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

arXiv: https://arxiv.org/abs/1804.02591 · HTML: https://mlsanity.com/paper/1804.02591/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2018.

## Built on by (most cited first, 6 of 6)

- [Efficient Detection of Long Consistent Cycles and its Application to Distributed Synchronization](https://mlsanity.com/paper/2407.04260/) (2024)
- [Cycle-Sync: Robust Global Camera Pose Estimation through Enhanced Cycle-Consistent Synchronization](https://mlsanity.com/paper/2511.02329/) (2025)
- [A Taxonomy of Structure from Motion Methods](https://mlsanity.com/paper/2505.15814/) (2025)
- [Higher-Order Group Synchronization](https://mlsanity.com/paper/2505.21932/) (2025)
- [TriDE: Triangle-Consistent Translation Directions for Global Camera Pose Estimation](https://mlsanity.com/paper/2605.06889/) (2026)
- [TriP: A Triangle Puzzle Approach to Robust Translation Averaging](https://mlsanity.com/paper/2605.07143/) (2026)

## Abstract (arXiv)

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.