  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.
