Accelerated Relative Camera Pose from Oriented Features

Determining relative camera pose is a fundamental task in many computer vision systems. Various algorithms have been proposed for determining relative camera pose from feature correspondences, usually based on point correspondences. These correspondences are commonly found using SIFT and similar feature detectors, and Random Sample and Consensus (RANSAC) is commonly applied to identify and remove incorrect matches. The ´points´ found by these feature detectors, however, are not simply two-dimensional image locations -- they have scale and orientation as well. The orientation associated with SIFT-like features can be used to quickly identify many incorrect pose hypotheses in a RANSAC process. This can reduce the number of camera poses that are evaluated with more expensive techniques, with a corresponding decrease in pose estimation time.