Baseline Desensitizing In Translation Averaging

TL;DR

This paper introduces a simple, robust translation averaging method that is insensitive to camera baseline variations, employs efficient optimization, and outperforms existing algorithms in accuracy and speed.

Contribution

The authors propose a novel baseline-insensitive bilinear objective function and an efficient optimization scheme, improving translation averaging performance and understanding of convex algorithms.

Findings

Achieves superior accuracy on benchmark datasets

Runs several times faster than state-of-the-art methods

Effectively handles outliers with rotation-assisted IRLS

Abstract

Many existing translation averaging algorithms are either sensitive to disparate camera baselines and have to rely on extensive preprocessing to improve the observed Epipolar Geometry graph, or if they are robust against disparate camera baselines, require complicated optimization to minimize the highly nonlinear angular error objective. In this paper, we carefully design a simple yet effective bilinear objective function, introducing a variable to perform the requisite normalization. The objective function enjoys the baseline-insensitive property of the angular error and yet is amenable to simple and efficient optimization by block coordinate descent, with good empirical performance. A rotation-assisted Iterative Reweighted Least Squares scheme is further put forth to help deal with outliers. We also contribute towards a better understanding of the behavior of two recent convex algorithms, LUD and Shapefit/kick, clarifying the underlying subtle difference that leads to the performance gap. Finally, we demonstrate that our algorithm achieves overall superior accuracies in benchmark dataset compared to state-of-theart methods, and is also several times faster.