A systematic evaluation shows that learned descriptors, GMS pruning, and a coarse-to-fine RANSAC plus LMedS combination improve fundamental matrix estimation over the SIFT plus RANSAC baseline.
HPatches: A benchmark and evaluation of handcrafted and learned local descriptors
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An Evaluation of Feature Matchers for Fundamental Matrix Estimation
A systematic evaluation shows that learned descriptors, GMS pruning, and a coarse-to-fine RANSAC plus LMedS combination improve fundamental matrix estimation over the SIFT plus RANSAC baseline.