  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.
