Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in
  Point Cloud   We introduce a novel 3D object proposal approach named Generative Shape
Proposal Network (GSPN) for instance segmentation in point cloud data. Instead
of treating object proposal as a direct bounding box regression problem, we
take an analysis-by-synthesis strategy and generate proposals by reconstructing
shapes from noisy observations in a scene. We incorporate GSPN into a novel 3D
instance segmentation framework named Region-based PointNet (R-PointNet) which
allows flexible proposal refinement and instance segmentation generation. We
achieve state-of-the-art performance on several 3D instance segmentation tasks.
The success of GSPN largely comes from its emphasis on geometric understandings
during object proposal, which greatly reducing proposals with low objectness.

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The categories of this paper are: chem-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: funct-an
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph
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The categories of this paper are: math
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The categories of this paper are: atom-ph
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The categories of this paper are: adap-org
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The categories of this paper are: physics
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The categories of this paper are: ao-sci
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The categories of this paper are: q-bio
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The categories of this paper are: q-fin
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The categories of this paper are: solv-int
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The categories of this paper are: bayes-an
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The categories of this paper are: stat
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The categories of this paper are: math-ph