Point cloud dataset creation for machine learning on CAD models

Recently, the application of machine learning on Computer-Aided Design (CAD) models has emerged. However, there is a lack of robust methods for the conversion of boundary representation (B-Rep) CAD models from engineering software to appropriate input representations for a machine learning algorithm. Those that do exist break the link with the B-Rep, meaning the ability to use machine learning to support future engineering operations on the B-Rep are challenging. This paper presents a method for the creation and labelling of point clouds from B-Rep CAD models for machine learning techniques, while maintaining a link between the two representations. This method allows for the creation of a dataset with additional input features determined from the CAD model such as B-Rep face labels, that could increase the accuracy of certain problems when machine learning is utilized. First, an open-source software called Cloud Compare is used for point cloud creation. Fast interrogation of the CAD model using face bounding boxes are utilized to link points to their corresponding faces. This link allows for easy traversal of the CAD model topology to gain other geometric features if needed. A deficiency of the approach is that some B-Rep faces result in being under sampled. For these insufficiently sampled faces, a method is presented to resample them. Experiments on the approach are outlined to illustrate the efficiency of the proposed method with the approach taking approximately 10 seconds per CAD model within the tested dataset.