BIM-based curtain wall simulation design method and system

Abstract

The application relates to the technical field of architectural model design, and discloses a curtain wall simulation design method and system based on BIM, which comprises the following steps: obtaining architectural point cloud data containing complex curved surface features; combining various geometric features of the point cloud data to improve a traditional region growing algorithm to segment the point cloud data; fitting geometric primitives to the segmented point cloud data and constructing an architectural BIM model to complete curtain wall simulation design. The application aims to improve the accuracy of region growing algorithm in edge segmentation of different architectural planar point cloud data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of architectural model design, in particular to a curtain wall simulation design method and system based on BIM. BACKGROUND

[0002] With the development of the construction industry, building curtain walls, as an important part of the building facade, have multiple functions such as heat preservation, heat insulation, sound insulation, and aesthetics. Building information modeling (BIM) is a digital modeling technology that integrates various stages such as architectural design, construction, and operation management. BIM creates a three-dimensional digital model by collecting information data of buildings, integrates and collaborates various information in architectural design, and realizes the visualization and digitization of the architectural engineering simulation design process. The curtain wall simulation design method based on BIM realizes the whole process simulation and optimization of building curtain wall design, and improves the collaborative efficiency of design and construction.

[0003] However, in modern architectural design, in order to highlight the novel design concept and artistic effect, many buildings are designed with complex curved surface design style. When collecting the appearance data (such as point cloud data) of these buildings to construct the BIM model, due to the massive and complex geometric characteristics, the existing BIM model construction method has low accuracy in extracting geometric features from the point cloud data of the building and segmenting the point cloud data, resulting in a deviation between the generated BIM model and the actual building. This leads to problems and conflicts in the actual construction of the curtain wall simulation design scheme based on the BIM model, affecting the construction efficiency and reducing the construction quality. SUMMARY

[0004] To solve the above technical problems, the present application provides a curtain wall simulation design method and system based on BIM, and the technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a curtain wall simulation design method based on BIM, which comprises the following steps:

[0006] Obtain building point cloud data containing complex curved surface features;

[0007] Voxelize the point cloud data, and cluster the point cloud voxels based on the density features and curvature features of the point cloud voxels to select region growth seed points;

[0008] Construct an adaptive growth criterion using the curvature difference and normal vector difference between any to-be-merged point cloud voxel and the region of any seed point; and adjust the merging threshold in the adaptive growth criterion using the fitting surface error of the to-be-merged point cloud voxel and the region of the seed point to perform region growth on the point cloud voxels;

[0009] The segmented point cloud data is fitted with geometric primitives and a building BIM model is constructed to complete the curtain wall simulation design.

[0010] Preferably, the distance metric in the clustering of the point cloud voxels is positively correlated with the density feature difference and the curvature feature difference between the point cloud voxels, respectively.

[0011] Preferably, the selection of the region growing seed points comprises: selecting three point cloud voxels between the maximum distance and the minimum distance from the distances of all point cloud voxels in each cluster to the cluster center point cloud voxel as the seed points for the region growing, together with the cluster center point cloud voxel.

[0012] Preferably, the adaptive growth criterion comprises:

[0013] When the number of point cloud voxels in the region where the seed point is located is greater than or equal to a preset number, an adaptive threshold weight is constructed based on the fitting surface error of the point cloud voxel to be merged and the region where the seed point is located; otherwise, the adaptive threshold weight is assigned a value of 1.

[0014] The adaptive threshold weight is used as the weight of the preset curvature feature difference threshold and the preset normal vector feature difference threshold, respectively.

[0015] When the curvature difference is less than or equal to the weighted preset curvature feature difference threshold, and the normal vector difference is less than or equal to the weighted preset normal vector difference threshold, the point cloud voxel to be merged is determined as the merging condition of the region where the seed point is located, and the point cloud voxel to be merged is merged into the region; otherwise, no merging is performed.

