A building modeling method, device, equipment and storage medium

Abstract

The present disclosure provides a building modeling method, apparatus, device, and storage medium, relating to the field of modeling technology. The method includes: obtaining a target point cloud corresponding to a target building; wherein the target point cloud includes a plurality of target sampling points; sequentially determining a plurality of target fitting planes with the largest number of corresponding inliers based on the target point cloud, and determining a plurality of plane feature groups of the plurality of target fitting planes; wherein the number of inliers is the number of inliers, and an inlier is a sampling point whose distance from the plane is less than a preset distance threshold, and the inliers corresponding to different target fitting planes do not overlap; constructing a plurality of target plane models corresponding to the plurality of target fitting planes according to the plurality of plane feature groups, and constructing a target building model corresponding to the target building based on the plurality of target plane models. Thereby, the overall accuracy of the building model is improved, and subsequently, the building model can be adjusted with a finer granularity of planes, improving the flexibility of adjusting the model.

Description

Technical Field

[0001] The present disclosure relates to the field of modeling technologies, and particularly to a building modeling method, apparatus, device, and storage medium. Background Art

[0002] In the process of urban renewal, compared with the demolition and reconstruction plan, the renovation of old buildings provides a solution with lower cost and less impact. However, the architectural drawings and data of old buildings are often incomplete, and coupled with the natural changes of the buildings themselves over the years, it is relatively difficult to design and reconstruct relying solely on traditional drawings.

[0003] Currently, the overall modeling of old buildings can be performed based on the point cloud data of old buildings. However, the model obtained by this method corresponds to the whole building, and only the whole model can be adjusted, with poor flexibility in adjustment and low accuracy of the modeled model. Summary of the Invention

[0004] To solve the above technical problems, embodiments of the present disclosure provide a building modeling method, apparatus, device, and storage medium.

[0005] In a first aspect, the present disclosure provides a building modeling method, the method comprising:

[0006] Obtaining a target point cloud corresponding to a target building; wherein, the target point cloud includes a plurality of target sampling points;

[0007] Based on the target point cloud, successively determining a plurality of target fitting planes with the largest number of corresponding inliers, and determining a plurality of plane feature groups of the plurality of target fitting planes; wherein, the number of inliers is the number of inliers, and an inlier is a sampling point whose distance from a plane is less than a preset distance threshold, and the inliers corresponding to different target fitting planes do not overlap;

[0008] Constructing a plurality of target plane models corresponding to the plurality of target fitting planes according to the plurality of plane feature groups, and constructing a target building model corresponding to the target building based on the plurality of target plane models.

[0009] In a second aspect, the present disclosure provides a building modeling apparatus, the apparatus comprising:

[0010] An obtaining module, configured to obtain a target point cloud corresponding to a target building; wherein, the target point cloud includes a plurality of target sampling points;

[0011] A determination module, configured to sequentially determine a plurality of target fitting planes with the largest number of corresponding inliers based on the target point cloud, and determine a plurality of plane feature groups of the plurality of target fitting planes; wherein, the number of inliers is the number of inliers, and the inliers are sampling points whose distance from the plane is less than a preset distance threshold, and the inliers corresponding to different target fitting planes do not overlap;

[0012] A construction module, configured to construct a plurality of target plane models corresponding to the plurality of target fitting planes according to the plurality of plane feature groups, and construct a target building model corresponding to the target building based on the plurality of target plane models.

[0013] In a third aspect, the present disclosure provides a computer-readable storage medium, in which instructions are stored. When the instructions are run on a terminal device, the terminal device is caused to implement the above method.

[0014] In a fourth aspect, the present disclosure provides a building modeling device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above method is implemented.

[0015] In a fifth aspect, the present disclosure provides a computer program product, which includes a computer program / instructions. When the computer program / instructions are executed by a processor, the above method is implemented.

[0016] The technical solution provided by the embodiments of the present disclosure has at least the following advantages compared with the prior art:

[0017] An embodiment of the present disclosure provides a building modeling method, which obtains a target point cloud corresponding to a target building; wherein, the target point cloud includes a plurality of target sampling points; based on the target point cloud, a plurality of target fitting planes with the largest number of corresponding inliers are determined in sequence, and a plurality of plane feature groups of the plurality of target fitting planes are determined; wherein, the number of inliers is the number of inliers, and an inlier is a sampling point whose distance from the plane is less than a preset distance threshold, and the inliers corresponding to different target fitting planes do not overlap; according to the plurality of plane feature groups, a plurality of target plane models corresponding to the plurality of target fitting planes are constructed, and based on the plurality of target plane models, a target building model corresponding to the target building is constructed. In the above solution, after obtaining a plurality of target point clouds corresponding to the target building, a plurality of target fitting planes are determined according to the number of inliers determined based on the target point cloud, realizing the conversion from the overall granularity of the target building to the plane granularity, and modeling according to the platform feature groups of the target fitting planes to obtain the target building model constructed by the target plane model, realizing the construction of the building model with the plane as the basic unit. Taking the plane as the basic unit of the construction model improves the overall accuracy of the building model, and subsequently, the building model can be adjusted with a finer granularity of the plane, improving the flexibility of adjusting the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present disclosure and used together with the specification to explain the principles of the present disclosure.

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a schematic flowchart of a building modeling method provided by an embodiment of the present disclosure;

[0021] Figure 2 It is a schematic flowchart of another building modeling method provided by an embodiment of the present disclosure;

[0022] Figure 3 It is a schematic diagram of a target point cloud before removing outliers provided by an embodiment of the present disclosure;

[0023] Figure 4 It is a schematic diagram of a target point cloud after removing outliers provided by an embodiment of the present disclosure;

[0024] Figure 5 It is a schematic flowchart of another building modeling method provided by an embodiment of the present disclosure;

[0025] Figure 6 Schematic diagram of a target building model provided by an embodiment of the present disclosure;

[0026] Figure 7 Schematic diagram of a target plane model provided by an embodiment of the present disclosure;

[0027] Figure 8 Schematic diagram of another target plane model provided by an embodiment of the present disclosure;

[0028] Figure 9 Schematic diagram of the structure of a building modeling device provided by an embodiment of the present disclosure;

[0029] Figure 10 Schematic diagram of the structure of a building modeling device provided by an embodiment of the present disclosure. Detailed implementation manners

[0030] In order to more clearly understand the above objects, features and advantages of the present disclosure, the solutions of the present disclosure will be further described below. It should be noted that, without conflict, the embodiments of the present disclosure and the features in the embodiments may be combined with each other.

