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We present OpenGlue: a free open-source framework for image matching, that uses a Graph Neural Network-based matcher inspired by SuperGlue \cite{sarlin20superglue}.
Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography
Martin A. Fischler and Robert C. Bolles · 1981
Earlier work this paper cites.
A combined corner and edge detector
Chris Harris and Mike Stephens · 1988
Earlier work this paper cites.
Good features to track
Jianbo Shi and Tomasi · 1994
Earlier work this paper cites.
Wide baseline stereo matching
Philip Pritchett and Andrew Zisserman · 1998
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Object recognition from local scale-invariant features
David G. Lowe · 1999
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Robust wide baseline stereo from maximally stable extremal regions
Jiri Matas, Ondrej Chum, Martin Urban, and Tomas Pajdla · 2002
Earlier work this paper cites.
Image registration methods: a survey
Barbara Zitová and Jan Flusser · 2003
Earlier work this paper cites.
Distinctive image features from scale-invariant keypoints
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2020
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