  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.
