Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Fine-Grained Segmentation of 3D Shapes without Part Labels   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.

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The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
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The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-th
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The categories of this paper are: gr-qc
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The categories of this paper are: stat
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph