FisheyeSuperPoint: Keypoint Detection and Description Network for Fisheye Images
This addresses a domain-specific problem for robotics and autonomous driving applications, but it is incremental as it adapts an existing method to new data.
The paper tackles the problem of keypoint detection and description for fisheye cameras, which are common in robotics and autonomous driving but lack specialized methods, by proposing a novel training and evaluation pipeline that adapts the SuperPoint baseline to fisheye images, achieving evaluation on the HPatches benchmark and a fisheye-based method on the Oxford RobotCar dataset.
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.