[0016] Preferably, the curvature difference is the difference between the curvature feature of the point cloud voxel to be merged and the mean value of the curvature features of all point cloud voxels in the region where the seed point is located; and the curvature feature of the point cloud voxel is calculated by eigenvalue decomposition of the covariance matrix of the centroid point cloud in the point cloud voxel.

[0017] Preferably, the normal vector difference is the difference between the normal vector feature of the point cloud voxel to be merged and the mean value of the unit normal vector features of all point cloud voxels in the region where the seed point is located; and the normal vector feature of the point cloud voxel is determined by the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the centroid point cloud in the point cloud voxel.

[0018] Preferably, the method for calculating the fitting surface error of the point cloud voxel to be merged and the region where the seed point is located comprises:

[0019] When the number of point cloud voxels in the region where the seed point is located is greater than or equal to a preset number, the spatial distance between the point cloud voxel to be merged and the seed point is obtained.

[0020] Using the seed point as the center, draw a circle with the radius determined by the spatial distance. Fit the voxels of the point cloud in the region where all seed points are located within the circle to a surface. Calculate the geometric distance from the voxels of the point cloud to be merged to the surface, and use it as the fitting residual of the voxels of the point cloud to be merged.

[0021] The mean of the fitting residuals between the voxels of the point cloud to be merged and their eight neighboring voxels and the surface is used as the fitting surface error of the voxels of the point cloud to be merged and the seed point within the region.

[0022] Preferably, the step of constructing adaptive threshold weights based on the fitting surface error within the region where the point cloud voxels to be merged and the seed point are located includes: ,in Based on the voxels of the point cloud to be merged With the The adaptive threshold weights are constructed from the fitting surface errors within the regions of the seed points, where e is a natural constant. As a preset value, voxels of point cloud to be merged With the The fitting surface error within the region where each seed point is located.

[0023] Preferably, the adaptive threshold weights are used as the weights of the preset curvature feature difference threshold and the preset normal vector feature difference threshold respectively by multiplying the adaptive threshold weights by the preset curvature feature difference threshold and the preset normal vector feature difference threshold respectively.

[0024] Secondly, another embodiment of this application also provides a BIM-based curtain wall simulation design system, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the BIM-based curtain wall simulation design method described above.

[0025] This application has at least the following beneficial effects:

[0026] The application solves the problem that the point cloud data segmentation effect is poor due to the random selection of seed points and the establishment of growth criteria based on the curvature characteristics of the point cloud data when the traditional region growing segmentation algorithm is used to segment the building point cloud data. Firstly, the density and curvature characteristics of the point cloud voxel are considered to select the point cloud voxel seed point of region growing, which can avoid the interference of noise points when selecting the seed point, better cover the point cloud voxel of the region with different geometric characteristics, and improve the representativeness of the seed point. Then, the growth criteria are constructed according to the curvature characteristic difference and the normal vector characteristic difference of the different building plane point cloud data, and the adaptive weight of the growth criteria threshold is constructed by considering the fitting surface error between each to-be-merged point cloud voxel and the region where the seed point is located, so as to improve the accuracy of the region growing algorithm in segmenting the edge of the different building plane point cloud data, and facilitate the subsequent construction of the high-precision BIM model for the simulation design of the curtain wall scheme. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0028] Figure 1 The flowchart of the BIM-based curtain wall simulation design method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0029] Embodiment 1

[0030] The BIM-based curtain wall simulation design method provided by an embodiment of the present application, with reference to Figure 1 , the method comprises the following steps:

[0031] Step 1: Obtain building point cloud data containing complex curved surface features.

[0032] The three-dimensional point cloud data of the building containing complex curved surface features is obtained by using a unmanned aerial vehicle carrying a laser radar device.

[0033] The obtained building point cloud data is preprocessed, and the statistical outlier algorithm is used to remove noise points to improve the data quality. The statistical outlier algorithm is prior art, and its specific process will not be repeated.

