提出快速鲁棒的旋转平均算法，解决大尺度三维重建中的误差累积问题。
Making Rotation Averaging Fast and Robust with Anisotropic Coordinate Descent
- 基于各向异性坐标下降法，简化优化流程提升速度。
- 在公开数据集上达到当前最优性能，误差显著低于已有方法。
- 适合大规模三维重建场景，尤其对初始化不敏感。
各向异性旋转平均是各向同性方法的自然拓展，通过将标准两视图优化得到的相对旋转不确定性传播至绝对旋转优化中，实现半定松弛求解全局最优，但随问题规模增长而计算缓慢。局部方法虽快且具鲁棒性，却依赖最小生成树结构，易受初始值影响，产生漂移并陷入次优解。本文分析一类最初用于优化弦距离的块坐标下降方法，推导出更简洁的公式及各向异性扩展，构建高效通用求解器，并集成至扩展的各向异性大规模鲁棒旋转平均流水线。所提算法在多个公开结构光运动数据集上表现优异，达到当前最佳性能。
原文摘要 · Abstract (English)
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
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