Method for constructing cad model based on point cloud data sub-domain reconstruction

Abstract

The application relates to a CAD model construction method based on point cloud data domain reconstruction, and comprises the following steps: for an ideal CAD model of a model to be constructed, prior information of the ideal CAD model is extracted; surface point cloud data of an actual manufacturing workpiece is obtained through a three-dimensional scanning device; the three-dimensional point cloud data is matched with the surface point cloud data of the ideal CAD model; the actual point cloud data is divided into point cloud data belonging to different characteristics according to the curvature variation of the point cloud data; the point cloud data belonging to different regions is extracted through point feature template matching; the point cloud data in different regions in step S4 is fitted into a characteristic surface by using a NURBS surface; the characteristic surfaces are connected and reorganized according to the adjacent connection relationship of the same characteristics of the ideal CAD model, so that a CAD model based on three-dimensional actual point cloud data is obtained. The application combines the prior information of the ideal CAD model to improve the modeling efficiency and precision.

Description

Technical Field

[0001] This invention belongs to the field of computer-aided design (CAD), specifically a method for constructing CAD models based on the regional reconstruction of point cloud data from 3D scanning. Background Technology

[0002] In the field of industrial manufacturing, CAD models are the most commonly used and universal models. To create a CAD model that reflects the actual shape of a workpiece, a significant amount of time and effort is usually required based on measurement data. Manual modeling techniques are often used to create CAD models, but the accuracy of these techniques is difficult to guarantee. Summary of the Invention

[0003] To address the aforementioned technical problems, the present invention aims to provide a CAD model construction method based on the domain-specific reconstruction of 3D scanned point cloud data, and to improve modeling efficiency and accuracy by incorporating prior information from an ideal CAD model. The method involves dividing the point cloud data acquired by a 3D scanning device into multiple regions, reconstructing the point cloud data for each region, and converting it into NURBS surfaces in the CAD model. The reconstructed CAD models from each region are then merged and recombined to create a complete CAD model. This process incorporates prior information from an ideal CAD model to improve modeling efficiency and accuracy.

[0004] The technical solution adopted by this invention to achieve the above objectives is: a CAD model construction method based on point cloud data domain reconstruction, comprising the following steps:

[0005] Step S1: For the ideal CAD model to be constructed, extract the prior information of the ideal CAD model, obtain the point feature template by uniform grid sampling on the surface of the ideal CAD model, and discretize the ideal CAD model into point cloud data;

[0006] Step S2: Obtain the surface point cloud data of the actual manufactured workpiece through a 3D scanning device, perform data preprocessing operations, and obtain 3D point cloud data;

[0007] Step S3: Register the 3D point cloud data obtained in step S2 with the surface point cloud data of the ideal CAD model to obtain the actual point cloud data located in the same coordinate system as the ideal CAD model;

[0008] Step S4: Use the region growing method to divide the actual point cloud data obtained in S3 into point cloud data belonging to different features according to the curvature change of the point cloud data; according to the position in the coordinate system, match the point cloud data belonging to different regions with the point feature template of the ideal CAD model in S1, and use the point feature template of the ideal CAD model to extract the point cloud data belonging to different regions.

[0009] Step S5: Use NURBS surfaces to fit the point cloud data of different regions in step S4 into feature surfaces;

[0010] Step S6: Connect and reorganize the feature surfaces obtained by fitting the point cloud data in step S5 according to the adjacent connection relationship of the same features in the ideal CAD model in S1 to obtain a CAD model based on the actual 3D point cloud data.

[0011] Step S1 includes the following steps:

[0012] Step S1.1: Extract prior information of the ideal CAD model: Identify the geometric features of the ideal CAD model using a feature recognition method based on geometric information, and record the position, geometric attributes, and Brep boundary of each feature;

[0013] Step S1.2: Uniformly sample grid points on the surface of the ideal CAD model according to different features to obtain multiple grid point feature templates of the ideal CAD model according to features; use the Monte Carlo method to sample the overall surface of the ideal CAD model and discretize the ideal CAD model into point cloud data.

