Parametric point clouds are sampled from CAD shapes, have become increasingly prevalent in industrial manufacturing. However, most existing point cloud learning methods focus on the geometric features, such as local and global features or developing efficient convolution operations, overlooking the important attribute of constraints inherent in CAD shapes, which limits these methods' ability to fully comprehend CAD shapes. 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) is developed to extract and leverage constraints. Our CstNet includes two stages. The Stage 1 extracts constraints from B-Rep data or point cloud. The Stage 2 leverages coordinates and constraints to enhance the comprehend 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 shapes datasets. To the best of our knowledge, CstNet is the first constraint-based learning method tailored for CAD shapes analysis.

翻译：参数化点云是从CAD形状中采样得到的点云，在工业制造领域日益普及。然而，现有的大多数点云学习方法主要关注几何特征，例如局部和全局特征或开发高效的卷积操作，忽略了CAD形状固有的约束这一重要属性，这限制了这些方法充分理解CAD形状的能力。为解决这一问题，我们分析了约束的影响，并提出了其深度学习友好的表示方法，随后开发了约束特征学习网络（CstNet）来提取和利用约束。我们的CstNet包含两个阶段：第一阶段从B-Rep数据或点云中提取约束；第二阶段利用坐标和约束来增强对CAD形状的理解。此外，针对带标签B-Rep数据集的稀缺性，我们构建了包含20,000个样本的参数化多模态数据集。实验表明，我们的CstNet在公开数据集和提出的CAD形状数据集上均实现了最先进的性能。据我们所知，CstNet是首个针对CAD形状分析量身定制的基于约束的学习方法。