🤖 AI Summary
Existing CAD deep learning methods neglect geometric constraint modeling, leading to poor discrimination between shapes with similar appearances but distinct constraints. To address this, we propose CstNet—a two-stage constraint-aware network for parametric point cloud analysis—that explicitly encodes CAD constraints as learnable ternary vectors and supports both B-Rep and point cloud inputs. Our contributions are threefold: (1) the first vectorized representation of CAD constraints and an end-to-end constraint learning paradigm; (2) Param20K—the first large-scale multimodal dataset of parametric point clouds, comprising 20,000 samples across 75 classes; and (3) state-of-the-art performance on Param20K, achieving a 3.52% absolute improvement in classification accuracy and a 26.17% gain in rotational robustness over prior methods.
📝 Abstract
Parametric point clouds are sampled from CAD shapes and are becoming increasingly common in industrial manufacturing. Most existing CAD-specific deep learning methods only focus on geometric features, while overlooking constraints which are inherent and important in CAD shapes. This limits their ability to discern CAD shapes with similar appearance but different constraints. To tackle this challenge, we first analyze the constraint importance via a simple validation experiment. Then, we introduce a deep learning-friendly constraints representation with three vectorized components, and design a constraint-aware feature learning network (CstNet), which includes two stages. Stage 1 extracts constraint feature from B-Rep data or point cloud based on shape local information. It enables better generalization ability to unseen dataset after model 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 shape instances of 75 classes. On this dataset, we performed the classification and rotation robustness experiments, and CstNet achieved 3.52% and 26.17% absolute improvements in instance accuracy over the state-of-the-art methods, respectively. To the best of our knowledge, CstNet is the first constraint-aware deep learning method tailored for parametric point cloud analysis in CAD domain.