A case for using rotation invariant features in state of the art feature matchers
Paper in proceeding, 2022

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.

Author

Georg Bökman

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Fredrik Kahl

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

21607508 (ISSN) 21607516 (eISSN)

Vol. 2022-June 5106-5115
9781665487399 (ISBN)

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
New Orleans, USA,

Subject Categories

Bioinformatics (Computational Biology)

Signal Processing

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/CVPRW56347.2022.00559

More information

Latest update

10/27/2023