Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visual Localization Using Semantic Segmentation and Depth Prediction   In this paper, we propose a monocular visual localization pipeline leveraging
semantic and depth cues. We apply semantic consistency evaluation to rank the
image retrieval results and a practical clustering technique to reject
estimation outliers. In addition, we demonstrate a substantial performance
boost achieved with a combination of multiple feature extractors. Furthermore,
by using depth prediction with a deep neural network, we show that a
significant amount of falsely matched keypoints are identified and eliminated.
The proposed pipeline outperforms most of the existing approaches at the
Long-Term Visual Localization benchmark 2020.

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The categories of this paper are: math
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
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The categories of this paper are: hep-ex
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The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: gr-qc
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The categories of this paper are: quant-ph
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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ph
18
The categories of this paper are: econ
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The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat