Parametric point clouds are sampled from CAD shapes, and have become increasingly prevalent in industrial manufacturing. However, most existing point cloud learning methods focus on the geometric features, such as developing efficient convolution operations, overlooking the important attribute of constraints inherent in CAD shapes, which limits these methods' ability to comprehend CAD shapes fully. To address this issue, we analyzed the effect of constraints, and proposed its deep learning-friendly representation, after that, the Constraint Feature Learning Network (CstNet) was developed to extract and leverage constraints. Our CstNet includes two stages. Stage 1 extracts constraints from B-Rep data or point cloud. Stage 2 leverages coordinates and constraints to enhance the comprehension of CAD shapes. Additionally, we built up the Parametric 20,000 Multi-modal Dataset for the scarcity of labeled B-Rep datasets. Experiments demonstrate that our CstNet achieved state-of-the-art performance on both public and proposed CAD shape datasets. To the best of our knowledge, CstNet is the first constraint-based learning method tailored for CAD shape analysis.

本研究解决了现有点云学习方法对CAD形状中的约束属性重视不足的问题。通过分析约束的影响并提出深度学习友好的表现形式，开发了约束特征学习网络（CstNet），该网络有效提取和利用约束信息。实验表明，CstNet在公共和提出的CAD形状数据集上达到了最先进的性能，标志着其在CAD形状分析领域的创新性贡献。

参数化点云的约束学习