[0031] In the following description, many specific details are set forth to facilitate a thorough understanding of the present disclosure, but the present disclosure may be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only a part of the embodiments of the present disclosure, rather than all of the embodiments.

[0032] With the significant increase in the permanent population, the urban infrastructure has been significantly improved, which has also brought an increase in the number of old buildings, and the comprehensive management of old buildings has become increasingly important.

[0033] In the process of urban renewal, compared with the demolition and reconstruction plan, the renovation and renewal of old buildings provide a more cost-effective and less impactful alternative. However, the architectural drawings and data of old buildings are often incomplete, and coupled with the natural changes of the buildings themselves over the years, it is difficult to rely solely on traditional drawings for design and reconstruction.

[0034] Currently, the overall modeling of old buildings can be carried out based on the point cloud data of old buildings. However, the model obtained by this method corresponds to the whole building, and only the whole model can be adjusted, with poor flexibility in adjustment and low accuracy of the modeled model.

[0035] To solve at least one of the above technical problems, the building modeling method provided by an embodiment of the present disclosure will be described below. In the embodiment of the present disclosure, the building modeling method can be executed by an electronic device. The electronic device may include devices with communication functions such as tablet computers, desktop computers, laptop computers, etc., or may also include devices simulated by virtual machines or simulators.

[0036] Figure 1 The figure shows a schematic flowchart of a building modeling method provided by an embodiment of the present disclosure. As Figure 1 shown, the building modeling method may include the following steps.

[0037] Step 101: Obtain a target point cloud corresponding to a target building; wherein, the target point cloud includes multiple target sampling points.

[0038] Wherein, the target building may be a building to be reverse-built in a three-dimensional model. This embodiment places no restrictions on the target building. For example, the target building may be a building with incomplete drawing data, or the building may be an old building built before a preset building year. The target point cloud may be the point cloud based on which a target building model is constructed for the target building. The point cloud may be a set of points representing the surface characteristics of the target building. The target sampling point may be a sampling point in the target point cloud. The sampling point may be a point representing the surface characteristics of the target building.

[0039] In an embodiment of the present disclosure, the building modeling method may be implemented based on Python, C++, and the Point Cloud Library (PCL). Based on this building modeling method, reverse modeling of target buildings such as old buildings can be achieved. In this embodiment, the target point cloud corresponding to the target building may be pre-determined, and the building modeling device may obtain the target point cloud; or, the building modeling device may use Python to perform downsampling processing and filtering processing on the point cloud obtained by scanning the target building to obtain a target point cloud suitable for surface reconstruction of the target building.

[0040] Figure 2 The figure shows a schematic flowchart of another building modeling method provided by an embodiment of the present disclosure. As Figure 2 shown, in some embodiments of the present disclosure, the target point cloud may be obtained by performing downsampling processing on the original point cloud. Specifically, obtaining the target point cloud corresponding to the target building includes:

[0041] Step 201: Obtain the original point cloud obtained by scanning the target building, and determine the minimum bounding box of the original point cloud; wherein, the original point cloud includes multiple original sampling points.

[0042] Wherein, the original point cloud may be the point cloud obtained by performing three-dimensional point cloud scanning on the target building through a scanner or the like. The original sampling point may be a sampling point in the original point cloud. The minimum bounding box may be the cuboid with the smallest volume that completely contains the original point cloud.

[0043] In this embodiment, a scanner or the like can scan a target building to obtain the original point cloud corresponding to the target building. The building modeling device can acquire the original point cloud and construct the minimum bounding box corresponding to the original point cloud according to the maximum and minimum values of the coordinates of the original point cloud in the X, Y, and Z directions.

[0044] Step 202: Divide the minimum bounding box into multiple grids according to a preset grid side length.

[0045] Among them, the grid side length can be the side length of the grid. The grid can be the smallest processing unit divided from the minimum bounding box, and this grid can be understood as a voxel. The shape of this grid is not limited in this embodiment. For example, the shape of the grid can be a cube or a cuboid.

[0046] In this embodiment, the building modeling device can perform spatial segmentation on the minimum bounding box in the X, Y, and Z directions according to a preset grid side length to obtain multiple grids.

[0047] Step 203: Determine the target sampling points in each grid according to the original sampling points in each grid, and aggregate the target sampling points into a target point cloud.

[0048] Among them, the target sampling points can correspond to the grids one by one.

[0049] In the embodiments of the present disclosure, for each grid, the building modeling device can perform coordinate calculation according to the original sampling points in the grid to determine a target sampling point in the grid. The set composed of the multiple target sampling points corresponding to the multiple grids is the target point cloud. Thus, downsampling processing of the original point cloud is realized with the grid as the smallest processing unit, and the target sampling points corresponding to the grids one by one and characterizing the original sampling points in the grid are obtained, while retaining the characteristics of the original point cloud, the number of sampling points to be processed subsequently is reduced.

[0050] In some embodiments of the present disclosure, determining the target sampling points in each grid according to the original sampling points in each grid includes:

[0051] In the vertical and longitudinal directions respectively, standardize the side lengths of the minimum bounding box according to the grid side length to obtain the standard side length of the vertical axis and the standard side length of the longitudinal axis of the minimum bounding box; determine the index number corresponding to each original sampling point according to the standard coordinates, the standard side length of the vertical axis, and the standard side length of the longitudinal axis of each original sampling point; wherein, the standard coordinates of the original sampling point are determined according to the coordinates of the original sampling point, the minimum vertex coordinates of the minimum bounding box, and the grid side length; determine the index range corresponding to each grid according to the standard coordinates, the standard side length of the vertical axis, and the standard side length of the longitudinal axis of the vertices corresponding to each grid; wherein, the standard coordinates of the vertex are determined according to the coordinates of the vertex, the minimum vertex coordinates of the minimum bounding box, and the grid side length; among the original sampling points, determine the grid sampling points within each grid according to the index range and the index number; for each grid, calculate the coordinates of the target sampling point according to the coordinates of the grid sampling points within the grid.

