A method and device for obtaining point cloud data based on a simulated scanned BIM model

Abstract

The application discloses a kind of based on simulation scanning BIM model acquisition point cloud data method and device, wherein, point cloud data acquisition method includes: semantic segmentation;In BIM model, set arbitrary point as initial point;With input point as the center of circle to generate sphere;Based on sphere surface to obtain subdivision point;Point set is connected to line by Line battery and initial point;Retain each straight line and each initial point in the nearest point;Classification output point cloud data.The method of the application is simple, reasonable and easy to implement, and can quickly obtain the point cloud data of BIM model. Through semantic segmentation in advance, the classification of point cloud data is realized, the acquisition method of point cloud data is expanded, and the efficiency and accuracy of data acquisition are improved.

Description

Technical Field

[0001] This invention relates to a method and apparatus for acquiring point cloud data based on simulated scanning of BIM models, belonging to the field of point cloud data acquisition technology. Background Technology

[0002] Point cloud data refers to a collection of vectors in a three-dimensional coordinate system. Each point contains three-dimensional coordinates, and some may contain information such as color. High-precision 3D point cloud data is used to generate curved surfaces through algorithm fitting, which can better represent the three-dimensional morphological features of the measured object. It can not only assist in building 3D digital models of buildings, but also has important applications in the development of molds and products in automobiles, aviation, ceramics, etc.; rapid prototyping of antiques, handicrafts, sculptures, and figurines; mechanical shape design; medical aesthetics; human body shape creation; human shape measurement; and plant morphology acquisition.

[0003] With the development of the modern construction industry, increasingly complex building structures and internal pipe networks have created an urgent need for precise management and maintenance of buildings, requiring the application of digital twin technology to improve management and maintenance levels. However, there is currently no good method for creating BIM models of existing buildings. Point cloud technology offers one possibility, but how to intelligently identify building components from point cloud data acquired through laser scanning and create BIM models remains a problem that urgently needs to be solved.

[0004] Intelligent recognition requires a large amount of point cloud data to train the model; however, building a massive point cloud database is extremely difficult. Currently, methods for acquiring point cloud data mainly include 3D laser scanning, camera photography, and 3D reconstruction of 2D images. The most commonly used traditional method is 3D laser scanning. While it offers flexibility in selecting data acquisition locations and allows for point cloud acquisition during scanning, its drawbacks include the need for on-site visits. Furthermore, when acquiring objects with complex shapes, the acquisition speed is slow, data loss is significant, and the acquired point cloud data lacks color and annotation, resulting in slow subsequent 3D building reconstruction, requiring manual segmentation, low accuracy, and hindering the synthesis of 3D point cloud data. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method and apparatus for acquiring point cloud data based on simulation scanning of BIM model, which can quickly acquire point cloud data of BIM model, classify point cloud data through semantic segmentation, expand the acquisition method of point cloud data, and improve the efficiency and accuracy of data acquisition.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] A method for acquiring point cloud data based on simulation scanning of a BIM model includes the following steps:

[0008] Step 1: Semantically segment the building BIM model used for simulation scanning according to component type, so that components of the same type are placed on the same layer and components of different types are placed on different layers. Export each layer one by one, with each layer corresponding to a semantically segmented BIM model.

[0009] Step 2: For any semantically segmented BIM model, set any point as the initial point;

[0010] Step 3: Generate a sphere with the initial point as the center, so that the sphere can enclose the BIM model after semantic segmentation described in Step 2;

[0011] Step 4: Generate a mesh on the surface of the sphere generated in step 3, and obtain a set of subdivided points based on the mesh;

[0012] Step 5: Connect each subdivision point in the subdivision point set to the initial point to form a line segment set;

[0013] Step 6: Intersect each line segment in the line segment set with the semantically segmented BIM model described in Step 2 to obtain the intersection point; when any line segment has multiple intersection points with the semantically segmented BIM model described in Step 2, retain the point closest to the initial point as the point cloud data of the semantically segmented BIM model described in Step 2.

[0014] Step 7: For each exported layer, repeat steps 2-6 to obtain the point cloud data corresponding to each layer and each component type.