[0034] Step 2: Improve the traditional region growing algorithm for point cloud data segmentation by combining various geometric characteristics of the point cloud data.

[0035] Because the collected building point cloud data contains massive amounts of complex curved building surface point cloud data, and these curved surface areas have more complex geometric structure features, traditional methods for building BIM models based on point cloud data have difficulty accurately extracting the geometric features of curved surface point clouds when processing massive amounts of complex curved surface point cloud data. For example, the segmentation algorithm based on region growth randomly selects seed points and formulates growth criteria based on the curvature features of the point cloud when segmenting the point cloud, resulting in poor segmentation effect of the point cloud data. This makes it difficult to accurately fit the geometric primitives of local building point cloud data when building the BIM model, and it is impossible to accurately obtain the geometric feature data of the building. Ultimately, this leads to the curtain wall simulation design based on the BIM model not matching the actual construction, affecting the efficiency of curtain wall construction.

[0036] To address the aforementioned issues, this application clusters point cloud voxels based on their density and curvature characteristics to obtain seed points for region growth. Then, it constructs growth criteria based on the curvature consistency and normal vector consistency constraints of point cloud data from different building planes. Furthermore, it considers the plane fitting error between each point cloud voxel to be merged and the seed point to construct an edge saliency score as an adaptive weight for the growth criterion threshold, thereby improving the accuracy of the region growth algorithm in edge segmentation of point cloud data from different building planes and obtaining clearer segmentation edges when segmenting different surfaces.

[0037] First, the point cloud data is voxelized to improve the efficiency of subsequent calculations of feature differences between point clouds. Point cloud voxelization involves dividing a discrete point cloud in 3D space into a series of voxels, each containing several point clouds. Then, the features of the centroid point cloud within each voxel are used as the features of that voxel. These centroid point cloud features include, but are not limited to, spatial location, curvature, normal vector differences, and density features. Specifically, a voxel mesh coordinate system is created in 3D space, with each voxel having a resolution of [resolution missing]. , The size can be set by the implementer according to the scenario, without special limitations, based on the building point cloud data collected in this scenario. The size is set to 0.1m.

[0038] In traditional point cloud segmentation methods based on region growing, seed points are often selected randomly. This approach lacks consideration for the geometric features of the point cloud and makes it difficult to guarantee that the data features of different surfaces in the entire building point cloud data are representative. This may result in the selected seed points falling on noise points or boundary points, which can easily lead to oversegmentation or incomplete segmentation in the subsequent growth results.

[0039] The obtained point cloud voxel data is ,in Indicates the first A point cloud voxel, assuming The CCP includes A point cloud.

[0040] The point cloud voxels are clustered according to the density and curvature features of the point cloud voxels to obtain region-growing point cloud voxel seed points. The density feature of the point cloud voxel is , The edge length of the point cloud voxel The density represents the local compactness of the point cloud, and the number of point clouds in all point cloud voxels will not be completely the same. On a plane, the point clouds are uniformly distributed after projection, while for regions such as curved surfaces or glass cavities, the distribution of the point clouds presents a certain sparseness due to the influence of the curved surface angle reflection. High-density regions often correspond to smooth building planes, and low-density regions are likely to be curved surface or glass cavity regions.

[0041] The curvature reflects the bending degree of the local surface in the building point cloud, and regions with smaller curvature correspond to planes or gentle curves, and regions with larger curvature represent corners and complex curves. For the point cloud voxel , the covariance matrix of the centroid point cloud is calculated, and then the eigenvalues , , of the covariance matrix are obtained by eigenvalue decomposition, and the curvature feature of the point cloud voxel The covariance matrix is calculated by the neighborhood of the point cloud, and the calculation of the covariance matrix and the eigenvalue decomposition are prior art, and the specific process will not be repeated.

[0042] According to the above analysis, the distance measurement when clustering the point cloud voxels is positively correlated with the density feature difference and the curvature feature difference between the point cloud voxels.

