IRA plus plus : Distributed Incremental Rotation Averaging

By observing that the recently presented Incremental Rotation Averaging (IRA) suffers from drifting and efficiency problems in large-scale situations, it is upgraded in this work to possess stronger scalability in both accuracy and efficiency based on the thought of divide and conquer. This upgraded version is termed as IRA++. Specifically, the original Epipolar-geometry Graph (EG) is clustered into several sub-graphs and inner-rotation averaging is distributedly performed in each of them with IRA at first. Then, the relative rotation between each pair of inner-sub-EG coordinate systems is distributedly estimated by a voting-based single rotation averaging method. Subsequently, IRA-based inter-rotation averaging is performed to obtain the absolute rotation of each inner-sub-EG coordinate system. And finally, the absolute rotations of all the cameras in the original EG are globally aligned and optimized to get the final rotation averaging result. Comprehensive evaluations on the 1DSfM, Campus, and San Francisco datasets demonstrate the advantages of our proposed IRA++ over IRA and several other state-of-the-art rotation averaging methods in both efficiency and accuracy, especially the accuracy in noise-polluted and efficiency in large-scale situations.

Automated summary

This work presents an upgraded version of Incremental Rotation Averaging (IRA) called IRA++, which improves the scalability in both accuracy and efficiency by dividing the original Epipolar-geometry Graph (EG) into sub-graphs and performing distributed inner-rotation averaging. The evaluation results on various datasets demonstrate that IRA++ outperforms IRA and other state-of-the-art rotation averaging methods in terms of accuracy and efficiency, especially in large-scale and noise-polluted situations.