Constraint-Aware Feature Learning for Parametric Point Cloud
This work addresses the challenge of discerning CAD shapes with similar appearances but different constraints for industrial manufacturing applications, representing an incremental advance in domain-specific deep learning.
The paper tackles the problem of analyzing parametric point clouds from CAD shapes by incorporating constraint information, which previous methods overlooked, and introduces CstNet to achieve improvements of 3.49% in classification accuracy and 26.17% in rotation robustness over state-of-the-art methods on a new dataset.
Parametric point clouds are sampled from CAD shapes and are becoming increasingly common in industrial manufacturing. Most CAD-specific deep learning methods focus on geometric features, while overlooking constraints inherent in CAD shapes. This limits their ability to discern CAD shapes with similar appearances but different constraints. To tackle this challenge, we first analyze the constraint importance via simple validation experiments. Then, we introduce a deep learning-friendly constraints representation with three components, and design a constraint-aware feature learning network (CstNet), which includes two stages. Stage 1 extracts constraint representation from BRep data or point cloud based on local features. It enables better generalization ability to unseen dataset after pre-training. Stage 2 employs attention layers to adaptively adjust the weights of three constraints' components. It facilitates the effective utilization of constraints. In addition, we built the first multi-modal parametric-purpose dataset, i.e. Param20K, comprising about 20K CAD instances of 75 classes. On this dataset, CstNet achieved 3.49% (classification) and 26.17% (rotation robustness) accuracy improvements over the state-of-the-art. To the best of our knowledge, CstNet is the first constraint-aware deep learning method tailored for parametric point cloud analysis.