[0052] Among them, the standard side length of the vertical axis can be the side length obtained by standardizing the side length of the minimum bounding box according to the grid side length in the vertical dimension, and this standard side length of the vertical axis can be understood as the number of grids in the vertical direction. The standard side length of the longitudinal axis can be the side length obtained by standardizing the side length of the minimum bounding box according to the grid side length in the longitudinal dimension, and this standard side length of the longitudinal axis can be understood as the number of grids in the longitudinal direction. The standardization process can be a length conversion process based on the corresponding grid side length as the basic unit.

[0053] The standard coordinates can be the coordinates obtained by standardizing the original coordinates according to the minimum vertex coordinates of the minimum bounding box and the grid side length. The minimum vertex coordinates can be the coordinates where the coordinate values of each dimension in the minimum bounding box are the minimum. The index number can be used to uniquely identify the original sampling point, and the position of the original sampling point can also be characterized by this index number. The index range can correspond to the grid one by one, and the index range can be the range of the index numbers of the original sampling points within the corresponding grid. The grid sampling point can be the original sampling point located inside the grid.

[0054] In this embodiment, the building modeling device can calculate the difference between the maximum value and the minimum value in the X, Y, and Z directions of the original sampling point cloud to obtain the side lengths of the minimum bounding box in the X, Y, and Z directions. Taking the grid as a cube as an example, further, the model modeling device can obtain the grid side length r of the grid, and divide the corresponding side lengths of the minimum bounding box by the grid side length r and round down in the X, Y, and Z directions respectively to obtain the standard side length of the horizontal axis, the standard side length of the vertical axis, and the standard side length of the longitudinal axis. Further, according to the minimum vertex coordinates of the minimum bounding box and the grid side length, the coordinates can be standardized to obtain the standard coordinates. The formula for standardizing the coordinates can be as follows:

[0055] ;

[0056] in, Indicates the minimum value of the horizontal coordinate of the minimum bounding box; Indicates the minimum vertical coordinate value of the minimum bounding box; Indicates the minimum vertical coordinate of the minimum bounding box; Represents the grid side length. For the coordinates of the original sampling point, Indicates the horizontal coordinate of the coordinate; Indicates the ordinate of the coordinate; Indicates the vertical coordinate of the coordinate; The horizontal coordinate of the coordinate in the standard coordinate system; The vertical coordinate of the coordinate in the standard coordinate system; Indicates the vertical coordinate of the coordinate in the standard coordinate. For the coordinates of the vertex, Indicates the horizontal coordinate of the coordinate; Indicates the ordinate of the coordinate; Indicates the vertical coordinate of the coordinate; The horizontal coordinate of the coordinate in the standard coordinate system; The vertical coordinate of the coordinate in the standard coordinate system; Indicates the vertical coordinate of the coordinate in standard coordinates.

[0057] For each original sampling point, the index number of the original sampling point It can be:

[0058] ;

[0059] in, Represents the horizontal coordinate of the original sampling point in the standard coordinates; Represents the ordinate of the original sampling point in standard coordinates; Represents the vertical coordinate of the original sampling point in standard coordinates, Indicates the standard side length of the vertical axis, Indicates the standard side length of the vertical axis.

[0060] In this embodiment, after determining the standard coordinates of the vertex, the building modeling device can convert the standard coordinates of the vertex into the index number of the vertex according to the standard coordinates of the vertex, the standard side length of the longitudinal axis, and the standard side length of the vertical axis, in the same manner as converting the standard coordinates of the original sampling point into the index number of the original sampling point, and calculate the index range corresponding to the grid according to the index numbers of the 8 vertices in the grid.

[0061] Furthermore, the building modeling device can sort the index numbers corresponding to the original sampling points in ascending order of numerical values. For each grid, the original sampling points whose index numbers are within the index range corresponding to the grid are determined as the grid sampling points corresponding to the grid, the centroid of the grid sampling points is calculated, and this centroid is used as the target sampling point corresponding to the grid.

[0062] In the above solution, the efficient determination of the original sampling points within the grid is achieved by determining the index numbers and index ranges. By performing downsampling of the original sampling points with voxels as the basic unit, redundant information and noise points are removed. On the basis of retaining the geometric features of the point cloud, the accuracy of the final modeling is ensured, and the number of sampling points to be processed is reduced, improving the modeling efficiency and solving the problems of excessive calculation time and overfitting. For example, in one embodiment, the number of original sampling points is nearly twenty million. After downsampling with a grid with a side length of 0.05, the number of target sampling points is reduced to more than one million, significantly reducing the number of sampling points.

[0063] In an optional implementation manner, in the Python environment, the building modeling device can import the Open3D and NumPy libraries. And create a point cloud reading function. This point cloud reading function reads the txt file storing the original point cloud through the loadtxt method in the NumPy library and automatically processes the delimiter of the coordinates in the txt file. After reading the txt file, the data stored in the file is converted into the target point cloud processed by the Open3D library, and the target point cloud is saved as a ply file through the point cloud writing function. Among them, the paths of the txt file and the ply file can be pre-configured. Based on the point cloud reading function, the format conversion of the file and the saving of the file can be achieved.

[0064] After obtaining the ply file, perform voxel downsampling processing on the target point cloud stored in the file. Re-introducing the Open3D and NumPy libraries, the building modeling device can read the ply file through the point cloud reading function. Downsample the target point cloud through the voxel downsampling function, and the size of the voxel can be set to 0.05 meters. Through voxel downsampling, the number of sampling points for subsequent processing can be greatly reduced, improving the efficiency of subsequent processing. After completing the downsampling, save the sampling points obtained by downsampling as a pcd format through the point cloud writing function.

[0065] When measuring a target building by three-dimensional laser scanning, measurement noise will inevitably be generated. The error caused by the measurement noise will produce sparse outlier points, which will lead to complex operations and incorrect numerical values when determining the local features of the point cloud (for example, determining the normal vector), and further lead to the failure of surface modeling.

