  We introduce UV-Net, a novel neural network architecture and representation
designed to operate directly on Boundary representation (B-rep) data from 3D
CAD models. The B-rep format is widely used in the design, simulation and
manufacturing industries to enable sophisticated and precise CAD modeling
operations. However, B-rep data presents some unique challenges when used with
modern machine learning due to the complexity of the data structure and its
support for both continuous non-Euclidean geometric entities and discrete
topological entities. In this paper, we propose a unified representation for
B-rep data that exploits the U and V parameter domain of curves and surfaces to
model geometry, and an adjacency graph to explicitly model topology. This leads
to a unique and efficient network architecture, UV-Net, that couples image and
graph convolutional neural networks in a compute and memory-efficient manner.
To aid in future research we present a synthetic labelled B-rep dataset,
SolidLetters, derived from human designed fonts with variations in both
geometry and topology. Finally we demonstrate that UV-Net can generalize to
supervised and unsupervised tasks on five datasets, while outperforming
alternate 3D shape representations such as point clouds, voxels, and meshes.