[0014] Step S2 includes the following steps:

[0015] Step S2.1: Use a 3D scanning device to collect data on the surface morphology of the actual manufactured workpiece to obtain the surface point cloud data of the actual manufactured workpiece;

[0016] Step S2.2: Process the actual point cloud data obtained by the 3D scanning device in step S2.1, and use radius filtering or statistical filtering methods to filter out noise points in the point cloud data;

[0017] Step S2.3: For the actual point cloud data obtained in step S2.2, the data is simplified using a random sampling method; the data is smoothed using a Gaussian filtering method to improve the accuracy of the 3D point cloud data.

[0018] Step S3 includes the following steps:

[0019] Step S3.1: First, perform coarse registration between the actual point cloud data and the ideal point cloud data: use the local point pair feature descriptor to determine the initial coordinate transformation matrix through random consistency sampling, and complete the coarse registration of the point cloud data acquired by the 3D scanning device;

[0020] Step S3.2: Perform fine registration of 3D scanning point cloud data: Use the iterative nearest point algorithm to calculate the coordinate transformation matrix of the point cloud to obtain finely registered 3D scanning point cloud data.

[0021] Step S4 includes the following steps:

[0022] Step S4.1: Calculate the curvature of the point cloud data based on the relationship between the point and its neighboring points;

[0023] Step S4.2: Randomly specify the initial seed point, growth criteria and number of iterations, and perform region growth segmentation of the point cloud data to obtain point cloud data belonging to different regions according to the curvature change;

[0024] Step S4.3: Match the point cloud data belonging to different regions obtained in step S4.2 with the point feature template of the ideal CAD model in step S1.2 according to the coordinate position, and mark the point cloud data of different regions as the feature attributes of the corresponding ideal CAD model;

[0025] Step S4.4: Use the point feature template of the ideal CAD model to extract point cloud data belonging to different regions.

[0026] Step S4.1 includes the following steps:

[0027] Step S4.1.1: For each point P, define a neighborhood centered at P;

[0028] Step S4.1.2: For each point Pi in the neighborhood, calculate its relative coordinates with respect to point P, and then construct the covariance matrix C;

[0029] Step S4.1.3: For each point P, based on the covariance matrix C, use principal component analysis to calculate the normal vector of point P, thereby obtaining the curvature of the point cloud data.

[0030] Step S6 includes the following steps:

[0031] Step S6.1: Find the adjacent connection relationship of features from the Brep boundary information of the ideal CAD model prior information in step S1.1, and record the adjacent features of each feature;

[0032] Step S6.2: Connect and reorganize the NURBS surface fitted in step S5.1 according to the same feature adjacent connection relationship as the corresponding ideal CAD model in step S6.1 to obtain a CAD model based on 3D scan point cloud data.

[0033] A CAD model building system based on point cloud data domain reconstruction includes:

[0034] The ideal model data acquisition module is used to extract the prior information of the ideal CAD model to be constructed, obtain point feature templates by uniform grid sampling on the surface of the ideal CAD model, and discretize the ideal CAD model into point cloud data.

[0035] The workpiece 3D point cloud data acquisition module is used to obtain the surface point cloud data of the actual manufactured workpiece through a 3D scanning device, perform data preprocessing operations, and obtain 3D point cloud data.

[0036] The point cloud data registration module is used to register 3D point cloud data with the surface point cloud data of an ideal CAD model to obtain actual point cloud data that is located in the same coordinate system as the ideal CAD model.

[0037] The point cloud data segmentation module is used to segment actual point cloud data into point cloud data belonging to different features according to the curvature change of point cloud data using the region growing method; based on the position in the coordinate system, the point cloud data belonging to different regions are matched with the point feature template of the ideal CAD model, and the point cloud data belonging to different regions are extracted using the point feature template of the ideal CAD model.

[0038] The model reconstruction module is used to fit point cloud data from different regions into feature surfaces using NURBS surfaces; the feature surfaces obtained by fitting the point cloud data are connected and recombined according to the adjacent connection relationship of the same features in the ideal CAD model to obtain a CAD model based on the actual 3D point cloud data.

[0039] A CAD model building device based on point cloud data domain reconstruction includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement the CAD model building method based on point cloud data domain reconstruction when the computer program is executed.

