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.

中文翻译：

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具有单个仿射对应的相对姿态估计


在本文中，我们通过利用特征点之间的仿射变换提出了两视图相对姿态估计的最小解决方案的四种情况，并且我们演示了这些情况的有效求解器。结果表明，在平面运动假设或知道垂直方向的情况下，单个仿射对应足以恢复相对相机位姿。考虑的四种情况是校准相机的两视图平面相对运动作为闭式和最小二乘解、未知焦距的闭式解以及已知垂直方向的情况。这些算法可有效用于 RANSAC 循环内的异常值检测和初始运动估计。所有方法都在合成数据和真实数据集上进行评估。实验结果表明，我们的方法在准确性方面优于同类最先进的方法，并且减少了所需的 RANSAC 迭代次数。源代码发布于https://github.com/jizhaox/relative_pose_from_affine。