Learning Bottom-up Assembly of Parametric CAD Joints

An overview of assemblies in the Fusion 360 Gallery assembly dataset.

AbstractPhysical 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.

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CAD assemblies contain valuable joint information describing how parts are locally constrained and positioned together. We use this weak supervision to learn a bottom-up approach to assembly. JoinABLe combines an encoder and joint axis prediction network together with a neurally guided joint pose search to assemble pairs of parts without class labels or human guidance.

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