  Boundary Representations (B-Reps) are the industry standard in 3D Computer
Aided Design/Manufacturing (CAD/CAM) and industrial design due to their
fidelity in representing stylistic details. However, they have been ignored in
the 3D style research. Existing 3D style metrics typically operate on meshes or
pointclouds, and fail to account for end-user subjectivity by adopting fixed
definitions of style, either through crowd-sourcing for style labels or
hand-crafted features. We propose UVStyle-Net, a style similarity measure for
B-Reps that leverages the style signals in the second order statistics of the
activations in a pre-trained (unsupervised) 3D encoder, and learns their
relative importance to a subjective end-user through few-shot learning. Our
approach differs from all existing data-driven 3D style methods since it may be
used in completely unsupervised settings, which is desirable given the lack of
publicly available labelled B-Rep datasets. More importantly, the few-shot
learning accounts for the inherent subjectivity associated with style. We show
quantitatively that our proposed method with B-Reps is able to capture stronger
style signals than alternative methods on meshes and pointclouds despite its
significantly greater computational efficiency. We also show it is able to
generate meaningful style gradients with respect to the input shape, and that
few-shot learning with as few as two positive examples selected by an end-user
is sufficient to significantly improve the style measure. Finally, we
demonstrate its efficacy on a large unlabeled public dataset of CAD models.
Source code and data will be released in the future.
