Estimation of Camera Locations in Highly Corrupted Scenarios: All About that Base, No Shape Trouble
This work addresses a specific challenge in computer vision for 3D reconstruction, but it is incremental as it builds on existing solvers with a preprocessing step.
The paper tackles the problem of camera location estimation in structure from motion under highly corrupted pairwise directions by identifying and removing corrupted data using a geometric consistency condition, resulting in significant improvement in performance as demonstrated by numerical results on artificial and real data.
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-of-the-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.