Making Rotation Averaging Fast and Robust with Anisotropic Coordinate Descent

TL;DR

This paper introduces a fast, robust anisotropic rotation averaging algorithm that balances optimality and efficiency, improving large-scale structure-from-motion results.

Contribution

It develops a simplified anisotropic coordinate descent method integrated into a robust pipeline, advancing rotation averaging techniques.

Findings

Achieves state-of-the-art performance on public datasets.

Balances robustness, efficiency, and optimality in rotation averaging.

Provides a scalable solution for large-scale problems.

Abstract

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