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University of Amsterdam Abstract Finding matching keypoints between images is a core problem in
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Surprisingly, learning rotation invariance directly in the feature descriptor yields similar performance to handling it in the matcher, but with improved speed and no loss in upright accuracy.
LoMa shatters performance ceilings in local feature matching, achieving state-of-the-art results by scaling data, model size, and compute, proving that even well-trodden areas can yield surprising gains with modern techniques.
