AutoMate: A Dataset and Learning Approach for the Automatic Mating of CAD Assemblies

SIGGRAPH Asia 2021
Benjamin Jones1, Dalton Hildreth1, Duowen Chen1,2, Ilya Baran3, Vova Kim4, and Adriana Schulz1
1University of Washington
2Columbia University
3PTC. Inc.
4Adobe Inc.

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Assembly modeling is a core task of computer aided design (CAD), comprising around one third of the work in a CAD workflow. Optimizing this process therefore represents a huge opportunity in the design of a CAD system, but current research of assembly based modeling is not directly applicable to modern CAD systems because it eschews the dominant data structure of modern CAD: parametric boundary representations (BREPs). CAD assembly modeling defines assemblies as a system of pairwise constraints, called mates, between parts, which are defined relative to BREP topology rather than in world coordinates common to existing work. We propose SB-GCN, a representation learning scheme on BREPs that retains the topological structure of parts, and use these learned representations to predict CAD type mates. To train our system, we compiled the first large scale dataset of BREP CAD assemblies, which we are releasing along with benchmark mate prediction tasks. Finally, we demonstrate the compatibility of our model with an existing commercial CAD system by building a tool that assists users in mate creation by suggesting mate completions, with 72.2% accuracy.


Main paper
ACM Arxiv Code Data
ACM Transactions on Graphics, Volume 40, Issue 6 (December 2021) Article No. 227, pp 1-18.


Available after SIGGRAPH Asia 2021