论文标题
Shonan旋转平均：通过冲浪$ SO（P）^n $进行全球最优性
Shonan Rotation Averaging: Global Optimality by Surfing $SO(p)^n$
论文作者
论文摘要
Shonan旋转平均是一种快速，简单，优雅的旋转平均算法，可以保证在测量噪声中的轻度假设下恢复全球最佳解决方案。我们的方法采用半决赛放松，以恢复旋转平均问题的全球最佳解决方案。与先前的工作相反，我们展示了如何使用在（仅略微）较高维的旋转歧管上最小化这些松弛的大规模实例，从而重复使用现有的高性能（但局部）结构 - 动作管道。因此，我们的方法保留了当前SFM方法的速度和可扩展性，同时恢复了全球最佳解决方案。
Shonan Rotation Averaging is a fast, simple, and elegant rotation averaging algorithm that is guaranteed to recover globally optimal solutions under mild assumptions on the measurement noise. Our method employs semidefinite relaxation in order to recover provably globally optimal solutions of the rotation averaging problem. In contrast to prior work, we show how to solve large-scale instances of these relaxations using manifold minimization on (only slightly) higher-dimensional rotation manifolds, re-using existing high-performance (but local) structure-from-motion pipelines. Our method thus preserves the speed and scalability of current SFM methods, while recovering globally optimal solutions.
