A case for using rotation invariant features in state of the art feature matchers
Georg Bökman, Fredrik Kahl
The aim of this paper is to demonstrate that a state of the art feature
matcher (LoFTR) can be made more robust to rotations by simply replacing the
backbone CNN with a steerable CNN which is equivariant to translations and
image rotations. It is experimentally shown that this boost is obtained without
reducing performance on ordinary illumination and viewpoint matching sequences.