Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Point Cloud Colorization Based on Densely Annotated 3D Shape Dataset   This paper introduces DensePoint, a densely sampled and annotated point cloud
dataset containing over 10,000 single objects across 16 categories, by merging
different kind of information from two existing datasets. Each point cloud in
DensePoint contains 40,000 points, and each point is associated with two sorts
of information: RGB value and part annotation. In addition, we propose a method
for point cloud colorization by utilizing Generative Adversarial Networks
(GANs). The network makes it possible to generate colours for point clouds of
single objects by only giving the point cloud itself. Experiments on DensePoint
show that there exist clear boundaries in point clouds between different parts
of an object, suggesting that the proposed network is able to generate
reasonably good colours. Our dataset is publicly available on the project page.

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The categories of this paper are: physics
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The categories of this paper are: supr-con
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The categories of this paper are: stat
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ph
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The categories of this paper are: math-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: ao-sci
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The categories of this paper are: quant-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-th
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The categories of this paper are: mtrl-th
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The categories of this paper are: nlin