  This paper introduces HPNet, a novel deep-learning approach for segmenting a
3D shape represented as a point cloud into primitive patches. The key to deep
primitive segmentation is learning a feature representation that can separate
points of different primitives. Unlike utilizing a single feature
representation, HPNet leverages hybrid representations that combine one learned
semantic descriptor, two spectral descriptors derived from predicted geometric
parameters, as well as an adjacency matrix that encodes sharp edges. Moreover,
instead of merely concatenating the descriptors, HPNet optimally combines
hybrid representations by learning combination weights. This weighting module
builds on the entropy of input features. The output primitive segmentation is
obtained from a mean-shift clustering module. Experimental results on benchmark
datasets ANSI and ABCParts show that HPNet leads to significant performance
gains from baseline approaches.
