Mesh Segmentation with Enhanced Shape Perception for CAD Models

Primitive segmentation and fitting of CAD meshes are important issues in the field of CAD. The segmentation results of existing methods have large errors with the surface primitives. In this regard, a CAD mesh segmentation method with enhanced shape perception is proposed. The segmentation region is generated by expanding from seed faces, while surface primitives are fitted synchronously and the subsequent expansion process is guided by the statistics of fitting error, thereby improving the consistency between the segmentation region and the surface primitives; by using the distance from the face to the primitive as the criterion, and estimating the noise distribution for adaptive expansion, noisy models can be robustly handled; for blending surfaces between primitives, curve skeletons are extracted to express the topology between adjacent primitives, and blending surfaces are segmented based on curve skeletons. The results on 146 models from the ABC dataset show that the proposed method improves the mIoU by 18% and reduces the RMSE by 92% on average.