[0043] It can be understood that the positive correlation means that the dependent variable will increase with the increase of the independent variable, and the dependent variable will decrease with the decrease of the independent variable, which is determined by actual application, and the present application does not make special limitation.

[0044] In the embodiment, the distance measurement formula when clustering the point cloud voxels is further represented as:

[0045]

[0046] Wherein, Sig is a Sigmoid function, which maps the density feature and the curvature feature of the point cloud voxel to [0, 1], , respectively represent the density features of the first , point cloud voxel, , respectively represent the density features of the first , curvature features of a point cloud voxel. The point cloud voxel clustering considers the density and curvature features for clustering, and finally all point cloud voxels are divided into clusters of point cloud voxels with similar density and curvature features. The point cloud voxel clustering method adopts the K-means algorithm, The size can be set by the implementer according to the implementation scene, and no special limitation is made. If the volume of the modeled building is larger and the complexity of the building design is higher, the value of the size can be appropriately increased to find more point cloud voxels with different features. In the embodiment, the size is 100.

[0047] Further, the three point cloud voxels between the first quartile and the second quartile and the minimum distance to the maximum distance among the distances from all point cloud voxels in each cluster to the cluster center point cloud voxel are selected as the seed points for region growing.

[0048] The seed points selected by the strategy can select representative point cloud voxels from each cluster with different features, avoid the interference of noise points, better cover the point cloud voxels in the building point cloud data with different geometric feature regions, improve the representativeness and stability of the seed points, and facilitate more comprehensive region segmentation.

[0049] Thus, the seed point selection strategy is formulated.

[0050] Further, the growth criterion of the region growing algorithm is constructed to segment different planes of the point cloud data.

[0051] The traditional region growing algorithm judges the difference in normal vector and curvature feature based on a fixed threshold. This setting method of the growth criterion will cause the segmentation edges of different planes to be inaccurate, and the reason for this phenomenon comes from the complexity and non-uniformity of the building point cloud data itself. When collecting the building point cloud data, the occlusion and glass mirror reflection effect are usually included, which will introduce the point cloud scattering caused by the measurement error and the light reflection interference, thereby causing the normal vector difference of the voxels in the same plane region to be large. The use of a fixed threshold will cause the plane edges to be excessively segmented, so that the segmentation result is inaccurate.

[0052] To solve this problem, the edge saliency score of the voxel to be merged is constructed as an adaptive weight of the threshold by considering the fitting surface residual feature of the voxel to be merged and the region of the seed point.

[0053] For the setting of the growth criterion, the region where the first seed point is located and the point cloud voxel to be merged are taken as an example to construct the growth criterion. Herein, is the selected ​one of the seed points.

[0054] First is the curvature difference:

[0055]

[0056] wherein denotes the curvature feature of the point cloud voxel and the curvature difference of the region where the th seed point is located, denotes the curvature feature of the point cloud voxel , which is calculated as described in the above steps, is the mean value of the curvature features of all point cloud voxels in the region where the th seed point is located, and the calculation process is not described again.

[0057] Then is the normal vector difference:

[0058]

[0059] wherein denotes the normal vector difference of the point cloud voxel and the region where the th seed point is located, is the inverse cosine function, denotes the normal vector feature of the point cloud voxel , i.e. the eigenvector corresponding to the smallest eigenvalue of the covariance matrix of the centroid point cloud in the point cloud voxel , denotes the mean value of the unit normal vector features of all point cloud voxels in the region where the th seed point is located, and is represented as follows:

[0060]

[0061] wherein denotes the number of point cloud voxels in the region where the th seed point is located, denotes the normal vector of the th point cloud voxel in , denotes the unitization of the normal vector, ensuring that the final is a standardized unit normal vector.

