser radars in the same IP network, selecting one laser radar as a standard, and unifying point cloud data of all the laser radars under a coordinate system of the laser radars according to external parameters of the laser radars, so that the point cloud data acquired by the laser radars are spliced together to obtain a yard laser point cloud, as shown in FIG. 2.
For a plurality of laser radars, the generated point cloud data are based on a coordinate system taking the laser radars as an origin, so that after the external parameter calibration of the laser radars is finished, external parameters of the laser radars can be determined, namely pitch angle, roll angle and yaw angle of the laser radars are determined; when a plurality of radars are spliced to obtain 3D coverage with a larger range, the radars are arranged in the same IP network, one radar is selected as a standard, and the point cloud data of all the radars are unified under the coordinate system of the radar through the external parameters, so that the point cloud data of all the laser radars can be spliced well.
Step S3 further includes: voxel division is carried out on the laser point cloud of the storage yard, and the centroid P of the voxels is obtained by adopting a PCL point cloud downsampling method centroid (x centroid ,y centroid ,z centroid ) Instead of all points in the voxel, the voxel of the point cloud data is realized, as shown in fig. 3;
wherein the voxel centroid P centroid (x centroid ,y centroid ,z centroid ) Is specifically shown as
(x i ，y i ，z i ) The coordinate of the ith point in the voxel is given, and m is the number of all points in the voxel.
The voxelized point cloud data is beneficial to reducing random memory access, increasing data operation efficiency and processing point cloud data with larger orders of magnitude.
And S4, dividing the laser point cloud of the storage yard by using a RANSAC plane division algorithm, dividing the storage yard, the ground and the dome to obtain a laser point cloud set of the storage yard, the ground and the dome, extracting a storage yard model, and using the storage yard model for subsequent mirror image processing and volume calculation, wherein the storage yard laser point cloud set is shown in FIG. 4.
Compared to computing each point P in the point cloud P i Normal vector information of (2) for each point P i The RANSAC algorithm is an outlier detection method, is little influenced by outliers (discrete points), can effectively process outliers in the storage yard point cloud data, and has stronger robustness; and the RANSAC algorithm does not need to calculate the normal vector information of each point in the point cloud in advance, and can directly use the geometric information of the point cloud to carry out simulation fitting and segmentation.
Since the multiple radars are installed on one side of the storage yard in the embodiment 1, the vertical blanking mode is adopted for the storage yard, and the material pile presents a symmetrical shape, in the embodiment 1, in the step S1, only the point cloud data on one side of the storage yard is scanned, therefore, before the step S5, the embodiment 1 of the invention also needs to obtain the plane corresponding to the maximum section of the storage yard body as ax+by+cz+d=0, and calculate the point cloud (x 0 ,y 0 ,z 0 ) Regarding the symmetrical point (x ', y ', z ') of the plane ax+by+cz+d=0, the point cloud reconstruction of the complete yard is realized By simulating the blocked side of the stacker through the point cloud mirror image, as shown in fig. 6;
wherein A, B, C, D are plane coefficients,
step S5, constructing a Delaunay triangle network according to the stacking model, as shown in FIG. 7;
delaunay triangulation is a collection of connected but non-overlapping triangles, and the circles circumscribed by these triangles do not contain any other points of this area.
In step S5, the specific implementation procedure for constructing the Delaunay triangulation network includes:
step S51, defining a super triangle containing all calculation areas according to the point cloud coordinate distribution, and taking out a triangle ABC from the triangle set, wherein the vertexes of the triangle ABC are A (x 1, y 1), B (x 2, y 2), C (x 3, y 3), and the circumscribed circle of the triangle ABC is calculated as follows:
wherein the triangle set is a triangle formed by the point cloud point set and meeting Delaunay condition, (x) 0 ，y 0 ) The coordinate of the circle center of the circumscribed circle of the triangle ABC, and r is the radius of the circumscribed circle of the triangle ABC;
step S52, inserting a point P into the triangle ABC, acquiring and deleting all triangles M in the triangle set to form a cavity, and connecting the point P with the cavity nodes to form a new triangle grid, wherein the circumscribed circle of the triangle M contains the point P;
and step S53, updating the data structure, filling the deleted triangle data with the newly generated triangle mesh, returning to the step S51, and repeatedly executing the steps S51 to S53 until all points are interpolated to construct the Delaunay triangle mesh.
Between step S5 to step S6, the method further comprises: and (3) repairing the point cloud holes of the constructed Delaunay triangular net, and recovering the surface shape of the model, as shown in fig. 8, so as to improve the accuracy of three-dimensional reconstruction and volume calculation of the storage yard.
Step S6, taking each triangle in the Delaunay triangle network as a bottom surface, taking the average value of the three-point-to-plane distances corresponding to the vertexes of the triangle as a height, and calculating the storage yard volume as the sum of the volumes of all the triangular prisms, as shown in FIG. 9.
Example 2
Embodiment 2 of the present invention provides a terminal device corresponding to embodiment 1, where the terminal device may be a processing device for a client, for example, a mobile phone, a notebook computer, a tablet computer, a desktop computer, etc., so as to execute the method of the embodiment.
