Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 2nd Place Solution for IJCAI-PRICAI 2020 3D AI Challenge: 3D Object
  Reconstruction from A Single Image   In this paper, we present our solution for the {\it IJCAI--PRICAI--20 3D AI
Challenge: 3D Object Reconstruction from A Single Image}. We develop a variant
of AtlasNet that consumes single 2D images and generates 3D point clouds
through 2D to 3D mapping. To push the performance to the limit and present
guidance on crucial implementation choices, we conduct extensive experiments to
analyze the influence of decoder design and different settings on the
normalization, projection, and sampling methods. Our method achieves 2nd place
in the final track with a score of $70.88$, a chamfer distance of $36.87$, and
a mean f-score of $59.18$. The source code of our method will be available at
https://github.com/em-data/Enhanced_AtlasNet_3DReconstruction.

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The categories of this paper are: math
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
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The categories of this paper are: stat
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
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The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol