标题： 快速且鲁棒的旋转平均与各向异性坐标下降 显示英文标题

标题： Fast and Robust Rotation Averaging with Anisotropic Coordinate Descent

摘要： 各向异性旋转平均化最近被探索为相应的各向同性方法的自然扩展。在各向异性公式中，通过标准双视图优化获得的估计相对旋转的不确定性被传播到绝对旋转的优化中。由此产生的半定松弛能够恢复全局最小值，但随着问题规模的增长表现不佳。局部方法速度快且也能实现鲁棒估计，但对初始值敏感。它们通常采用最小生成树，因此会受到漂移积累的影响，并可能陷入较差的局部最小值。本文试图弥合各向异性旋转平均化的最优性、鲁棒性和效率之间的差距。我们分析了一组最初提出用于优化标准弦距离的块坐标下降法，并推导出一个更简单的公式和各向异性的扩展，从而得到一个快速的通用求解器。我们将该求解器集成到扩展的各向异性大规模鲁棒旋转平均化管道中。所得到的算法在公开的从运动恢复结构数据集中实现了最先进的性能。项目页面：https://ylochman.github.io/acd 显示英文摘要

摘要： Anisotropic rotation averaging has recently been explored as a natural extension of respective isotropic methods. In the anisotropic formulation, uncertainties of the estimated relative rotations -- obtained via standard two-view optimization -- are propagated to the optimization of absolute rotations. The resulting semidefinite relaxations are able to recover global minima but scale poorly with the problem size. Local methods are fast and also admit robust estimation but are sensitive to initialization. They usually employ minimum spanning trees and therefore suffer from drift accumulation and can get trapped in poor local minima. In this paper, we attempt to bridge the gap between optimality, robustness and efficiency of anisotropic rotation averaging. We analyze a family of block coordinate descent methods initially proposed to optimize the standard chordal distances, and derive a much simpler formulation and an anisotropic extension obtaining a fast general solver. We integrate this solver into the extended anisotropic large-scale robust rotation averaging pipeline. The resulting algorithm achieves state-of-the-art performance on public structure-from-motion datasets. Project page: https://ylochman.github.io/acd