  Keypoint detection and matching is a fundamental task in many computer vision
problems, from shape reconstruction, to structure from motion, to AR/VR
applications and robotics. It is a well-studied problem with remarkable
successes such as SIFT, and more recent deep learning approaches. While great
robustness is exhibited by these techniques with respect to noise, illumination
variation, and rigid motion transformations, less attention has been placed on
image distortion sensitivity. In this work, we focus on the case when this is
caused by the geometry of the cameras used for image acquisition, and consider
the keypoint detection and matching problem between the hybrid scenario of a
fisheye and a projective image. We build on a state-of-the-art approach and
derive a self-supervised procedure that enables training an interest point
detector and descriptor network. We also collected two new datasets for
additional training and testing in this unexplored scenario, and we demonstrate
that current approaches are suboptimal because they are designed to work in
traditional projective conditions, while the proposed approach turns out to be
the most effective.
