Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Guide Local Feature Matching by Overlap Estimation   Local image feature matching under large appearance, viewpoint, and distance
changes is challenging yet important. Conventional methods detect and match
tentative local features across the whole images, with heuristic consistency
checks to guarantee reliable matches. In this paper, we introduce a novel
Overlap Estimation method conditioned on image pairs with TRansformer, named
OETR, to constrain local feature matching in the commonly visible region. OETR
performs overlap estimation in a two-step process of feature correlation and
then overlap regression. As a preprocessing module, OETR can be plugged into
any existing local feature detection and matching pipeline, to mitigate
potential view angle or scale variance. Intensive experiments show that OETR
can boost state-of-the-art local feature matching performance substantially,
especially for image pairs with small shared regions. The code will be publicly
available at https://github.com/AbyssGaze/OETR.

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The categories of this paper are: cmp-lg
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The categories of this paper are: funct-an
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom
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The categories of this paper are: plasm-ph
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The categories of this paper are: nlin
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The categories of this paper are: hep-th
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The categories of this paper are: astro-ph
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The categories of this paper are: math