[0066] In some embodiments of the present disclosure, after downsampling the original point cloud, in order to filter out the outliers in the target point cloud, the target point cloud obtained by downsampling can also be filtered. In an alternative embodiment, after obtaining the target point cloud, the building modeling method further includes:

[0067] Each target sampling point is respectively determined as a sampling point to be processed, and the grid whose distance from the grid where the sampling point to be processed is located is less than the preset grid distance is determined as the associated grid; the number of target sampling points in the associated grid is determined to obtain the associated number. If the associated number is less than the first number threshold, the sampling point to be processed is deleted; if the associated number is not less than the first number threshold, the sampling point to be processed is retained, or the target sampling points in the key grid whose distance from the sampling point to be processed is less than the preset sampling point distance are determined as associated sampling points. If the number of the associated sampling points is less than the second number threshold, the sampling point to be processed is deleted; if the number of the associated sampling points is not less than the second number threshold, the sampling point to be processed is retained.

[0068] In another alternative embodiment, since the outliers in the point cloud are usually sparsely distributed in space, the building modeling device can identify the outliers by identifying the low-density parts in the target point cloud, preset a density threshold, and delete the target sampling points whose density values are lower than this density threshold. Specifically, for each target sampling point, statistical analysis is performed on the neighborhood of the target sampling point, and the average distance between each target sampling point and its nearest k points is calculated. This average distance characterizes the density of the neighborhood point cloud corresponding to the target sampling point, and k can be a set positive integer. Assume that the average distance follows a Gaussian distribution, which is described by the mean μ and the standard deviation σ. Preset the standard deviation multiple std and the positive integer k. If the average distance from the target sampling point to its neighboring k other target sampling points is within the interval (μ - σ·std, μ + σ·std), the target sampling point is retained; otherwise, it means that the target sampling point is an outlier and is deleted.

[0069] In an optional implementation manner, the Open3D library is pre-imported in the Python environment. The model modeling device can load a file recording the target point cloud through a point cloud reading function and assign the target point cloud to a point cloud variable pcd. By printing the point cloud variable pcd, the number of sampling points in the current target point cloud can be viewed. Subsequently, statistical filtering processing is implemented through a discrete point removal function. For this discrete point removal function, the standard deviation multiple and the number of closest points need to be set. The number of closest points defines the number of nearest points considered when calculating the average distance corresponding to each target sampling point, and this number of closest points can be set to 150. The standard deviation multiple sets the standard deviation threshold for determining whether the target sampling point is an outlier, and this standard deviation multiple can be set to 2. Further, by comparing the average distance between the target sampling point and other target sampling points in its neighborhood, outliers are identified and removed according to the standard deviation of the Gaussian distribution.

[0070] After the filtering of the point cloud is completed, the building modeling device can assign the point cloud after removing outliers to a new point cloud variable, save the new point cloud variable in the pcd format through a point cloud writing function, and use the new point cloud variable as the current target point cloud. The data in the pcd format can be used to observe the target point cloud through a CloudCompare tool. Figure 3 This is a schematic diagram of the target point cloud before removing outliers provided by an embodiment of the present disclosure. Figure 4 This is a schematic diagram of the target point cloud after removing outliers provided by an embodiment of the present disclosure. By Figure 3 and Figure 4 comparison, it can be determined that the outliers have been effectively removed. By removing outliers, the efficiency of subsequent work such as surface reconstruction can be effectively improved.

[0071] In the above solution, the sampling points are filtered, the outliers in the point cloud are automatically removed, the effective information in the point cloud is retained, the data quality and data accuracy of the retained point cloud are improved, and a basis is created for accurately establishing a target plane model subsequently.

[0072] Step 102: Based on the target point cloud, successively determine multiple target fitting planes with the largest number of corresponding inliers, and determine multiple plane feature groups of the multiple target fitting planes; where the number of inliers is the number of inliers, and an inlier is a sampling point whose distance from the plane is less than a preset distance threshold, and the inliers corresponding to different target fitting planes do not overlap.

[0073] Among them, the number of inliers corresponding to the target fitting plane can be the number of target sampling points whose distance from the target fitting plane is less than a preset distance threshold. The preset distance threshold can be a distance threshold preset for screening inliers, and this embodiment does not limit this preset distance threshold. The target fitting plane can be a fitting plane determined based on the positional relationship between the plane and the target sampling points in the target point cloud.

[0074] A plane feature group can be a set of eigenvalues characterizing the features of the target fitting plane. This embodiment does not limit this plane feature group. In some embodiments of the present disclosure, the plane feature group includes the plane boundary, plane normal vector, and reference point of the target fitting plane. The plane boundary can be used to determine the contour of the target fitting plane, and the plane normal vector can be used to determine the direction of the target fitting plane. Through this plane normal vector, the possible inclination problem of the target building can be characterized. The reference point can be a point passed by the target fitting plane, and this embodiment does not limit this reference point. For example, this reference point can be the intersection point of the target fitting plane and the plane normal vector.

[0075] In the embodiments of the present disclosure, the building modeling device can perform plane-based point cloud segmentation and extraction of the plane feature group on the target point cloud through C++ and a point cloud data processing library. Specifically, since the surface of the target building can be approximately considered as a plane, in this embodiment, the building modeling device can randomly select multiple target sampling points in the target point cloud to fit a fitting plane, and count the number of inliers that are inliers of this fitting plane in the target point cloud, determine the fitting plane corresponding to the largest number of current inliers as the target fitting plane, delete the inliers corresponding to this target fitting plane, and return to re-select target sampling points for iterative cycling to determine the target fitting plane until a preset end condition is met.

[0076] After determining the target fitting plane, the building modeling device can determine the plane normal vector and reference point of this target fitting plane, and calculate the projection points of the inliers on this target fitting plane through a boundary algorithm to determine the plane boundary of this target fitting plane, where the boundary algorithm can be an algorithm for determining the boundary formed by sampling points, and this embodiment does not limit this boundary algorithm. For example, this boundary algorithm can be a convex hull algorithm. And use the plane normal vector, reference point, and plane boundary as the plane feature group of the target fitting plane.

