Minimal Solutions for Relative Pose with a Single Affine Correspondence

TL;DR

This paper introduces four minimal solutions for two-view relative pose estimation using a single affine correspondence, improving efficiency and accuracy in outlier detection and initial motion estimation.

Contribution

It presents novel algorithms for relative pose estimation leveraging affine correspondences under various conditions, including planar motion and known vertical direction.

Findings

Outperforms state-of-the-art methods in accuracy

Reduces RANSAC iterations needed for robust estimation

Validated on synthetic and real-world datasets like KITTI

Abstract

In this paper we present four cases of minimal solutions for two-view relative pose estimation by exploiting the affine transformation between feature points and we demonstrate efficient solvers for these cases. It is shown, that under the planar motion assumption or with knowledge of a vertical direction, a single affine correspondence is sufficient to recover the relative camera pose. The four cases considered are two-view planar relative motion for calibrated cameras as a closed-form and a least-squares solution, a closed-form solution for unknown focal length and the case of a known vertical direction. These algorithms can be used efficiently for outlier detection within a RANSAC loop and for initial motion estimation. All the methods are evaluated on both synthetic data and real-world datasets from the KITTI benchmark. The experimental results demonstrate that our methods outperform comparable state-of-the-art methods in accuracy with the benefit of a reduced number of needed RANSAC iterations.