Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Single-Image Depth Prediction Makes Feature Matching Easier   Good local features improve the robustness of many 3D re-localization and
multi-view reconstruction pipelines. The problem is that viewing angle and
distance severely impact the recognizability of a local feature. Attempts to
improve appearance invariance by choosing better local feature points or by
leveraging outside information, have come with pre-requisites that made some of
them impractical. In this paper, we propose a surprisingly effective
enhancement to local feature extraction, which improves matching. We show that
CNN-based depths inferred from single RGB images are quite helpful, despite
their flaws. They allow us to pre-warp images and rectify perspective
distortions, to significantly enhance SIFT and BRISK features, enabling more
good matches, even when cameras are looking at the same scene but in opposite
directions.

22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
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The categories of this paper are: funct-an
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The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th