Part segmentation of industrial 3D models is often limited by the lack of sufficiently large and consistently labeled training datasets. This study proposes a workflow for generating synthetic segmentation datasets from robust parametric computer-aided design (CAD) models and evaluates its applicability on a dental abutment case. The workflow includes the definition of a modeling strategy, creation of a robust parametric CAD model, automated generation of valid geometry variants, and preparation of labeled training data for point-cloud-based segmentation. In the experimental part of the study, a synthetic dataset of segmented dental abutment geometries was generated from the developed parametric CAD model and used to train a PointNeXt-S part-segmentation model. The segmentation performance of the trained model was evaluated on manually labeled real-world abutments. Results show that the segmentation of industrial 3D models improved with increasing synthetic training-set size and further improved when data augmentation was applied. The best-performing augmented model achieved a mean Intersection over Union (IoU) of 89.2% on the real-world validation set, compared with 82.4% without augmentation. The findings indicate that parametric-CAD-based synthetic dataset generation can provide an effective basis for training segmentation models for complex industrial geometries.
Sever et al. (Fri,) studied this question.