  We present a method that detects boundaries of parts in 3D shapes represented
as point clouds. Our method is based on a graph convolutional network
architecture that outputs a probability for a point to lie in an area that
separates two or more parts in a 3D shape. Our boundary detector is quite
generic: it can be trained to localize boundaries of semantic parts or
geometric primitives commonly used in 3D modeling. Our experiments demonstrate
that our method can extract more accurate boundaries that are closer to
ground-truth ones compared to alternatives. We also demonstrate an application
of our network to fine-grained semantic shape segmentation, where we also show
improvements in terms of part labeling performance.
