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The aim of this paper is to demonstrate that a state of the art featurematcher (LoFTR) can be made more robust to rotations by simply replacing thebackbone CNN with a steerable CNN which is equivariant to translations andimage rotations. It is experimentally shown that this boost is obtained withoutreducing performance on ordinary illumination and viewpoint matching sequences.

A case for using rotation invariant features in state of the art feature matchers