# Rotation-Only Bundle Adjustment

arXiv: https://arxiv.org/abs/2011.11724 · HTML: https://mlsanity.com/paper/2011.11724/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2020.

## Built on by (most cited first, 12 of 12)

- [HARA: A Hierarchical Approach for Robust Rotation Averaging](https://mlsanity.com/paper/2111.08831/) (2021)
- [Full-DoF Egomotion Estimation for Event Cameras Using Geometric Solvers](https://mlsanity.com/paper/2503.03307/) (2025)
- [The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions](https://mlsanity.com/paper/2204.02256/) (2022)
- [Instant Visual Odometry Initialization for Mobile AR](https://mlsanity.com/paper/2107.14659/) (2021)
- [PoserNet: Refining Relative Camera Poses Exploiting Object Detections](https://mlsanity.com/paper/2207.09445/) (2022)
- [What's Wrong with the Absolute Trajectory Error?](https://mlsanity.com/paper/2212.05376/) (2022)
- [Alignment Scores: Robust Metrics for Multiview Pose Accuracy Evaluation](https://mlsanity.com/paper/2407.20391/) (2024)
- [RAUM-VO: Rotational Adjusted Unsupervised Monocular Visual Odometry](https://mlsanity.com/paper/2203.07162/) (2022)
- [A Pose-only Geometric Constraint for Multi-Camera Pose Adjustment](https://mlsanity.com/paper/2604.23704/) (2026)
- [VOCA: Visual Odometry with Codec Awareness](https://mlsanity.com/paper/2607.00189/) (2026)
- [PRAGO: Differentiable Multi-View Pose Optimization From Objectness Detections](https://mlsanity.com/paper/2403.08586/) (2024)
- [Towards Rotation-only Imaging Geometry: Rotation Estimation](https://mlsanity.com/paper/2511.12415/) (2025)

## Abstract (arXiv)

We propose a novel method for estimating the global rotations of the cameras independently of their positions and the scene structure. When two calibrated cameras observe five or more of the same points, their relative rotation can be recovered independently of the translation. We extend this idea to multiple views, thereby decoupling the rotation estimation from the translation and structure estimation. Our approach provides several benefits such as complete immunity to inaccurate translations and structure, and the accuracy improvement when used with rotation averaging. We perform extensive evaluations on both synthetic and real datasets, demonstrating consistent and significant gains in accuracy when used with the state-of-the-art rotation averaging method.