mdBrief - A Fast Online Adaptable, Distorted Binary Descriptor for Real-Time Applications Using Calibrated Wide-Angle Or Fisheye Cameras

TL;DR

This paper introduces mdBrief, a fast binary descriptor tailored for real-time vision applications using calibrated wide-angle or fisheye cameras, by incorporating distortion directly into the descriptor without undistortion artifacts.

Contribution

It proposes a novel distorted and masked binary descriptor that adapts to image distortions in wide-angle and fisheye cameras, avoiding artifacts from traditional undistortion methods.

Findings

Achieves real-time performance in vision tasks.

Improves robustness to wide-angle and fisheye distortions.

Outperforms traditional descriptors in distorted camera setups.

Abstract

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.