[0040] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the CAD model construction method based on point cloud data domain reconstruction.

[0041] The present invention has the following beneficial effects and advantages:

[0042] 1. This invention can improve the efficiency of CAD models based on measured point cloud data, and greatly save the time required to create CAD models using manual modeling techniques;

[0043] 2. This invention divides point cloud data into multiple regions and performs NURBS surface feature reconstruction on each region, thereby improving the accuracy of the reconstructed features.

[0044] 3. In the modeling process, this invention introduces prior information of the ideal CAD model through point cloud registration, and improves modeling efficiency and accuracy by combining the prior information of the ideal CAD model in the process of point cloud segmentation and NURBS surface fitting point selection. Attached Figure Description

[0045] Figure 1This is the overall flow chart of the CAD model construction method based on domain reconstruction of 3D scanned point cloud data according to the present invention;

[0046] Figure 2 This is a principle block diagram of step S3 in an embodiment of the present invention;

[0047] Figure 3 This is a principle block diagram of step S4 in an embodiment of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments. However, the scope of protection of this invention is not limited to the specific embodiments described below.

[0049] Example

[0050] This embodiment discloses a CAD model construction method based on domain-based reconstruction of 3D scanned point cloud data.

[0051] like Figure 1 As shown, the CAD model construction method based on domain-specific reconstruction of 3D scanned point cloud data includes the following steps:

[0052] Step S1: For the ideal CAD model to be constructed, extract the prior information of the ideal CAD model. First, mark all features of the ideal CAD model and record the position, geometric attributes and Brep boundary information of each feature; perform uniform grid sampling on the surface of the ideal CAD model according to different features to obtain several grid point feature templates of the ideal CAD model according to features; sample the entire surface of the ideal CAD model to discretize the ideal CAD model into ideal point cloud data;

[0053] Step S2: Obtain the surface point cloud data of the actual manufactured workpiece through a 3D scanning device, and perform data preprocessing operations (point cloud data denoising, data simplification, and smoothing) to obtain high-precision 3D actual point cloud data.

[0054] Step S3: Register the 3D point cloud data obtained in S2 with the surface point cloud data of the ideal CAD model to obtain the actual point cloud data located in the same coordinate system as the ideal CAD model;

[0055] Step S4: Use the region growing method to segment the point cloud data obtained in S3 into point cloud data belonging to different features according to the curvature change of the point cloud data; according to the position in the coordinate system, match the point cloud data belonging to different regions with the point feature template of the ideal CAD model in S1, and use the point feature template of the ideal CAD model to extract the point cloud data belonging to different regions.

[0056] Step S5: Use NURBS surfaces to fit the point cloud data of different regions in step S4 into feature surfaces.

[0057] Step S6: Connect and reorganize the feature surfaces obtained by fitting the point cloud data in S5 according to the adjacent connection relationship of the same features in the ideal CAD model in S1 to obtain a CAD model based on the actual 3D point cloud data.

[0058] Specifically, step S1 includes the following steps:

[0059] Step S1.1: Extract prior information of the ideal CAD model. The geometric features of the ideal CAD model are identified using a feature recognition method based on geometric information, and the position, geometric attributes, and Brep boundaries of each feature are recorded.

[0060] Specifically, step S1.1 includes the following steps:

[0061] Step S1.1.1: Use feature recognition methods based on geometric information such as shape and curvature to identify and mark the geometric features of the ideal CAD model. These features include holes, planes, cylinders, etc.

[0062] Step S1.1.2: After the features are identified, record the position, geometric attributes, and Brep boundary information of each feature. The position information includes the three-dimensional coordinates of the feature, the geometric attributes include the shape, size, angle, etc. of the feature, and the Brep boundary information includes the parametric representation of the curves surrounding the feature.

[0063] Step S1.2: Uniformly sample grid points on the surface of the ideal CAD model according to different features to obtain several grid point feature templates of the ideal CAD model according to the features; use the Monte Carlo method to sample the overall surface of the ideal CAD model and discretize the ideal CAD model into point cloud data.