[0077] In some embodiments of the present disclosure, the plane normal vector may be a normal vector determined based on the inner points of the target fitting plane. Determining the plane normal vector through the inner points can avoid deviations caused by local points in the process of modeling the plane, thereby improving the fitting accuracy of the target plane model. The reference point may be a point determined based on the inner points of the target fitting plane. Specifically, the building construction device may use the least squares method to determine the plane normal vector for the inner points corresponding to the target fitting plane. In the least squares method, the building modeling device may construct a covariance matrix based on the inner points corresponding to the target fitting plane, perform eigenvalue decomposition on the covariance matrix, and obtain the plane normal vector of the target fitting plane. In addition, the coordinate average of the inner points corresponding to the target fitting plane is calculated to obtain the inner point centroid, and the inner point centroid is used as the reference point of the target fitting plane.

[0078] In one optional embodiment, the process of determining the plane normal vector and the reference point includes performing eigenvalue decomposition of the covariance matrix corresponding to the interior point using an eigenvalue decomposition function. In this process, the eigenvectors of the covariance matrix represent the principal axis direction, and the eigenvector corresponding to the smallest eigenvalue represents the plane normal vector. If the calculated plane normal vector does not point to the positive Z axis, the plane normal vector needs to be reversed to ensure its correctness.

[0079] Figure 5 A flow chart of another building modeling method provided by the embodiment of the present disclosure is shown as follows: Figure 5 As shown, in some embodiments of the present disclosure, multiple target fitting planes corresponding to the largest number of inliers are sequentially determined based on the target point cloud, including:

[0080] Step 501: determine the target point cloud as an intermediate point cloud; wherein the intermediate point cloud includes multiple intermediate sampling points.

[0081] The intermediate point cloud may be a point cloud determined in an intermediate process of determining the target fitting plane based on the target point cloud, and the intermediate sampling point may be a sampling point in the intermediate point cloud.

[0082] In this embodiment, the building modeling apparatus may determine the target point cloud as an intermediate point cloud, and determine the target acquisition point in the target point cloud as an intermediate acquisition point.

[0083] Step 502: Select multiple groups of fitting sampling points from the intermediate sampling points, and obtain multiple fitting planes by fitting the multiple groups of fitting sampling points.

[0084] Among them, a set of fitting sampling points can be composed of multiple intermediate sampling points. The number of intermediate sampling points within a set of fitting sampling points is not limited in this embodiment. For example, it can be 3. Each set of fitting sampling points corresponds to a fitting plane one by one. The number of sets of fitting sampling points is not limited in this embodiment, and this number can be set according to user requirements, etc. By setting an appropriate upper limit of the number of sets, the accuracy of the target fitting plane can be improved while reducing unnecessary calculations and increasing the determination speed of the plane. By setting an appropriate number of sets, a target fitting plane that conforms to the actual situation can be obtained while reducing the amount of computation.

[0085] In this embodiment, a preset number of intermediate sampling points are randomly selected from the intermediate sampling points, and these preset number of intermediate sampling points are used as a set of fitting sampling points. Plane fitting is performed for each set of fitting sampling points to obtain the fitting plane corresponding to this set of fitting sampling points.

[0086] Step 503, determine the number of inliers corresponding to multiple fitting planes.

[0087] In this embodiment, there are multiple fitting planes corresponding to multiple sets of fitting sampling points. For each fitting plane, the intermediate sampling points whose distance from this fitting plane is less than a preset distance threshold are determined as the inliers corresponding to this fitting plane, and the number of these inliers is counted to obtain the number of inliers corresponding to this fitting plane.

[0088] Step 504, determine the fitting plane corresponding to the maximum value of the number of inliers as the target fitting plane.

[0089] In this embodiment, after determining the number of inliers corresponding to multiple fitting planes, the fitting plane corresponding to the maximum value among the multiple numbers of inliers is determined as the target fitting plane corresponding to the current intermediate point cloud.

[0090] In the embodiments of the present disclosure, the building modeling device can randomly select multiple intermediate sampling points from the intermediate point cloud as a set of fitting sampling points, and through the corresponding fitting plane fitted by this set of fitting sampling points, for this fitting plane, determine the intermediate sampling points whose distance from this fitting plane is less than a preset distance threshold, and use this intermediate sampling point as the inlier of the fitting plane, and count the number of inliers of this inlier. Multiple sets of fitting sampling points are determined for the building modeling device. During the determination process of each iteration, if the current fitting plane has a larger number of inliers than the fitting plane corresponding to the previous iteration, then this current fitting plane is determined as the current optimal plane. This process is repeatedly executed until the number of iterations reaches the preset number of sets of fitting sampling points, and the optimal plane at this time is determined as the target fitting plane corresponding to this intermediate point cloud.

[0091] In the above solution, the plane segmentation of the intermediate point cloud is implemented based on the Random Sample Consensus (RANSAC) algorithm. This algorithm can effectively distinguish noise and outliers, and has good high robustness to noise and outliers. By randomly determining the fitting sampling points and performing iterative optimization, it can extract the best target fitting plane with the most inliers in the intermediate point cloud, achieve convergence quickly while better processing the intermediate point cloud, and improve the accuracy of the plane result.

[0092] Step 505: Delete the inliers corresponding to the target fitting plane in the intermediate point cloud to obtain a new intermediate point cloud, and return to determine the target fitting plane corresponding to the new intermediate point cloud until the preset end condition is met.

[0093] Among them, the preset end condition can be a condition for stopping the iteration to determine the target fitting plane set in advance, and this embodiment does not limit this preset end condition. For example, the preset end condition can be that the number of target fitting planes reaches a preset plane number, or the preset end condition can be that the number of sampling points in the new intermediate point cloud is less than a preset sampling point number threshold.

[0094] In this embodiment, after determining the target fitting plane corresponding to the current intermediate point cloud, delete the inliers of this target fitting plane in the current intermediate point cloud to obtain a new intermediate point cloud, and return to Step 502 based on this new intermediate point cloud to determine the target fitting plane corresponding to this new intermediate point cloud until the preset end condition is met.

