  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.