[0064] Specifically, step S1.2 includes the following steps:

[0065] Step S1.2.1: For each identified feature, perform uniform grid sampling according to different features. Within each feature region, generate a uniformly distributed set of grid points so that subsequent steps can map the point cloud data onto these grid points.

[0066] Step S1.2.2: Use the Monte Carlo method or other random sampling method to sample the overall surface of the ideal CAD model and discretize it into point cloud data for registration with the point cloud data acquired by the subsequent 3D scanning equipment.

[0067] Step S2 includes the following steps:

[0068] Step S2.1: Use a 3D scanning device to collect data on the surface morphology of the actual manufactured workpiece to obtain the surface point cloud data of the actual manufactured workpiece;

[0069] Step S2.2: Process the actual point cloud data obtained by the 3D scanning device in S2.1, and use radius filtering and statistical filtering methods to filter out noise points in the point cloud data;

[0070] Specifically, step S2.2 includes the following steps:

[0071] Step S2.2.1: Set the filter radius and threshold, and use radius filtering to remove isolated noise points in the point cloud data;

[0072] Step S2.2.2: Set the mean and variance of the filter, and use statistical filtering methods to further remove outliers from the point cloud data;

[0073] Step S2.3: For the point cloud data obtained in S2.2, the data is simplified by random sampling; the data is smoothed by Gaussian filtering to improve the accuracy of the 3D point cloud data.

[0074] Specifically, step S2.3 includes the following steps:

[0075] Step S2.3.1: Set the sampling percentage based on the point cloud data density obtained by discretizing the ideal CAD model in S1, and use a random sampling method to select a portion of points to reduce the amount of actual point cloud data.

[0076] Step S2.3.2: Use Gaussian filtering to smooth the point cloud data, eliminate irregularities caused by scanning equipment or environmental noise, reduce spikes and pits, and improve the overall accuracy of the point cloud data.

[0077] The actual point cloud data in S2 is registered with the ideal point cloud data obtained by discretizing the ideal CAD model in S1.2. Step S3 includes the following steps:

[0078] Step S3.1: First, perform coarse registration between the actual point cloud data and the ideal point cloud data. Use the local point pair feature descriptor combined with random consistency sampling to determine the initial coordinate transformation matrix and complete the coarse registration of the point cloud data acquired by the 3D scanning equipment.

[0079] like Figure 2 As shown, specifically, step S3.1 includes the following steps:

[0080] Step S3.1.1: Calculate the local point-pair feature descriptors for all points in the actual point cloud data in S2 and the ideal point cloud data in S1, respectively.

[0081] Step S3.1.2: Use the random consistency sampling method to identify point pairs that correspond to each other between the actual point cloud data and the ideal point cloud data;

[0082] Step S3.1.3: Based on the point pairs obtained by the random consistency sampling method in step S3.1.2, calculate the coordinate transformation matrix from the actual point cloud data to the ideal point cloud data, and apply the matrix transformation to the actual point cloud data, transforming it from the coordinate system of the scanning device to the same coordinate system as the ideal point cloud data.

[0083] Step S3.2: Perform fine registration of the 3D scanning point cloud data. Use the Iterative Closest Point (ICP) algorithm to calculate the coordinate transformation matrix of the fine registration of the point cloud, and obtain the finely registered 3D scanning point cloud data.

[0084] Specifically, step S3.2 includes the following steps:

[0085] Step S3.2.1: Initialize the ICP algorithm and set the convergence conditions: the error is less than a certain threshold or the number of iterations reaches a predetermined value;

[0086] Step S3.2.2: For each point in the actual point cloud data, find the nearest point in the ideal point cloud and establish a point pair matching relationship;

[0087] Step S3.2.3: Use Euclidean distance to measure the distance error between each pair of points;

[0088] Step S3.2.4: Assign weights to each point pair using the Gaussian weighting function based on the error value;

[0089] Step S3.2.5: Use numerical optimization methods to minimize the error between weighted point pairs and adjust the coordinate transformation matrix between point pairs;

[0090] Step S3.2.6: Check if the convergence condition is met. If the convergence condition is not met, proceed to step S3.2.7. If the convergence condition is met, proceed to step S3.2.8.

