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

Learning Fine-Grained Segmentation of 3D Shapes without Part Labels