Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Label-Efficient Learning on Point Clouds using Approximate Convex
  Decompositions   The problems of shape classification and part segmentation from 3D point
clouds have garnered increasing attention in the last few years. Both of these
problems, however, suffer from relatively small training sets, creating the
need for statistically efficient methods to learn 3D shape representations. In
this paper, we investigate the use of Approximate Convex Decompositions (ACD)
as a self-supervisory signal for label-efficient learning of point cloud
representations. We show that using ACD to approximate ground truth
segmentation provides excellent self-supervision for learning 3D point cloud
representations that are highly effective on downstream tasks. We report
improvements over the state-of-the-art for unsupervised representation learning
on the ModelNet40 shape classification dataset and significant gains in
few-shot part segmentation on the ShapeNetPart dataset.Code available at
https://github.com/matheusgadelha/PointCloudLearningACD

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The categories of this paper are: q-bio
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The categories of this paper are: supr-con
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The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
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The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
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The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
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The categories of this paper are: physics