IEEE Trans Cybern. 2022 Oct;52(10):10111-10122. doi: 10.1109/TCYB.2021.3069806. Epub 2022 Sep 19.
In this article, 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 least-squares solutions, 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. 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. The source code is released at https://github.com/jizhaox/relative_pose_from_affine.
在本文中，我们提出了四种利用特征点之间的仿射变换进行双视图相对位姿估计的最小解，并展示了这些情况的有效求解器。结果表明，在平面运动假设下或具有垂直方向知识的情况下，单个仿射对应足以恢复相对相机姿态。所考虑的四种情况是校准相机的双视图平面相对运动，作为闭合形式和最小二乘解，未知焦距的闭合形式解，以及已知垂直方向的情况。这些算法可用于 RANSAC 循环内的异常值检测和初始运动估计。所有方法均在合成数据和真实数据集上进行了评估。实验结果表明，与类似的最先进方法相比，我们的方法在准确性方面表现出色，同时减少了所需 RANSAC 迭代次数。源代码可在 https://github.com/jizhaox/relative_pose_from_affine 上获得。
