Generation of Synthetic Dataset for Part Segmentation Problems

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.