IEEE Trans Pattern Anal Mach Intell. 2018 Apr;40(4):958-972. doi: 10.1109/TPAMI.2017.2693984. Epub 2017 Apr 12.
This paper addresses the problem of robust and efficient relative rotation averaging in the context of large-scale Structure from Motion. Relative rotation averaging finds global or absolute rotations for a set of cameras from a set of observed relative rotations between pairs of cameras. We propose a generalized framework of relative rotation averaging that can use different robust loss functions and jointly optimizes for all the unknown camera rotations. Our method uses a quasi-Newton optimization which results in an efficient iteratively reweighted least squares (IRLS) formulation that works in the Lie algebra of the 3D rotation group. We demonstrate the performance of our approach on a number of large-scale data sets. We show that our method outperforms existing methods in the literature both in terms of speed and accuracy.
本文针对大规模运动结构中鲁棒高效相对旋转平均问题进行了研究。相对旋转平均是从一对相机之间的观测相对旋转中为一组相机找到全局或绝对旋转。我们提出了一个相对旋转平均的广义框架，可以使用不同的鲁棒损失函数，并为所有未知相机旋转进行联合优化。我们的方法使用拟牛顿优化，这导致了一个在 3D 旋转群的李代数中工作的高效迭代重加权最小二乘 (IRLS) 公式。我们在一些大规模数据集上展示了我们方法的性能。我们表明，我们的方法在速度和准确性方面都优于文献中的现有方法。
