  In this paper, we revisit the rotation averaging problem applied in global
Structure-from-Motion pipelines. We argue that the main problem of current
methods is the minimized cost function that is only weakly connected with the
input data via the estimated epipolar geometries.We propose to better model the
underlying noise distributions by directly propagating the uncertainty from the
point correspondences into the rotation averaging. Such uncertainties are
obtained for free by considering the Jacobians of two-view refinements.
Moreover, we explore integrating a variant of the MAGSAC loss into the rotation
averaging problem, instead of using classical robust losses employed in current
frameworks. The proposed method leads to results superior to baselines, in
terms of accuracy, on large-scale public benchmarks. The code is public.
https://github.com/zhangganlin/GlobalSfMpy
