提出更快的五点法相机位姿估计算法，精度相当但速度翻倍。
Fast Iterative Five point Relative Pose Estimation
- 基于Powell狗腿算法迭代求解，提升计算效率。
- 在三类真实数据集上验证，精度与Nister方法相当。
- 可扩展至多于五点，适合作为精修步骤使用。
精确估计双相机间的相对位姿是结构与运动方法的基础。对于已标定相机，五点法结合RANSAC等鲁棒估计器在多数情况下表现最佳。当前最先进的五点位姿求解方法是Nister提出的算法，因其速度快，且在RANSAC框架中可通过增加迭代次数提升精度。本文提出一种新的迭代方法，基于Powell的狗腿算法，其精度与Nister方法相当，但速度约为其两倍。该方法可轻松扩展至超过五点的情况，同时保持高效的误差度量，因此也十分适合用作优化精修步骤。算法在三类具有真实标注的数据集上进行了系统评估。
原文摘要 · Abstract (English)
Robust estimation of the relative pose between two cameras is a fundamental part of Structure and Motion methods. For calibrated cameras, the five point method together with a robust estimator such as RANSAC gives the best result in most cases. The current state-of-the-art method for solving the relative pose problem from five points is due to Nister [9], because it is faster than other methods and in the RANSAC scheme one can improve precision by increasing the number of iterations. In this paper, we propose a new iterative method, which is based on Powell's Dog Leg algorithm. The new method has the same precision and is approximately twice as fast as Nister's algorithm. The proposed method is easily extended to more than five points while retaining a efficient error metrics. This makes it also very suitable as an refinement step. The proposed algorithm is systematically evaluated on three types of datasets with known ground truth.
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