Augmenting Geometric Deep Learning for CAD Analysis

This presentation delves into CAD model analysis with a focus on leveraging neural networks and geometric deep learning for STEP file processing. It explores the challenges of CAD data irregularities, preprocessing techniques, and advanced methods like Tree-LSTMs and UV-Net for hierarchical modeling and feature extraction. The discussion includes robust data augmentation strategies, experimental findings on CAD-specific datasets, and their implications. Concluding with key insights and future...