Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PatchMVSNet: Patch-wise Unsupervised Multi-View Stereo for
  Weakly-Textured Surface Reconstruction   Learning-based multi-view stereo (MVS) has gained fine reconstructions on
popular datasets. However, supervised learning methods require ground truth for
training, which is hard to be collected, especially for the large-scale
datasets. Though nowadays unsupervised learning methods have been proposed and
have gotten gratifying results, those methods still fail to reconstruct intact
results in challenging scenes, such as weakly-textured surfaces, as those
methods primarily depend on pixel-wise photometric consistency which is
subjected to various illuminations. To alleviate matching ambiguity in those
challenging scenes, this paper proposes robust loss functions leveraging
constraints beneath multi-view images: 1) Patch-wise photometric consistency
loss, which expands the receptive field of the features in multi-view
similarity measuring, 2) Robust twoview geometric consistency, which includes a
cross-view depth consistency checking with the minimum occlusion. Our
unsupervised strategy can be implemented with arbitrary depth estimation
frameworks and can be trained with arbitrary large-scale MVS datasets.
Experiments show that our method can decrease the matching ambiguity and
particularly improve the completeness of weakly-textured reconstruction.
Moreover, our method reaches the performance of the state-of-the-art methods on
popular benchmarks, like DTU, Tanks and Temples and ETH3D. The code will be
released soon.

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The categories of this paper are: comp-gas
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The categories of this paper are: eess
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The categories of this paper are: q-alg
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The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: hep-ex
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The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
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The categories of this paper are: atom-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: math
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The categories of this paper are: econ
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The categories of this paper are: acc-phys