mdBrief - A Fast Online Adaptable, Distorted Binary Descriptor for Real-Time Applications Using Calibrated Wide-Angle Or Fisheye Cameras
This addresses the issue of image distortion in real-time vision applications like SLAM for users of wide-angle or fisheye cameras, though it is incremental as it modifies an existing descriptor.
The paper tackled the problem of binary descriptors failing under wide-angle or fisheye camera distortions by proposing a distorted and masked version of BRIEF that adapts to different image regions, resulting in a fast online adaptable descriptor for real-time applications.
Fast binary descriptors build the core for many vision based applications with real-time demands like object detection, Visual Odometry or SLAM. Commonly it is assumed, that the acquired images and thus the patches extracted around keypoints originate from a perspective projection ignoring image distortion or completely different types of projections such as omnidirectional or fisheye. Usually the deviations from a perfect perspective projection are corrected by undistortion. Latter, however, introduces severe artifacts if the cameras field-of-view gets larger. In this paper, we propose a distorted and masked version of the BRIEF descriptor for calibrated cameras. Instead of correcting the distortion holistically, we distort the binary tests and thus adapt the descriptor to different image regions.