Authors:
Anna Konrad
1
;
2
;
Ciarán Eising
3
;
Ganesh Sistu
4
;
John McDonald
5
;
Rudi Villing
1
and
Senthil Yogamani
4
Affiliations:
1
Department of Electronic Engineering, Maynooth University, Ireland
;
2
Hamilton Institute, Maynooth University, Ireland
;
3
Department of Electronic & Computer Engineering, University of Limerick, Ireland
;
4
Valeo Vision Systems, Galway, Ireland
;
5
Department of Computer Science, Maynooth University, Ireland
Keyword(s):
Keypoints, Interest Points, Feature Detection, Feature Description, Fisheye Images, Deep Learning.
Abstract:
Keypoint detection and description is a commonly used building block in computer vision systems particularly for robotics and autonomous driving. However, the majority of techniques to date have focused on standard cameras with little consideration given to fisheye cameras which are commonly used in urban driving and automated parking. In this paper, we propose a novel training and evaluation pipeline for fisheye images. We make use of SuperPoint as our baseline which is a self-supervised keypoint detector and descriptor that has achieved state-of-the-art results on homography estimation. We introduce a fisheye adaptation pipeline to enable training on undistorted fisheye images. We evaluate the performance on the HPatches benchmark, and, by introducing a fisheye based evaluation method for detection repeatability and descriptor matching correctness, on the Oxford RobotCar dataset.