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.

本文介绍了一种以深度聚类为基础的三维形状部分分割方法，通过学习来自具有细颗粒度分割但没有零件标签的形状数据集的部分先验，它通过最小化新型低秩损失来实现部分分割并表现出优秀的性能。