[0015] Step 8: Perform the operations of Steps 2-6 on the building BIM model used for simulation scanning described in Step 1 to obtain the point cloud data corresponding to the building BIM model used for simulation scanning. Perform occlusion processing on the point cloud data corresponding to each type obtained in Step 7 and the point cloud data corresponding to the building BIM model used for simulation scanning to obtain the final point cloud data.

[0016] As a preferred embodiment of the method of the present invention, the component types mentioned in step 1 include slabs, beams, columns, walls, and pipes.

[0017] As a preferred embodiment of the method of the present invention, the software used to export each layer one by one in step 1 is 3ds Max.

[0018] As a preferred embodiment of the method of the present invention, the number of arbitrary points in step 2 is one or more; when the number of arbitrary points is multiple, the sphere generated by each arbitrary point can wrap the BIM model after semantic segmentation in step 2.

[0019] As a preferred embodiment of the method of the present invention, the specific process of the occlusion processing in step 8 is as follows: the point cloud data corresponding to each type obtained in step 7 is compared with the point cloud data corresponding to the building BIM model used for simulation scanning, and the point cloud data in each type that overlaps with the point cloud data corresponding to the building BIM model used for simulation scanning is retained, thereby obtaining the final point cloud data.

[0020] A device for acquiring point cloud data based on simulation scanning of a BIM model, the device comprising a feature segmentation module, a data scanning module, and a data occlusion processing module;

[0021] The feature segmentation module is used to semantically segment the building BIM model used for simulation scanning according to component type, so that components of the same type are placed on the same layer, and components of different types are located on different layers. Each layer is exported one by one, and each layer corresponds to a semantically segmented BIM model.

[0022] The data scanning module is used to scan each exported layer to obtain point cloud data corresponding to each layer, i.e., each component type; it is also used to scan the building BIM model used for simulation scanning to obtain point cloud data corresponding to the building BIM model used for simulation scanning.

[0023] The data occlusion processing module is used to occlude the point cloud data corresponding to each type with the point cloud data corresponding to the building BIM model used for simulation scanning, so as to obtain the final point cloud data.

[0024] As a preferred embodiment of the apparatus of the present invention, the scanning of each exported layer specifically involves the following process:

[0025] A. For each exported layer and each semantically segmented BIM model, set any point as the initial point;

[0026] B. Generate a sphere with the initial point as the center, so that the sphere can enclose the semantically segmented BIM model;

[0027] C. Generate a mesh on the surface of the generated sphere, and obtain a set of subdivision points based on the mesh;

[0028] D. Connect each subdivision point in the subdivision point set to the initial point to form a line segment set;

[0029] E. Intersect each line segment in the line segment set with the semantically segmented BIM model to obtain the intersection point; when any line segment has multiple intersection points with the semantically segmented BIM model, retain the point closest to the initial point as the point cloud data of the semantically segmented BIM model.

[0030] A computer device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the steps of the method for acquiring point cloud data based on a simulation scanning BIM model as described above.

[0031] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method for acquiring point cloud data based on a simulation scanning BIM model as described above.

[0032] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0033] 1. This invention solves the problem of insufficient point cloud data for buildings. Compared with traditional point cloud data acquisition methods, this method is simple in steps, reasonable in design, convenient to implement, and has high accuracy.

[0034] 2. The present invention uses a simulation scanning method to obtain point cloud data with the characteristics of point cloud data obtained by laser scanning, and has an occlusion effect, that is, for the scanned point, there is at most one point data in any direction.

[0035] 3. This invention has a wide range of applications and can be used for all BIM models. Attached Figure Description

[0036] Figure 1 This is a flowchart of the method for obtaining point cloud data based on simulation scanning of a BIM model according to the present invention;

[0037] Figure 2 This is a screenshot of the semantic segmentation result;

[0038] Figure 3 It is the implementation form of simulated scanning;

[0039] Figure 4 This is the result of the simulation scan;

[0040] Figure 5 It is the hierarchical structure of the basic database;

[0041] Figure 6 It is a digital representation of a point cloud model. Detailed Implementation

[0042] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0043] like Figure 1As shown, this invention provides a method for obtaining point cloud data based on simulation scanning of a BIM model. The specific steps are as follows:

[0044] Step 1: Manually semantically segment the BIM model, placing each type of component on a separate layer, and export each layer one by one.

