# Towards Rotation-only Imaging Geometry: Rotation Estimation

arXiv: https://arxiv.org/abs/2511.12415 · HTML: https://mlsanity.com/paper/2511.12415/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 16 Nov 2025.

## Builds on (most cited first, 8 of 8)

- [Global Structure-from-Motion Revisited](https://mlsanity.com/paper/2407.20219/) (2024)
- [Robust Camera Location Estimation by Convex Programming](https://mlsanity.com/paper/1412.0165/) (2014)
- [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)
- [Rotation-Only Bundle Adjustment](https://mlsanity.com/paper/2011.11724/) (2020)
- [Equivalent Constraints for Two-View Geometry: Pose Solution/Pure Rotation Identification and 3D Reconstruction](https://mlsanity.com/paper/1810.05863/) (2018)
- [The Chiral Domain of a Camera Arrangement](https://mlsanity.com/paper/2003.09265/) (2020)
- [Linear Relative Pose Estimation Founded on Pose-only Imaging Geometry](https://mlsanity.com/paper/2401.13357/) (2024)

## Abstract (arXiv)

Structure from Motion (SfM) is a critical task in computer vision, aiming to recover the 3D scene structure and camera motion from a sequence of 2D images. The recent pose-only imaging geometry decouples 3D coordinates from camera poses and demonstrates significantly better SfM performance through pose adjustment. Continuing the pose-only perspective, this paper explores the critical relationship between the scene structures, rotation and translation. Notably, the translation can be expressed in terms of rotation, allowing us to condense the imaging geometry representation onto the rotation manifold. A rotation-only optimization framework based on reprojection error is proposed for both two-view and multi-view scenarios. The experiment results demonstrate superior accuracy and robustness performance over the current state-of-the-art rotation estimation methods, even comparable to multiple bundle adjustment iteration results. Hopefully, this work contributes to even more accurate, efficient and reliable 3D visual computing.