[0091] Step S3.2.7: Continue iterating and update the coordinate transformation matrix;

[0092] Step S3.2.8: Obtain the final coordinate transformation matrix, apply the matrix to the actual point cloud data, and accurately register it to the ideal point cloud data.

[0093] like Figure 3 As shown, the point cloud data obtained in S3 is segmented. Step S4 includes the following steps:

[0094] Step S4.1: Calculate the curvature of the point cloud data based on the relationship between the point and its neighboring points;

[0095] Specifically, step S4.1 includes the following steps:

[0096] Step S4.1.1: For each point P, a neighborhood is defined with P as the center;

[0097] Step S4.1.2: For each point Pi in the neighborhood, calculate its relative coordinates with respect to point P, and then construct the covariance matrix C;

[0098] Step S4.1.3: For each point P, calculate the normal vector using principal component analysis (PCA) based on the covariance matrix C;

[0099] Step S4.2: Randomly specify the initial seed point, growth criteria and number of iterations, and perform region growth segmentation of the point cloud data to obtain point cloud data belonging to different regions according to the curvature change;

[0100] Specifically, step S4.2 includes the following steps:

[0101] Step S4.2.1: Randomly select a seed point from the point cloud data as the initial point for segmentation, and give a curvature threshold as the growth criterion;

[0102] Step S4.2.2: For each seed point, calculate the curvature value of that seed point as the curvature reference value for that region;

[0103] Step S4.2.3: Check the neighboring points around the seed point and calculate their curvature values. If the curvature of a neighboring point is similar to that of the seed point (satisfying the growth criterion), then assign the neighboring points to the same region and mark them as processed.

[0104] Step S4.2.4: Repeat step S4.2.3 until no more neighboring points satisfy the growth criterion, or the region stops growing.

[0105] Step S4.2.5: Select an unprocessed point that has not been assigned to any region as the next seed point, and repeat steps S4.2.2, S4.2.3, and S4.2.4 until all points are assigned to a region. This results in a series of point cloud datasets belonging to different regions. Each region has similar curvature features, representing its local geometric properties.

[0106] Step S4.3: Match the point cloud data belonging to different regions obtained in S4.2 with the point feature template of the ideal CAD model in step S1.2 according to the coordinate position, and mark the point cloud data of different regions as the feature attributes (holes, planes, cylinders, etc.) of the corresponding ideal CAD model.

[0107] Specifically, step S4.3 includes the following steps:

[0108] Step S4.3.1: For the point cloud data regions divided in step S4.2, perform the following operations: Traverse each region, use coordinate position relationships to determine the point feature template that best matches the current region, and mark the current point as the feature attribute of the corresponding ideal CAD model, such as hole, plane, cylinder, etc.

[0109] Step S4.3.2: Repeat step S4.3.1 until the corresponding feature attributes have been assigned to each region.

[0110] Step S4.4: Extract point cloud data belonging to different regions using the point feature template of the ideal CAD model;

[0111] Specifically, step S4.4 includes the following steps:

[0112] Step S4.4.1: For each point feature template, perform the following operations: Traverse each point in the point feature template. For each point, calculate its nearest neighbor in the point cloud data belonging to different features matched in step S4.3.1; use the distance between points to determine which point in the point feature template best matches the current point; replace the point in the current point feature template with the matched nearest neighbor in the point cloud data belonging to different features to obtain the point feature template of the actual point cloud data.

[0113] Step S4.4.2: Repeat step S4.4.1 until the actual point cloud has been replaced for each point feature template.

[0114] The point cloud data obtained in S4 is subjected to surface fitting. Step S5 includes the following steps:

[0115] Step S5.1: Determine the data nodes and control points based on the point cloud data extracted in S4.4, and use NURBS surface fitting.

