Making Rotation Averaging Fast and Robust with Anisotropic Coordinate Descent
This work addresses a domain-specific problem in computer vision for structure-from-motion, offering an incremental improvement in efficiency and robustness.
The paper tackled the problem of anisotropic rotation averaging, which scales poorly with problem size and is sensitive to initialization, by developing a fast and robust solver based on block coordinate descent. The resulting algorithm achieved state-of-the-art performance on public structure-from-motion datasets.
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