Minimal Solutions for Relative Pose With a Single Affine Correspondence

Banglei Guan, Ji Zhao, Zhang Li, Fang Sun, Friedrich Fraundorfer; The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 1929-1938

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.

Related Material


[pdf] [supp]
[bibtex]
@InProceedings{Guan_2020_CVPR,
author = {Guan, Banglei and Zhao, Ji and Li, Zhang and Sun, Fang and Fraundorfer, Friedrich},
title = {Minimal Solutions for Relative Pose With a Single Affine Correspondence},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}