[0095] In the above solution, the target fitting plane is first determined by random sampling, and then the plane normal vector and other features of the target fitting plane are determined by the least squares method, which not only improves the accuracy of the simulation modeling of the exterior wall of the target building, but also reduces the difficulty and cost of modeling.

[0096] Step 103: Construct multiple target plane models corresponding to multiple target fitting planes according to multiple plane feature groups, and construct a target building model corresponding to the target building based on the multiple target plane models.

[0097] Among them, the target plane model can be a three-dimensional virtual model corresponding to the target fitting plane. The target building model can be a three-dimensional virtual model of the target building, and the smallest constituent unit of this target building model can be the target plane model. This target building model can be a Building Information Modeling (BIM) of the target building.

[0098] In the embodiments of the present disclosure, after determining multiple plane feature groups, the corresponding target plane models can be constructed according to the plane feature groups automatically or manually, and the target building model formed by splicing the target plane models can be determined. Figure 6A schematic diagram of a target building model provided by an embodiment of the present disclosure. As Figure 6 shown, a plurality of target plane models are spliced to generate the target building model.

[0099] In some embodiments of the present disclosure, constructing a plurality of target plane models corresponding to a plurality of target fitting planes according to a plurality of plane feature groups includes: displaying the plane feature group corresponding to each target fitting plane on a preset interface; in response to a plane construction operation performed by the user based on the plane feature group, constructing a target plane model.

[0100] Among them, the preset interface may be a pre-set display interface. The plane construction operation may be an operation in which the user constructs a target plane model according to the plane feature group through a program.

[0101] In this embodiment, after determining the plane feature group, the building modeling device can display the plane feature group to the user in a visual manner on the preset interface. After seeing the plane feature group, the user performs a plane construction operation according to the plane feature group, and the building modeling device constructs a corresponding target plane in response to the plane construction operation. Figure 7 A schematic diagram of a target plane model provided by an embodiment of the present disclosure, Figure 8 Another schematic diagram of a target plane model provided by an embodiment of the present disclosure. As Figure 7 shown, the target plane model corresponds to the front of the target building. As Figure 8 shown, the target plane model corresponds to the back of the target building.

[0102] In the above solution, the plane feature group is determined by segmenting the plane from the point cloud, and the plane is reconstructed manually according to the plane feature group. Compared with the modeling method in the related art of directly modeling the model corresponding to the whole target building according to the point cloud, the target building is modeled with a finer granularity, and the accuracy of the finally determined target building model is higher. Moreover, it avoids directly constructing the plane according to a large amount of point cloud, and improves the construction efficiency of the target building model.

[0103] In the building modeling method provided by an embodiment of the present disclosure, a target point cloud corresponding to a target building is obtained; wherein, the target point cloud includes a plurality of target sampling points; based on the target point cloud, a plurality of target fitting planes with the largest number of corresponding inlier points are sequentially determined, and a plurality of plane feature groups of the plurality of target fitting planes are determined; wherein, the number of inlier points is the number of inlier points, and an inlier point is a sampling point whose distance from the plane is less than a preset distance threshold, and the inlier points corresponding to different target fitting planes do not overlap; according to the plurality of plane feature groups, a plurality of target plane models corresponding to the plurality of target fitting planes are constructed, and a target building model corresponding to the target building is constructed based on the plurality of target plane models. In the above solution, after obtaining the plurality of target point clouds corresponding to the target building, a plurality of target fitting planes are determined according to the number of inlier points determined based on the target point cloud, realizing the conversion of the granularity of the entire target building to the plane granularity, and modeling according to the platform feature group of the target fitting plane to obtain the target building model constructed by the target plane model, realizing the construction of the building model with the plane as the basic unit. Taking the plane as the basic unit for constructing the model improves the overall accuracy of the building model, and subsequently, the building model can be adjusted with a finer granularity of the plane, improving the flexibility of adjusting the model.

[0104] In some embodiments of the present disclosure, the building modeling method further includes: performing surface reconstruction processing on the target point cloud through a surface reconstruction algorithm to obtain a reference building model; and comparing and displaying the reference building model beside the construction area of the target building model.

[0105] Wherein, the surface reconstruction algorithm can be an algorithm for reconstructing based on the target point cloud with the entire target building as the smallest construction basic unit. This embodiment does not limit this surface reconstruction algorithm. For example, the surface reconstruction algorithm can be a sphere rotation algorithm. The reference model building model can be a three-dimensional virtual model used as a reference during the process of the user constructing the target building model. The construction area can be the area for constructing the target building model.

[0106] In this embodiment, the building modeling device can perform surface reconstruction processing on the target point cloud through Python to obtain a reference building model generated by reverse modeling of the target building. Specifically, the building modeling device can import the Open3D library, read the target point cloud through the point cloud reading function, and assign the target point cloud to the point cloud variable pcd, and print the point cloud variable pcd to output the number of points in the target point cloud. For this target point cloud, normal calculation is performed through the normal vector estimation function, and adjacent points are found through the hybrid search of the k-dimensional tree (kd-tree). The search radius is set to 0.5 meters, and only 300 sampling points within the neighborhood are considered. After the normal calculation is completed, a radius list is defined. For example, this radius list can be [0.5, 0.1, 0.2, 0.5]. The numbers in the radius list represent the radii of the three-dimensional spheres used for surface reconstruction, and the unit is the same as that of the target building model, which can be meters. Then, the function of creating a triangular mesh based on the point cloud's sphere rotation algorithm is used to create a triangular mesh using the sphere rotation algorithm to achieve surface reconstruction, obtaining a reference building model, and using the model writing function to save this reference building model as an obj format that can be imported into Revit.

[0107] Furthermore, the building modeling device can read this reference building model and display it beside the construction area of the target building model. During the process of constructing the target building model, the user can compare the reference building model with the unfinished target building model. This enables the user to understand whether there are mistakes in their planar construction operations that lead to errors in the construction of the target building model.

