  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.