[0045] Component types can be divided into basic structures such as slabs, beams, columns, walls, and pipes. Layer export is done using 3ds Max software.

[0046] Step 2: In the BIM model after semantic segmentation, set any point as the initial point. The setting of the point should take into account the practical feasibility. There can be one or more initial points.

[0047] Step 3: Generate a sphere with the input point as the center, which should be able to wrap the semantically segmented BIM model. When there are multiple initial points, the sphere generated by each initial point should be able to wrap the semantically segmented BIM model.

[0048] Step 4: Generate a mesh based on the surface of the sphere, and obtain subdivision points based on the sphere mesh; the density of subdivision points can be defined as the number of points per unit area on the sphere.

[0049] Step 5: Generate a line segment between the point set and the initial point.

[0050] Step 6: When multiple points on the line coincide with the model, retain the point closest to the initial point.

[0051] Step 7: Categorize and output point cloud data. The categorized point cloud data also needs to be processed to remove occlusion effects. That is, only the points that overlap with the simulation scan results of the overall model (the complete BIM model that has not been exported in layers) are retained in the exported partial models of each layer.

[0052] Furthermore, a point cloud database can be constructed and verified based on the point cloud data obtained in step 7:

[0053] 1. Building a database for point cloud data;

[0054] First, the point cloud data obtained in step 7 is merged, modified, and color parameters are added sequentially.

[0055] The database construction method is hierarchical: the first level is for each component, the second level is for each model, and the third level is for each point.

[0056] 2. Verification of the point cloud model;

[0057] The verification method is visualization. When the point cloud data density is too high, the point cloud model can be thinned before digital display.

[0058] Example

[0059] A method for acquiring point cloud data based on simulation scanning of a BIM model includes the following steps:

[0060] Step S1 involves format conversion and manual semantic segmentation of the BIM model used for simulation scanning. Based on the information of the BIM model components themselves, the BIM model is exported according to different component types, such as slabs, beams, columns, walls, and pipes. Specifically, the file format is set using the export function of 3ds Max software, converting the RVT format file to FBX format. At this point, independent FBX models of basic building components (slabs, beams, columns, walls, and pipes) have been obtained. The semantic segmentation effect is shown in the figure below. Figure 2 As shown.

[0061] Step S2: Import the basic building components obtained in Step S1 into Rhino by component type, and set the initial points with coordinates (x... a1 ,y a1 ,z a1 Of course, multiple points can also be set here {(x) a1 ,y a1 ,z a1 ),(x a2 ,y a2 ,z a2 ),…,(x an ,y an ,z an To set a point location, use Rhino's "Add Point" command to set its x, y, and z coordinates in the command window. The location of the point should consider the actual feasible location of the point cloud scanner in reality.

[0062] Step S3: Using the points set in step S2 as the center, use Grasshopper (hereinafter referred to as GH) to generate a sphere network. Specifically, it is generated through GH's Sphere cell. Input the coordinates of the sphere center and the sphere radius. The sphere radius ensures that the spheres generated at each point can wrap the semantically segmented BIM model.

[0063] Step S4: Based on the sphere obtained in step S3, use the DivideSurface battery in GH to mesh the surface of the sphere to obtain subdivision points {(x b1 ,y b1 ,z b1 ),(x b2 ,y b2 ,z b2 ),…,(x bm ,y bm ,z bmBy setting the Ucount and Vcount parameters of the DivideSurface battery (Ucount = Vcount in this example), different scan densities can be simulated. The implementation of the simulated scan is as follows: Figure 3 As shown, the simulated scan result is as follows: Figure 4 As shown.

[0064] Step S5, using the Line battery in GH, define the end point parameter as the point set {(x b1 ,y b1 ,z b1 ),(x b2 ,y b2 ,z b2 ),…,(x bm ,y bm ,z bm The start point parameter is the initial point (x) a1 ,y a1 ,z a1 This allows the initial point to be connected to the subdivision points to form line segments. The set of connected line segments can simulate the laser beams used in real point cloud scanning.

[0065] Step S6: Using GH's Mesh Curve cell, generate intersection points between the line segment set obtained in Step S5 and the BIM model imported in Step S2; then, using GH's Closest point cell, retain each straight line and each initial point (x). a1 ,y a1 ,z a1 The nearest point (x) in ) c1 ,y c1 ,z c1 ).

