Dataset and method for deep learning-based reconstruction of 3D CAD models containing machining features for mechanical parts
Hyunoh Lee, Jinwon Lee, Hyungki Kim, Duhwan Mun
Abstract
ABSTRACT Three-dimensional (3D) computer-aided design (CAD) model reconstruction techniques are used for numerous purposes across various industries, including free-viewpoint video reconstruction, robotic mapping, tomographic reconstruction, 3D object recognition, and reverse engineering. With the development of deep learning techniques, researchers are investigating the reconstruction of 3D CAD models using learning-based methods. Therefore, we proposed a method to effectively reconstruct 3D CAD models containing machining features into 3D voxels through a 3D encoder–decoder network. 3D CAD model datasets were built to train the 3D CAD model reconstruction network. For this purpose, large-scale 3D CAD models containing machining features were generated through parametric modeling and then converted into a 3D voxel format to build the training datasets. The encoder–decoder network was then trained using these training datasets. Finally, the performance of the trained network was evaluated through 3D reconstruction experiments on numerous test parts, which demonstrated a high reconstruction performance with an error rate of approximately 1%.