A case for using rotation invariant features in state of the art feature matchers
Paper i 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.
Författare
Georg Bökman
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Fredrik Kahl
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
21607508 (ISSN) 21607516 (eISSN)
Vol. 2022-June 5106-51159781665487399 (ISBN)
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
New Orleans, USA,
New Orleans, USA,
Ämneskategorier
Bioinformatik (beräkningsbiologi)
Signalbehandling
Datorseende och robotik (autonoma system)
DOI
10.1109/CVPRW56347.2022.00559