[0108] In some embodiments of the present disclosure, the building modeling method includes: preprocessing the original point cloud through Python to obtain the target point cloud. This preprocessing includes downsampling processing and filtering processing, and redundant sampling points in the target point cloud can be removed through this preprocessing. Performing point cloud segmentation on the target point cloud through C++ and the point cloud data processing library to obtain the target fitting plane, and determining the plane boundary corresponding to the target fitting plane. Exporting the inliers and plane boundary corresponding to the target fitting plane through C++. Performing least squares method calculation on the inliers through Python to determine the plane normal vector corresponding to the inliers, calculating the average coordinates of the inliers to obtain the corresponding reference point. Performing surface reconstruction based on the plane normal vector, reference point, and plane boundary to obtain the target plane model, and this target plane model can be imported into Revit software, and reverse modeling of the target building can be achieved based on this target plane model in Revit software to generate the target building model.

[0109] The building modeling method provided by the embodiments of the present disclosure realizes the automatic progress of the main process of building a building model, determines the plane feature group corresponding to the plane, and realizes the reverse modeling of the target building model based on the plane feature group. Compared with the reverse modeling method of directly modeling according to the point cloud, the modeling error caused by the sampling point error is reduced, and the modeling efficiency is improved.

[0110] Based on the above method embodiments, the present disclosure further provides a building modeling device. Refer to Figure 9 , which is a schematic structural diagram of a building modeling device provided by an embodiment of the present disclosure. The device includes:

[0111] An acquisition module 901, configured to acquire a target point cloud corresponding to a target building; wherein, the target point cloud includes multiple target sampling points;

[0112] A determination module 902, configured to sequentially determine multiple target fitting planes with the largest number of corresponding inliers based on the target point cloud, and determine multiple plane feature groups of the multiple target fitting planes; wherein, the number of inliers is the number of inliers, and the inliers are sampling points whose distance from the plane is less than a preset distance threshold, and the inliers corresponding to different target fitting planes do not overlap;

[0113] A construction module 903, configured to construct multiple target plane models corresponding to the multiple target fitting planes according to the multiple plane feature groups, and construct a target building model corresponding to the target building based on the multiple target plane models.

[0114] In an optional implementation manner, the acquisition module 901 includes:

[0115] An acquisition sub-module, configured to acquire an original point cloud obtained by scanning a target building, and determine the minimum bounding box of the original point cloud; wherein, the original point cloud includes multiple original sampling points;

[0116] A division sub-module, configured to divide the minimum bounding box into multiple grids according to a preset grid side length;

[0117] A determination sub-module, configured to determine target sampling points in each grid according to the original sampling points in each grid, and aggregate the target sampling points into the target point cloud.

[0118] In an optional implementation manner, the determination of the target sampling points in each grid according to the original sampling points in each grid includes:

[0119] Respectively in the longitudinal and vertical directions, standardize the side lengths of the minimum bounding box according to the grid side length to obtain the longitudinal standard side length and the vertical standard side length of the minimum bounding box;

[0120] Determine the index number corresponding to each of the original sampling points according to the standard coordinates of each of the original sampling points, the standard side length of the vertical axis, and the standard side length of the vertical axis; wherein, the standard coordinates of the original sampling points are determined according to the coordinates of the original sampling points, the minimum vertex coordinates of the minimum bounding box, and the grid side length;

[0121] Determine the index range corresponding to each of the grids according to the standard coordinates of the vertices corresponding to each of the grids, the standard side length of the vertical axis, and the standard side length of the vertical axis; wherein, the standard coordinates of the vertices are determined according to the coordinates of the vertices, the minimum vertex coordinates of the minimum bounding box, and the grid side length;

[0122] Among the original sampling points, determine the grid sampling points within each of the grids according to the index range and the index number;

[0123] For each of the grids, calculate the coordinates of the target sampling points according to the coordinates of the grid sampling points within the grid.

[0124] In an optional implementation manner, the determining a plurality of target fitting planes corresponding to the most inlier points in sequence based on the target point cloud includes:

[0125] Determine the target point cloud as the intermediate point cloud; wherein, the intermediate point cloud includes a plurality of intermediate sampling points;

[0126] Select multiple groups of fitting sampling points from the intermediate sampling points, and obtain a plurality of fitting planes by fitting according to the multiple groups of fitting sampling points;

[0127] Determine the number of inlier points corresponding to the plurality of fitting planes;

[0128] Determine the fitting plane corresponding to the maximum value of the number of inlier points as the target fitting plane;

[0129] Delete the inlier points corresponding to the target fitting plane in the intermediate point cloud, obtain a new intermediate point cloud, and return to determine the target fitting plane corresponding to the new intermediate point cloud until a preset end condition is satisfied.

[0130] In an optional implementation manner, the plane feature group includes the plane boundary, plane normal vector, and reference point of the target fitting plane.

[0131] In an optional implementation manner, the constructing a plurality of target plane models corresponding to the plurality of target fitting planes according to the plurality of plane feature groups includes:

[0132] Display the plane feature group corresponding to each of the target fitting planes on a preset interface;

[0133] In response to a plane construction operation performed by the user based on the plane feature group, construct the target plane model.

[0134] In an optional implementation manner, the building modeling device further includes:

[0135] A reconstruction module, configured to perform surface reconstruction processing on the target point cloud through a surface reconstruction algorithm to obtain a reference building model;

[0136] A display module, configured to comparatively display the reference building model beside the construction area of the target building model.

[0137] It should be noted that, Figure 9 The shown building modeling device can execute each step in the above-mentioned building modeling method embodiments, and implement each process and effect in the above-mentioned building modeling method embodiments, which will not be elaborated here.

[0138] In addition to the above methods and devices, the embodiments of the present disclosure also provide a computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a terminal device, the terminal device is enabled to implement the building modeling method described in the embodiments of the present disclosure.

[0139] The embodiments of the present disclosure also provide a computer program product. The computer program product includes computer programs / instructions. When the computer programs / instructions are executed by a processor, the building modeling method described in the embodiments of the present disclosure is implemented.

[0140] In addition, the embodiments of the present disclosure also provide a building modeling device. Referring to Figure 10 shown, it may include:

[0141] A processor 1001, a memory 1002, an input device 1003, and an output device 1004. The number of processors 1001 in the building modeling device may be one or more, Figure 10 Taking one processor as an example. In some embodiments of the present disclosure, the processor 1001, the memory 1002, the input device 1003, and the output device 1004 may be connected through a bus or other means. Among them, Figure 10 Taking the connection through a bus as an example.

