Physical products are often complex assemblies combining a multitude of 3D parts modeled in computer-aided design (CAD) software. CAD designers build up these assemblies by aligning individual parts to one another using constraints called joints. In this paper we introduce JoinABLe, a learning-based method that assembles parts together to form joints. JoinABLe uses the weak supervision available in standard parametric CAD files without the help of object class labels or human guidance. Our results show that by making network predictions over a graph representation of solid models we can outperform multiple baseline methods with an accuracy (79.53%) that approaches human performance (80%). Finally, to support future research we release the Fusion 360 Gallery assembly dataset, containing assemblies with rich information on joints, contact surfaces, holes, and the underlying assembly graph structure.
翻译:物理产品往往是复杂的组合,结合了计算机辅助设计软件(CAD)中模型的多种3D部件。 CAD设计师通过使用所谓的联合限制使各个部件相互对齐来建立这些组合。 在本文中,我们引入了以学习为基础的方法UnitABLe, 将部件组合在一起形成联合。 JoinABLe使用标准参数 CAD文件中的薄弱监督功能, 而没有目标类标签或人的指导。 我们的结果表明,通过对固体模型的图形表示进行网络预测,我们可以在接近人类性能(80%)的精度(79.53%)下,超越多种基线方法。 最后,为了支持未来的研究,我们释放了组合360画组数据集, 其中包含关于联合、接触表面、孔和基本组装图结构的丰富信息。