[0066] Step S7: Output point cloud data through panel battery classification in GH.

[0067] The point cloud data output in step S7 is subjected to feature calibration to obtain multiple key point cloud features. These key point cloud features may include, for example, spatial coordinates (location information), color information, and type information. In specific implementations, spatial coordinates, color information, and type information of the point cloud data can be obtained through coordinate transformation. Point cloud data from different construction types form a point cloud dataset. Generally, a component of the same type is represented by i, and the point cloud data of component i can be represented by a set C. i express:

[0068] C i ={C i1 C i2 ,...,C ij Cij+1 ,...,C im}

[0069] In the formula, C ij This represents the j-th point in component i. For C ij The data is the set of nearest points obtained by component i through steps S2-S6, with added color and type information. It can generally be represented by a vector A. i express:

[0070] A i ∈x ij ×y ij ×z ij ×r ij ×b ij ×g ij ×t i

[0071] In the formula, the element values ​​of vector A are equal to the encoded values ​​of the point cloud data, x ij ,y ij ,z ij These represent the spatial coordinates of the point cloud, specifically obtained through step S6; r ij ,b ij ,g ij This represents the color information of the point cloud; t i This refers to the type of the component.

[0072] When i = 0, C0 refers to the point cloud dataset obtained after scanning the complete BIM model.

[0073] To more closely approximate the point cloud data obtained from real-world scanning, occlusion effects are applied.

[0074] The processing method is as follows: the point cloud data of component i under occlusion can be represented by a set B. i It means that B is one of them. i =C i ∩C0, specifically implemented through the Find similar member battery in GH, C i The intersection of B and C0 is obtained. Similarly, for B... ij The data can generally be represented by a vector B. i express:

[0075] B i ∈x ij ×y ij ×z ij ×r ij ×b ij ×g ij ×t i

[0076] The method for constructing and verifying a point cloud database includes the following processes:

[0077] 1) Building a database for point cloud data, with a point cloud database framework such as... Figure 5 As shown, the first level consists of various components, the second level consists of various models, and the third level consists of various points.

[0078] 2) Verification of the point cloud model. The point cloud databases of each component are visualized using Cloudcompare, and the results are shown below. Figure 6 As shown.

[0079] Based on the same inventive concept, this invention also proposes a device for obtaining point cloud data based on simulation scanning of BIM model, including a feature segmentation module, a data scanning module and a data occlusion processing module.

[0080] The feature segmentation module is used to semantically segment the building BIM model used for simulation scanning according to component type, so that components of the same type are placed on the same layer, and components of different types are located on different layers. Each layer is exported one by one, and each layer corresponds to a semantically segmented BIM model.

[0081] The data scanning module is used to scan each exported layer to obtain point cloud data corresponding to each layer, i.e., each component type; it is also used to scan the building BIM model used for simulation scanning to obtain point cloud data corresponding to the building BIM model used for simulation scanning.

[0082] The data occlusion processing module is used to perform occlusion processing on the point cloud data corresponding to each type and the point cloud data corresponding to the building BIM model used for simulation scanning, so as to obtain the final point cloud data.

[0083] Each exported layer is scanned, and the specific process is as follows:

[0084] A. For each exported layer and each semantically segmented BIM model, set any point as the initial point;

[0085] B. Generate a sphere with the initial point as the center, so that the sphere can wrap the semantically segmented BIM model;

[0086] C. Generate a mesh on the surface of the generated sphere, and obtain a set of subdivision points based on the mesh;

[0087] D. Connect each subdivision point in the subdivision point set to the initial point to form a line segment set;

[0088] E. Intersect each line segment in the line segment set with the semantically segmented BIM model to obtain the intersection point; when any line segment has multiple intersection points with the semantically segmented BIM model, retain the point closest to the initial point as the point cloud data of the semantically segmented BIM model.

[0089] Based on the same inventive concept, this application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the aforementioned method for acquiring point cloud data based on a simulation scanning BIM model.

[0090] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned method for acquiring point cloud data based on a simulated scanning BIM model.

[0091] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can 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, etc.) containing computer-usable program code.

