  We address rotation averaging (RA) and its application to real-world 3D
reconstruction. Local optimisation based approaches are the de facto choice,
though they only guarantee a local optimum. Global optimisers ensure global
optimality in low noise conditions, but they are inefficient and may easily
deviate under the influence of outliers or elevated noise levels. We push the
envelope of rotation averaging by leveraging the advantages of a global RA
method and a local RA method. Combined with a fast view graph filtering as
preprocessing, the proposed hybrid approach is robust to outliers. We further
apply the proposed hybrid rotation averaging approach to incremental Structure
from Motion (SfM), the accuracy and robustness of SfM are both improved by
adding the resulting global rotations as regularisers to bundle adjustment.
Overall, we demonstrate high practicality of the proposed method as bad camera
poses are effectively corrected and drift is reduced.
