# Bundle Adjustment Revisited

arXiv: https://arxiv.org/abs/1912.03858 · HTML: https://mlsanity.com/paper/1912.03858/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2019.

## Built on by (most cited first, 7 of 7)

- [Orbeez-SLAM: A Real-time Monocular Visual SLAM with ORB Features and NeRF-realized Mapping](https://mlsanity.com/paper/2209.13274/) (2022)
- [POMATO: Marrying Pointmap Matching with Temporal Motion for Dynamic 3D Reconstruction](https://mlsanity.com/paper/2504.05692/) (2025)
- [RL-PGO: Reinforcement Learning-based Planar Pose-Graph Optimization](https://mlsanity.com/paper/2202.13221/) (2022)
- [Exploring the Adjugate Matrix Approach to Quaternion Pose Extraction](https://mlsanity.com/paper/2205.09116/) (2022)
- [Quantum Levenberg--Marquardt Algorithm for optimization in Bundle Adjustment](https://mlsanity.com/paper/2203.02311/) (2022)
- [GoToNet: Fast Monocular Scene Exposure and Exploration](https://mlsanity.com/paper/2206.05967/) (2022)
- [Semantic Validation in Structure from Motion](https://mlsanity.com/paper/2304.02420/) (2023)

## Abstract (arXiv)

3D reconstruction has been developing all these two decades, from moderate to medium size and to large scale. It's well known that bundle adjustment plays an important role in 3D reconstruction, mainly in Structure from Motion(SfM) and Simultaneously Localization and Mapping(SLAM). While bundle adjustment optimizes camera parameters and 3D points as a non-negligible final step, it suffers from memory and efficiency requirements in very large scale reconstruction. In this paper, we study the development of bundle adjustment elaborately in both conventional and distributed approaches. The detailed derivation and pseudo code are also given in this paper.