Estimation of Camera Locations in Highly Corrupted Scenarios: All About that Base, No Shape Trouble

TL;DR

This paper introduces a robust method for estimating camera locations in structure from motion, effectively identifying and removing highly corrupted pairwise directions to improve accuracy in challenging scenarios.

Contribution

It presents a novel strategy that detects and filters severely corrupted data using geometric consistency, enhancing existing camera location estimation methods.

Findings

Significant improvement over existing methods on artificial data

Effective detection of corrupted pairwise directions

Theoretical guarantees under synthetic corruption models

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-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.