# Linear Relative Pose Estimation Founded on Pose-only Imaging Geometry

arXiv: https://arxiv.org/abs/2401.13357 · HTML: https://mlsanity.com/paper/2401.13357/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 33 references that are in the map.
- First posted on arXiv 24 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [A Survey of Structure from Motion](https://mlsanity.com/paper/1701.08493/) (2017)
- [A Pose-only Solution to Visual Reconstruction and Navigation](https://mlsanity.com/paper/2103.01530/) (2021)
- [An Efficient Solution to Non-Minimal Case Essential Matrix Estimation](https://mlsanity.com/paper/1903.09067/) (2019)
- [Equivalent Constraints for Two-View Geometry: Pose Solution/Pure Rotation Identification and 3D Reconstruction](https://mlsanity.com/paper/1810.05863/) (2018)

## Built on by (most cited first, 2 of 2)

- [Towards Rotation-only Imaging Geometry: Rotation Estimation](https://mlsanity.com/paper/2511.12415/) (2025)
- [Non-Minimal Sampling and Consensus for Prohibitively Large Datasets](https://mlsanity.com/paper/2604.22518/) (2026)

## Abstract (arXiv)

How to efficiently and accurately handle image matching outliers is a critical issue in two-view relative estimation. The prevailing RANSAC method necessitates that the minimal point pairs be inliers. This paper introduces a linear relative pose estimation algorithm for n ( n ≥ 6) point pairs, which is founded on the recent pose-only imaging geometry to filter out outliers by proper reweighting. The proposed algorithm is able to handle planar degenerate scenes, and enhance robustness and accuracy in the presence of a substantial ratio of outliers. Specifically, we embed the linear global translation (LiGT) constraint into the strategies of iteratively reweighted least-squares (IRLS) and RANSAC so as to realize robust outlier removal. Simulations and real tests of the Strecha dataset show that the proposed algorithm achieves relative rotation accuracy improvement of 2 ~ 10 times in face of as large as 80% outliers.