[0116] Specifically, step S5.1 includes the following steps:

[0117] Step S5.1.1: Based on the point feature template of the actual CAD model extracted in step S4.4.1, determine the data nodes and control points used to construct the NURBS surface;

[0118] Step S5.1.2: Initialize the NURBS surface using data nodes and initial control points;

[0119] Step S5.1.3: Use numerical optimization methods to adjust the position of control points, minimize the distance error between the point cloud data and the NURBS surface, and generate the NURBS surface by connecting the control points;

[0120] Step S5.1.4: Repeat steps S5.1.1-S5.1.3 to construct the corresponding NURBS surface for each point feature template of the actual point cloud data;

[0121] The NURBS surface obtained in S5 is reconstructed according to the Brep boundary information of the ideal CAD model in step S1.1. Step S6 includes the following steps:

[0122] Step S6.1: Find the adjacent connection relationships of features from the Brep boundary information of the ideal CAD model in S1.1, and record the adjacent features of each feature;

[0123] Specifically, step S6.1 includes the following steps:

[0124] Step S6.1.1: Traverse each feature in the ideal CAD model. For each feature, check its Brep boundary information obtained in step S1.1. Find and determine the adjacent features by checking whether the boundary curves share endpoints or intersect.

[0125] Step S6.1.2: Use an association list to store the type of each feature (e.g., hole, plane, cylinder, etc.) and the features adjacent to it.

[0126] Step S6.1.3: Repeat steps S6.1.1-S6.1.2 above until information about adjacent features has been recorded for each feature.

[0127] Step S6.2: Connect and reorganize the NURBS surface fitted in step S5.1 according to the same feature adjacent connection relationship as the corresponding ideal CAD model in step S6.1 to obtain a CAD model based on 3D scan point cloud data.

[0128] Step S6.2.1: Traverse the adjacency relationships of each feature in the ideal CAD model, and for each pair of adjacent features, determine which boundaries they share;

[0129] Step S6.2.2: For features with shared boundaries, merge their NURBS surfaces along the shared boundaries to ensure a smooth transition between surfaces;

[0130] Step S6.2.3: Repeat the above process until a connection is established for each pair of adjacent features through a shared boundary. By connecting and recombining the NURBS surfaces, a complete CAD model is obtained.

Claims

1. A CAD model construction method based on point cloud data domain reconstruction, characterized in that, Includes the following steps: Step S1: For the ideal CAD model to be constructed, extract the prior information of the ideal CAD model, obtain the point feature template by uniform grid sampling on the surface of the ideal CAD model, and discretize the ideal CAD model into point cloud data; Step S2: Obtain the surface point cloud data of the actual manufactured workpiece through a 3D scanning device, perform data preprocessing operations, and obtain 3D point cloud data; Step S3: Register the 3D point cloud data obtained in step S2 with the surface point cloud data of the ideal CAD model to obtain the actual point cloud data located in the same coordinate system as the ideal CAD model; Step S4: Use the region growing method to divide the actual point cloud data obtained in S3 into point cloud data belonging to different features according to the curvature change of the point cloud data; according to the position in the coordinate system, match the point cloud data belonging to different regions with the point feature template of the ideal CAD model in S1, and use the point feature template of the ideal CAD model to extract the point cloud data belonging to different regions. Step S5: Use NURBS surfaces to fit the point cloud data of different regions in step S4 into feature surfaces; Step S6: Connect and reorganize the feature surfaces obtained by fitting the point cloud data in step S5 according to the adjacent connection relationship of the same features in the ideal CAD model in S1 to obtain a CAD model based on the actual 3D point cloud data.

2. The CAD model construction method based on point cloud data domain reconstruction according to claim 1, characterized in that, Step S1 includes the following steps: Step S1.1: Extract prior information of the ideal CAD model: Identify the geometric features of the ideal CAD model using a feature recognition method based on geometric information, and record the position, geometric attributes, and Brep boundary of each feature; Step S1.2: Uniformly sample grid points on the surface of the ideal CAD model according to different features to obtain multiple grid point feature templates of the ideal CAD model according to features; use the Monte Carlo method to sample the overall surface of the ideal CAD model and discretize the ideal CAD model into point cloud data.

3. The CAD model construction method based on point cloud data domain reconstruction according to claim 1, characterized in that, Step S2 includes the following steps: Step S2.1: Use a 3D scanning device to collect data on the surface morphology of the actual manufactured workpiece to obtain the surface point cloud data of the actual manufactured workpiece; Step S2.2: Process the actual point cloud data obtained by the 3D scanning device in step S2.1, and use radius filtering or statistical filtering methods to filter out noise points in the point cloud data; Step S2.3: For the actual point cloud data obtained in step S2.2, the data is simplified using a random sampling method; the data is smoothed using a Gaussian filtering method to improve the accuracy of the 3D point cloud data.

