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Keypoint detection and description is a commonly used building block incomputer vision systems particularly for robotics and autonomous driving.However, the majority of techniques to date have focused on standard cameraswith little consideration given to fisheye cameras which are commonly used inurban driving and automated parking. In this paper, we propose a novel trainingand evaluation pipeline for fisheye images. We make use of SuperPoint as ourbaseline which is a self-supervised keypoint detector and descriptor that hasachieved state-of-the-art results on homography estimation. We introduce afisheye adaptation pipeline to enable training on undistorted fisheye images.We evaluate the performance on the HPatches benchmark, and, by introducing afisheye based evaluation method for detection repeatability and descriptormatching correctness, on the Oxford RobotCar dataset.

FisheyeSuperPoint: Keypoint Detection and Description Network for Fisheye Images