[0062] Based on the curvature feature and the normal vector feature to determine the judgment feature of the growth criterion, the normal vector difference and the curvature difference of the point cloud voxel can be considered as a constraint, and the flatness and directionality of the point cloud voxel are described, which effectively distinguishes the difference between the plane and various curved surfaces, and avoids the influence of the single feature on the segmentation accuracy.

[0063] Further, the surface equation of the region where the first seed point is located is fitted, when the region The Euclidean distance is used to calculate the spatial distance between the to-be-merged point cloud voxel

[0064]

[0065] The smaller the residual error is, the more the point satisfies the fitted surface, and the higher the probability of belonging to the region is. Conversely, the point deviates from the fitted surface, and may belong to other surfaces or is a boundary point of two surfaces.

[0066] Further, the fitting residual error of the eight-neighborhood voxel points of The larger the fitting residual error is, the greater the deviation of the voxel centered on The smaller the fitting residual error is, the higher the probability of being a boundary point or belonging to a different surface is. In the subsequent adaptive weight threshold, the threshold is increased to avoid the measurement error and the light reflection interference problem causing the error boundary segmentation.

[0067] ​​​​​​​​​​​​​​​​​​​​​​​​​In summary, the improved growth criterion judgment function based on the fitting surface error of the to-be-merged point cloud voxel and the region where the seed point is located can be expressed as:

[0068]

[0069] wherein is the adaptive threshold weight constructed based on the fitting surface error of the to-be-merged point cloud voxel and the region where the first seed point is located, wherein e is a natural constant, is a preset value, for adjusting the sensitivity of the adaptive threshold weight to the fitting surface error, the size of which can be set by the implementer according to the implementation scenario, without special limitation, and in the present embodiment the size of is taken as 2. In particular, when the region , , the size of the threshold is not adjusted when the number of merged voxels is too small considering the actual volume size of the building. is a preset curvature feature difference threshold, the size of which is taken as the curvature variance of all point cloud voxels, is a preset normal vector feature difference threshold, the size of which can be set by the implementer according to the implementation scenario, without special limitation, and in the present embodiment the size of .

[0070] When , the point cloud voxel satisfies the merging condition of the region where the first seed point is located, the point cloud voxel is merged into the region ; otherwise, when , the point cloud voxel is not merged.

[0071] The growth criterion thus designed can adaptively adjust the threshold size according to the fitting surface residual error of the neighborhood of the to-be-merged point cloud voxel and the merged region, avoiding the problem of inaccurate planar edge segmentation caused by the measurement error and light reflection interference introduced by the large amount of occlusion and glass mirror reflection effect in the point cloud data of the building, and improving the accuracy of subsequent BIM model modeling.

[0072] Repeat the above steps to complete the segmentation of the point cloud data.

[0073] Step three: fitting the segmented point cloud data into geometric primitives and constructing a building BIM model to complete the curtain wall simulation design.

[0074] The RANSAC algorithm is used to fit the geometric primitives of the segmented point cloud clusters to obtain the geometric information of each point cloud cluster. The RANSAC algorithm is a prior art, and its specific process will not be described here.

[0075] Finally, a parametric building BIM model is automatically generated based on the segmented point cloud data and the fitted geometric primitive information using Revit or other software, which is used to complete the simulation design of the curtain wall scheme.

[0076] Embodiment 2

[0077] Another embodiment of the present application also provides a BIM-based curtain wall simulation design system, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the BIM-based curtain wall simulation design method described above when executing the computer program.

[0078] Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the present application cover any and all variations of the application that come within the scope of the general inventive concepts described herein and that the application include all modifications, alterations and equivalents thereof.

[0079] It should be understood that the application is not limited to the precise construction that has been described above and shown in the accompanying drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the application.