4. The CAD model construction method based on point cloud data domain reconstruction according to claim 1, characterized in that, Step S3 includes the following steps: Step S3.1: First, perform coarse registration between the actual point cloud data and the ideal point cloud data: use the local point pair feature descriptor to determine the initial coordinate transformation matrix through random consistency sampling, and complete the coarse registration of the point cloud data acquired by the 3D scanning device; Step S3.2: Perform fine registration of 3D scanning point cloud data: Use the iterative nearest point algorithm to calculate the coordinate transformation matrix of the point cloud to obtain finely registered 3D scanning point cloud data.

5. The CAD model construction method based on point cloud data domain reconstruction according to claim 1, characterized in that, Step S4 includes the following steps: Step S4.1: Calculate the curvature of the point cloud data based on the relationship between the point and its neighboring points; Step S4.2: Randomly specify the initial seed point, growth criteria and number of iterations, and perform region growth segmentation of the point cloud data to obtain point cloud data belonging to different regions according to the curvature change; Step S4.3: Match the point cloud data belonging to different regions obtained in step S4.2 with the point feature template of the ideal CAD model in step S1.2 according to the coordinate position, and mark the point cloud data of different regions as the feature attributes of the corresponding ideal CAD model; Step S4.4: Use the point feature template of the ideal CAD model to extract point cloud data belonging to different regions.

6. The CAD model construction method based on point cloud data domain reconstruction according to claim 5, characterized in that, Step S4.1 includes the following steps: Step S4.1.1: For each point P, define a neighborhood centered at P; Step S4.1.2: For each point Pi in the neighborhood, calculate its relative coordinates with respect to point P, and then construct the covariance matrix C; Step S4.1.3: For each point P, based on the covariance matrix C, use principal component analysis to calculate the normal vector of point P, thereby obtaining the curvature of the point cloud data.

7. The CAD model construction method based on point cloud data domain reconstruction according to claim 1, characterized in that: Step S6 includes the following steps: Step S6.1: Find the adjacent connection relationship of features from the Brep boundary information of the ideal CAD model prior information in step S1.1, and record the adjacent features of each feature; Step S6.2: Connect and reorganize the NURBS surface fitted in step S5.1 according to the same feature adjacent connection relationship as the corresponding ideal CAD model in step S6.1 to obtain a CAD model based on 3D scan point cloud data.

8. A CAD model construction system based on point cloud data domain reconstruction, characterized in that, include: The ideal model data acquisition module is used to extract the prior information of the ideal CAD model to be constructed, obtain point feature templates by uniform grid sampling on the surface of the ideal CAD model, and discretize the ideal CAD model into point cloud data. The workpiece 3D point cloud data acquisition module is used to obtain the surface point cloud data of the actual manufactured workpiece through a 3D scanning device, perform data preprocessing operations, and obtain 3D point cloud data. The point cloud data registration module is used to register 3D point cloud data with the surface point cloud data of an ideal CAD model to obtain actual point cloud data that is located in the same coordinate system as the ideal CAD model. The point cloud data segmentation module is used to segment actual point cloud data into point cloud data belonging to different features according to the curvature change of point cloud data using the region growing method; based on the position in the coordinate system, the point cloud data belonging to different regions are matched with the point feature template of the ideal CAD model, and the point cloud data belonging to different regions are extracted using the point feature template of the ideal CAD model. The model reconstruction module is used to fit point cloud data from different regions into feature surfaces using NURBS surfaces; the feature surfaces obtained by fitting the point cloud data are connected and recombined according to the adjacent connection relationship of the same features in the ideal CAD model to obtain a CAD model based on the actual 3D point cloud data.

9. A CAD model construction device based on point cloud data domain reconstruction, characterized in that, It includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement the CAD model construction method based on point cloud data domain reconstruction as described in any one of claims 1-7 when the computer program is executed.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the CAD model construction method based on point cloud data domain reconstruction as described in any one of claims 1-7.

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