[0142] The memory 1002 can be used to store software programs and modules. The processor 1001 executes various functional applications and data processing of the building modeling device by running the software programs and modules stored in the memory 1002. The memory 1002 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc. In addition, the memory 1002 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices. The input device 1003 can be used to receive input digital or character information, and generate signal inputs related to the user settings and function controls of the building modeling device.

[0143] Specifically in this embodiment, the processor 1001 loads the executable files corresponding to the processes of one or more application programs into the memory 1002 according to the following instructions, and the processor 1001 runs the application programs stored in the memory such that the various functions of the above-mentioned building modeling device are implemented.

[0144] It should be noted that in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without more limitations, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0145] The above are only specific embodiments of the present disclosure, enabling those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments described herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A building modeling method, characterized in that: The method comprises: Obtaining a target point cloud corresponding to a target building; wherein the target point cloud includes multiple target sampling points; Determining, based on the target point cloud, a plurality of target fitting planes corresponding to the largest number of inliers, and determining a plurality of plane feature groups for the plurality of target fitting planes; wherein the number of inliers is the number of inliers, each inlier being a sampling point whose distance from the plane is less than a preset distance threshold, and the inliers corresponding to different target fitting planes are non-repeated; Constructing a plurality of target plane models corresponding to the plurality of target fitting planes according to the plurality of plane feature groups, and constructing a target building model corresponding to the target building based on the plurality of target plane models; The method of sequentially determining a plurality of target fitting planes corresponding to the largest number of inliers based on the target point cloud includes: determining the target point cloud as an intermediate point cloud; wherein the intermediate point cloud contains a plurality of intermediate sampling points; selecting a plurality of groups of fitting sampling points from the intermediate sampling points, and obtaining a plurality of fitting planes by fitting the plurality of groups of fitting sampling points; determining a plurality of numbers of inliers corresponding to the plurality of fitting planes; determining a fitting plane corresponding to a maximum number of inliers as the target fitting plane; deleting the inliers corresponding to the target fitting plane in the intermediate point cloud to obtain a new intermediate point cloud, and returning to determine the target fitting plane corresponding to the new intermediate point cloud, until a preset end condition is met.

2. The method according to claim 1, characterized in that The step of obtaining a target point cloud corresponding to a target building includes: Obtaining an original point cloud obtained by scanning the target building, and determining a minimum bounding box of the original point cloud; wherein the original point cloud includes a plurality of original sampling points; Dividing the minimum bounding box into a plurality of grids according to a preset grid side length; Target sampling points in each grid are determined according to the original sampling points in each grid, and the target sampling points are aggregated into the target point cloud.

3. The method according to claim 2, characterized in that The determining of the target sampling points in each grid according to the original sampling points in each grid includes: Standardizing the side lengths of the minimum bounding box in the longitudinal direction and the vertical direction according to the grid side lengths to obtain the longitudinal axis standard side length and the vertical axis standard side length of the minimum bounding box; Determining an index number corresponding to each original sampling point according to the standard coordinates of each original sampling point, the standard side length of the longitudinal axis, and the standard side length of the vertical axis; wherein the standard coordinates of the original sampling point are determined according to the coordinates of the original sampling point, the minimum vertex coordinates of the minimum bounding box, and the grid side length; Determine the index range corresponding to each grid according to the standard coordinates of the vertices corresponding to each grid, the standard side length of the longitudinal axis, and the standard side length of the vertical axis; wherein the standard coordinates of the vertex are determined according to the coordinates of the vertex, the minimum vertex coordinates of the minimum bounding box, and the grid side length; In the original sampling points, determining the grid sampling points in each grid according to the index range and the index number; For each of the grids, the coordinates of the target sampling point are calculated according to the coordinates of the grid sampling points within the grid.

4. The method according to claim 1, wherein The plane feature group includes a plane boundary, a plane normal vector, and a reference point of the target fitting plane.

5. The method according to claim 1, wherein The step of constructing a plurality of target plane models corresponding to the plurality of target fitting planes according to the plurality of plane feature groups includes: Displaying the plane feature group corresponding to each target fitting plane on a preset interface; In response to a plane construction operation performed by a user based on the plane feature group, the target plane model is constructed.

6. The method according to claim 5, characterized in that The method further comprises: Performing surface reconstruction processing on the target point cloud using a surface reconstruction algorithm to obtain a reference building model; The reference building model is displayed for comparison next to the construction area of the target building model.

7. A building modeling device, characterized in that: The device comprises: An acquisition module is used to acquire a target point cloud corresponding to a target building; wherein the target point cloud includes a plurality of target sampling points; a determination module, configured to sequentially determine, based on the target point cloud, a plurality of target fitting planes corresponding to the largest number of inliers, and determine a plurality of plane feature groups for the plurality of target fitting planes; wherein the number of inliers is the number of inliers, each inlier being a sampling point whose distance from the plane is less than a preset distance threshold, and wherein the inliers corresponding to different target fitting planes are non-repeated; a construction module, configured to construct a plurality of target plane models corresponding to the plurality of target fitting planes according to the plurality of plane feature groups, and to construct a target building model corresponding to the target building based on the plurality of target plane models; The method of sequentially determining a plurality of target fitting planes corresponding to the largest number of inliers based on the target point cloud includes: determining the target point cloud as an intermediate point cloud; wherein the intermediate point cloud contains a plurality of intermediate sampling points; selecting a plurality of groups of fitting sampling points from the intermediate sampling points, and obtaining a plurality of fitting planes by fitting the plurality of groups of fitting sampling points; determining a plurality of numbers of inliers corresponding to the plurality of fitting planes; determining a fitting plane corresponding to a maximum number of inliers as the target fitting plane; deleting the inliers corresponding to the target fitting plane in the intermediate point cloud to obtain a new intermediate point cloud, and returning to determine the target fitting plane corresponding to the new intermediate point cloud, until a preset end condition is met.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed on a terminal device, the terminal device implements the method according to any one of claims 1 to 6.

9. A building modeling device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

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