Claims

1. A BIM-based curtain wall simulation design method, characterized in that, The method comprises the following steps: Obtain building point cloud data containing complex curved surface features; Voxelize the point cloud data, cluster the point cloud voxels based on the density features and curvature features of the point cloud voxels, and select a region growth seed point; Construct an adaptive growth criterion using the curvature difference and normal vector difference between any to-be-merged point cloud voxel and the region of any seed point, and adjust the merging threshold in the adaptive growth criterion using the fitting surface error of the to-be-merged point cloud voxel and the region of the seed point to perform region growth on the point cloud voxels; Fit the segmented point cloud data to geometric primitives and construct a building BIM model to complete the curtain wall simulation design; The adaptive growth criterion comprises: When the number of point cloud voxels in the region of the seed point is greater than or equal to a preset number, construct an adaptive threshold weight based on the fitting surface error of the to-be-merged point cloud voxel and the region of the seed point; otherwise, assign the adaptive threshold weight a value of 1; Use the adaptive threshold weight as the weight of the preset curvature feature difference threshold and the preset normal vector feature difference threshold, respectively; When the curvature difference is less than or equal to the weighted preset curvature feature difference threshold, and the normal vector difference is less than or equal to the weighted preset normal vector difference threshold, determine that the to-be-merged point cloud voxel meets the merging condition of the region of the seed point, and merge the to-be-merged point cloud voxel into the region; otherwise, do not perform merging. The calculation method of the fitting surface error of the to-be-merged point cloud voxel and the region of the seed point comprises: When the number of point cloud voxels in the region of the seed point is greater than or equal to a preset number, obtain the spatial distance between the to-be-merged point cloud voxel and the seed point; Use the spatial distance to determine the radius to make a circle with the seed point as the center, fit the surface of all point cloud voxels in the region of the seed point in the circle, calculate the geometric distance from the to-be-merged point cloud voxel to the surface as the fitting residual error of the to-be-merged point cloud voxel; Use the average fitting residual error of the to-be-merged point cloud voxel and its eight neighboring voxels and the surface as the fitting surface error of the to-be-merged point cloud voxel and the region of the seed point. 2.The BIM-based curtain wall simulation design method of claim 1, wherein, The distance measurement when clustering the point cloud voxels is positively correlated with the density feature difference and the curvature feature difference between the point cloud voxels. 3.The BIM-based curtain wall simulation design method of claim 2, wherein, The selection of the region growth seed point comprises: selecting three point cloud voxels between the maximum distance and the minimum distance from all point cloud voxels in each cluster to the cluster center point cloud voxel as the first, second and third quartiles, and the cluster center point cloud voxel as the region growth seed point.

4. The BIM-based curtain wall mockup design method of claim 1, wherein, The curvature difference is the difference between the curvature feature of the to-be-merged point cloud voxel and the average curvature feature of all point cloud voxels in the region of the seed point; the curvature feature of the point cloud voxel is calculated by eigenvalue decomposition of the covariance matrix of the centroid point cloud in the point cloud voxel.

5. The BIM-based curtain wall mockup design method of claim 4, wherein, The normal vector difference is the difference between the normal vector feature of the to-be-merged point cloud voxel and the average unit normal vector feature of all point cloud voxels in the region of the seed point; the normal vector feature of the point cloud voxel is determined by the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the centroid point cloud in the point cloud voxel.

6. The BIM-based curtain wall mockup design method of claim 1, wherein, The adaptive threshold weights are constructed based on the fitting surface error within the region where the voxels of the point cloud to be merged and the seed point are located, including: ,in Based on the voxels of the point cloud to be merged With the The adaptive threshold weights are constructed from the fitting surface errors within the regions of the seed points, where e is a natural constant. As a preset value, voxels of point cloud to be merged With the The fitting surface error within the region where each seed point is located.

7. The BIM-based curtain wall mockup design method of claim 6, wherein, The adaptive threshold weight is multiplied by the preset curvature feature difference threshold and the preset normal vector feature difference threshold respectively as the weight of the preset curvature feature difference threshold and the preset normal vector feature difference threshold. 8.A BIM-based curtain wall simulation design system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor implements the BIM-based curtain wall simulation design method in claim 1-7 when executing the computer program.

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