  Keypoint detection and description is a commonly used building block in
computer vision systems particularly for robotics and autonomous driving.
Recently CNN based approaches have surpassed classical methods in a number of
perception tasks. However, the majority of techniques to date have focused on
standard cameras with little consideration given to fisheye cameras which are
commonly used in autonomous driving. 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 methods for detection repeatability and descriptor
matching correctness on the Oxford RobotCar datasets.
