Characterizing geometric variability of industrial 3D models to guide preparation of synthetic datasets for machine learning applications

This paper presents a characterization approach for analysing geometric variability in industrial 3D model datasets to support the preparation of synthetic datasets for machine-learning applications. By implementing pairwise Hausdorff distances and manifold-based embedding techniques, the study identifies variability ranges required for generating representative synthetic data and demonstrates how targeted augmentation can effectively reproduce real data’s variability, ultimately leading to more reliable and robust NN model performance.