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 [20]
and Shapefit/kick [9], 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