Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Conditional Single-view Shape Generation for Multi-view Stereo
  Reconstruction   In this paper, we present a new perspective towards image-based shape
generation. Most existing deep learning based shape reconstruction methods
employ a single-view deterministic model which is sometimes insufficient to
determine a single groundtruth shape because the back part is occluded. In this
work, we first introduce a conditional generative network to model the
uncertainty for single-view reconstruction. Then, we formulate the task of
multi-view reconstruction as taking the intersection of the predicted shape
spaces on each single image. We design new differentiable guidance including
the front constraint, the diversity constraint, and the consistency loss to
enable effective single-view conditional generation and multi-view synthesis.
Experimental results and ablation studies show that our proposed approach
outperforms state-of-the-art methods on 3D reconstruction test error and
demonstrate its generalization ability on real world data.

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The categories of this paper are: supr-con
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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: quant-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: q-fin
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The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
19
The categories of this paper are: eess
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
18
The categories of this paper are: econ