The terminal device of the present embodiment includes a memory, a processor, and a computer program stored on the memory; the processor executes the computer program on the memory to implement the steps of the method of embodiment 1 described above.
In some implementations, the memory may be high-speed random access memory (RAM: random Access Memory), and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
In other implementations, the processor may be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), or other general-purpose processor, which is not limited herein.
Example 3
Embodiment 3 of the present invention provides a computer-readable storage medium corresponding to embodiment 1 described above, on which a computer program/instructions is stored. The steps of the method of embodiment 1 described above are implemented when the computer program/instructions are executed by a processor.
The computer readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the preceding.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java, and an transliterated scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (9)
1. The storage yard volume calculation method based on three-dimensional reconstruction is characterized by comprising the following steps of:
s1, collecting yard point cloud data by using a plurality of laser radars arranged in a yard;
s2, performing external parameter calibration on the laser radar, and determining external parameters of the laser radar; the external parameters comprise pitch angle pitch, roll angle roll and yaw angle yaw of the laser radar;
s3, arranging the laser radars in the same IP network, and unifying point cloud data acquired by different laser radars to the same coordinate system according to external parameters of the laser radars, so that the point cloud data acquired by a plurality of laser radars are spliced together to obtain a yard laser point cloud; the same coordinate system is used for selecting one laser radar as a standard, and the point cloud data of all the laser radars are unified under the coordinate system of the laser radar;
s4, dividing the laser point cloud of the storage yard to obtain a laser point cloud set of a material pile, the ground and a dome, and extracting a storage model;
s5, constructing a Delaunay triangle network according to the stacking model;
s6, taking each triangle in the Delaunay triangle network as a bottom surface, taking the average value of the three-point-to-plane distances corresponding to the vertexes of the triangles as a height, and calculating the storage yard volume as the sum of the volumes of all the triangular prisms.
2. The method according to claim 1, wherein in step S2, the laser radar is calibrated by using a TFAC automatic external parameter calibration algorithm.
3. The method of claim 1, wherein in step S3, further comprising: voxel division is carried out on the storage yard laser point cloud, and a voxel centroid P is adopted centroid (x centroid ,y centroid ,z centroid ) Instead of all points within a voxel, wherein the voxel centroid P centroid (x centroid ,y centroid ,z centroid ) Is that
(x i ，y i ，z i ) The coordinate of the ith point in the voxel is given, and m is the number of all points in the voxel.
4. The method according to claim 1, wherein in the step S4, a RANSAC plane segmentation algorithm is used to segment the yard laser point cloud.
5. The method of claim 1, wherein when the plurality of lidars are disposed on one side of the yard in the step S1, further comprising, between the steps S4 to S5: obtaining a plane corresponding to the maximum tangent plane of the stacking body as ax+by+cz+d=0, and calculating a three-dimensional space point cloud (x 0 ,y 0 ,z 0 ) Regarding symmetry points (x ', y ', z ') of the plane ax+by+cz+d=0, simulating one side of the stockpile, which is shielded, through point cloud mirroring, and reconstructing a complete stockpile model;
wherein A, B, C, D are plane coefficients,
6. the method for calculating the volume of the storage yard based on the three-dimensional reconstruction according to claim 1, wherein in the step S5, the specific implementation process of constructing the Delaunay triangulation network includes:
s51, defining a super triangle containing all calculation areas according to the point cloud coordinate distribution, taking out a triangle ABC from a triangle set, wherein the vertex of the triangle ABC is A (x 1, y 1), B (x 2, y 2), C (x 3, y 3), and calculating the circumscribed circle of the triangle ABC as follows:
wherein the triangle set is a triangle formed by the point cloud point set and meeting Delaunay condition, (x) 0 ，y 0 ) The coordinates of the circle center of the circumscribed circle of the triangle ABC are the radius of the circumscribed circle of the triangle ABC;
s52, inserting a point P into the triangle ABC, acquiring and deleting all triangles M in the triangle set to form a cavity, and connecting the point P with the cavity nodes to form a new triangle grid, wherein the circumscribed circle of the triangle M comprises the point P;
and S53, updating the data structure, filling the deleted triangle data with the newly generated triangle mesh, returning to the step S51, and repeatedly executing the steps S51 to S53 until all points are interpolated to construct the Delaunay triangle mesh.
7. The method of claim 1, further comprising, between the step S5 and the step S6: and (3) repairing the point cloud holes of the Delaunay triangular net to recover the surface shape of the model.
8. A terminal device, comprising:
one or more processors;
a memory having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the steps of the method of any of claims 1-7.
9. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the steps of the method according to any one of claims 1-7.
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| CN118196326A (en) * | 2024-03-27 | 2024-06-14 | 淮阴工学院 | Three-dimensional modeling and volume calculation method for stock yard stock |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118196326A (en) * | 2024-03-27 | 2024-06-14 | 淮阴工学院 | Three-dimensional modeling and volume calculation method for stock yard stock |
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