[0092] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0093] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0094] 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, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0095] The above embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A method for obtaining point cloud data based on a simulated scanned BIM model, characterized in that, Includes the following steps: Step 1: Semantically segment the building BIM model used for simulation scanning according to component type, so that components of the same type are placed on the same layer and components of different types are placed on different layers. Export each layer one by one, with each layer corresponding to a semantically segmented BIM model. Step 2: For any semantically segmented BIM model, set any point as the initial point; Step 3: Generate a sphere with the initial point as the center, so that the sphere can enclose the BIM model after semantic segmentation described in Step 2; Step 4: Generate a mesh on the surface of the sphere generated in step 3, and obtain a set of subdivided points based on the mesh; Step 5: Connect each subdivision point in the subdivision point set to the initial point to form a line segment set; Step 6: Intersect each line segment in the line segment set with the semantically segmented BIM model described in Step 2 to obtain the intersection point; When any line segment has multiple intersections with the semantically segmented BIM model described in step 2, the point closest to the initial point is retained as the point cloud data of the semantically segmented BIM model described in step 2. Step 7: For each exported layer, repeat steps 2-6 to obtain the point cloud data corresponding to each layer and each component type. Step 8: Perform the operations of Steps 2-6 on the building BIM model used for simulation scanning described in Step 1 to obtain the point cloud data corresponding to the building BIM model used for simulation scanning. Perform occlusion processing on the point cloud data corresponding to each type obtained in Step 7 and the point cloud data corresponding to the building BIM model used for simulation scanning to obtain the final point cloud data.

2. The method for acquiring point cloud data based on a simulated scanning BIM model according to claim 1, characterized in that, The component types mentioned in step 1 include slabs, beams, columns, walls, and pipes.

3. The method for acquiring point cloud data based on a simulated scanning BIM model according to claim 1, characterized in that, The software used to export each layer individually as described in step 1 is 3ds Max.

4. The method for acquiring point cloud data based on simulation scanning of a BIM model according to claim 1, characterized in that, The number of arbitrary points in step 2 can be one or more; when there are multiple arbitrary points, the sphere generated by each arbitrary point can wrap the BIM model after semantic segmentation in step 2.

5. The method for acquiring point cloud data based on a simulated scanning BIM model according to claim 1, characterized in that, The specific process of the occlusion processing in step 8 is as follows: compare the point cloud data corresponding to each type obtained in step 7 with the point cloud data corresponding to the building BIM model used for simulation scanning, and retain the point cloud data in each type that overlaps with the point cloud data corresponding to the building BIM model used for simulation scanning, thereby obtaining the final point cloud data.

6. A device for acquiring point cloud data based on simulation scanning of a BIM model, characterized in that, The device includes a feature segmentation module, a data scanning module, and a data occlusion processing module. The feature segmentation module is used to semantically segment the building BIM model used for simulation scanning according to component type, so that components of the same type are placed on the same layer, and components of different types are located on different layers. Each layer is exported one by one, and each layer corresponds to a semantically segmented BIM model. The data scanning module is used to scan each exported layer to obtain point cloud data corresponding to each layer, i.e., each component type; it is also used to scan the building BIM model used for simulation scanning to obtain point cloud data corresponding to the building BIM model used for simulation scanning. The process of scanning each exported layer is as follows: A. For each exported layer and each semantically segmented BIM model, set any point as the initial point; B. Generate a sphere with the initial point as the center, such that the sphere can enclose the semantically segmented BIM model; C. Generate a mesh on the surface of the generated sphere, and obtain a set of subdivision points based on the mesh; D. Connect each subdivision point in the subdivision point set to the initial point to form a line segment set; E. Intersect each line segment in the line segment set with the semantically segmented BIM model to obtain the intersection point; When any line segment has multiple intersections with the semantically segmented BIM model, the point closest to the initial point is retained as the point cloud data of the semantically segmented BIM model; The data occlusion processing module is used to occlude the point cloud data corresponding to each type with the point cloud data corresponding to the building BIM model used for simulation scanning, so as to obtain the final point cloud data.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for acquiring point cloud data based on a simulation scanning BIM model as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for acquiring point cloud data based on a simulation scanning BIM model as described in any one of claims 1 to 5.

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