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

By: Georg Bokman and Fredrik Kahl: [https://arxiv.org/pdf/2204.10144.pdf](https://arxiv.org/pdf/2204.10144.pdf)

(Accepted to Image Matching Workshop CVPR 2022)

**Abstract**:

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.

art case computervision feature features rotation state