Estimation of Camera Locations in Highly Corrupted Scenarios: All About that Base, No Shape Trouble
Published in CVPR, 2018
Recommended citation: Yunpeng Shi, Gilad Lerman. CVPR 2018.
Abstract
We propose a strategy for improving camera location estimation in structure from motion. Our setting assumes highly corrupted pairwise directions (i.e., normalized relative location vectors), so there is a clear room for improving current state-ofthe-art solutions for this problem. Our strategy identifies severely corrupted pairwise directions by using a geometric consistency condition. It then selects a cleaner set of pairwise directions as a preprocessing step for common solvers. We theoretically guarantee the successful performance of a basic version of our strategy under a synthetic corruption model. Numerical results on artificial and real data demonstrate the significant improvement obtained by our strategy.