Learning Part Boundaries from 3D Point Clouds
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.