论文标题
CAD的自我监督的代表性学习
Self-Supervised Representation Learning for CAD
论文作者
论文摘要
人造物体的设计由计算机辅助设计（CAD）工具主导。通过CAD的本机格式缺乏标记的数据，可以阻碍使用数据驱动的机器学习方法来协助设计。参数边界表示（B-REP）。最近已发布了几个以B-REP格式的机械零件数据集用于机器学习研究。但是，大型数据库在很大程度上是未标记的，标记的数据集很小。此外，特定于任务的标签集很少见，并且注释昂贵。这项工作建议在监督学习任务上利用未标记的CAD几何形状。我们学习了B-REP几何形状的一种新颖的，混合的隐式/显式表面表示，并表明这种预训练显着提高了很少的学习性能，并且还可以在几种现有的B-REP基准上实现最先进的性能。
The design of man-made objects is dominated by computer aided design (CAD) tools. Assisting design with data-driven machine learning methods is hampered by lack of labeled data in CAD's native format; the parametric boundary representation (B-Rep). Several data sets of mechanical parts in B-Rep format have recently been released for machine learning research. However, large scale databases are largely unlabeled, and labeled datasets are small. Additionally, task specific label sets are rare, and costly to annotate. This work proposes to leverage unlabeled CAD geometry on supervised learning tasks. We learn a novel, hybrid implicit/explicit surface representation for B-Rep geometry, and show that this pre-training significantly improves few-shot learning performance and also achieves state-of-the-art performance on several existing B-Rep benchmarks.
