Hybrid Rotation Averaging: A Globally Guaranteed Fast and Robust Rotation Averaging Approach
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We address rotation averaging and its application to real-world 3D reconstruction. Local optimisation based approaches are the defacto choice, though they only guarantee a local optimum. Global optimizers 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 global rotation averaging by formulating it as a semi-definite program that can be solved efficiently by applying the Burer-Monteiro method. Both memory and time requirements are thereby largely reduced through a low-rank factorisation. Combined with a fast view graph filtering as preprocessing, and a local optimiser as post-processing, the proposed hybrid approach is robust to outliers. Compared against state-of-the-art globally optimal methods, our approach is 1 2 orders of magnitude faster while maintaining the same or better accuracy. We apply the proposed hybrid rotation averaging approach to incremental Structure from Motion (SfM) by adding the resulting global rotations as regularizers to bundle adjustment. Overall, we demonstrate high practicality of the proposed method as bad camera poses are effectively corrected and drift is reduced.
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