  Learning-based 3D shape segmentation is usually formulated as a semantic
labeling problem, assuming that all parts of training shapes are annotated with
a given set of tags. This assumption, however, is impractical for learning
fine-grained segmentation. Although most off-the-shelf CAD models are, by
construction, composed of fine-grained parts, they usually miss semantic tags
and labeling those fine-grained parts is extremely tedious. We approach the
problem with deep clustering, where the key idea is to learn part priors from a
shape dataset with fine-grained segmentation but no part labels. Given point
sampled 3D shapes, we model the clustering priors of points with a similarity
matrix and achieve part segmentation through minimizing a novel low rank loss.
To handle highly densely sampled point sets, we adopt a divide-and-conquer
strategy. We partition the large point set into a number of blocks. Each block
is segmented using a deep-clustering-based part prior network trained in a
category-agnostic manner. We then train a graph convolution network to merge
the segments of all blocks to form the final segmentation result. Our method is
evaluated with a challenging benchmark of fine-grained segmentation, showing
state-of-the-art performance.
