# Stochastic Bundle Adjustment for Efficient and Scalable 3D Reconstruction

arXiv: https://arxiv.org/abs/2008.00446 · HTML: https://mlsanity.com/paper/2008.00446/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2020.

## Built on by (most cited first, 16 of 16)

- [LU-NeRF: Scene and Pose Estimation by Synchronizing Local Unposed NeRFs](https://mlsanity.com/paper/2306.05410/) (2023)
- [Square Root Bundle Adjustment for Large-Scale Reconstruction](https://mlsanity.com/paper/2103.01843/) (2021)
- [MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment](https://mlsanity.com/paper/2112.01349/) (2021)
- [Power Variable Projection for Initialization-Free Large-Scale Bundle Adjustment](https://mlsanity.com/paper/2405.05079/) (2024)
- [Distributed Global Structure-from-Motion with a Deep Front-End](https://mlsanity.com/paper/2311.18801/) (2023)
- [Efficient Second-Order Plane Adjustment](https://mlsanity.com/paper/2211.11542/) (2022)
- [A Game of Bundle Adjustment -- Learning Efficient Convergence](https://mlsanity.com/paper/2308.13270/) (2023)
- [Decentralization and Acceleration Enables Large-Scale Bundle Adjustment](https://mlsanity.com/paper/2305.07026/) (2023)
- [Distributed bundle adjustment with block-based sparse matrix compression for super large scale datasets](https://mlsanity.com/paper/2307.08383/) (2023)
- [A Taxonomy of Structure from Motion Methods](https://mlsanity.com/paper/2505.15814/) (2025)
- [Multidirectional Conjugate Gradients for Scalable Bundle Adjustment](https://mlsanity.com/paper/2110.04015/) (2021)
- [Keeping Less is More: Point Sparsification for Visual SLAM](https://mlsanity.com/paper/2207.00225/) (2022)
- [Incremental Rotation Averaging Revisited](https://mlsanity.com/paper/2309.16924/) (2023)
- [A Pose-only Geometric Constraint for Multi-Camera Pose Adjustment](https://mlsanity.com/paper/2604.23704/) (2026)
- [Hierarchical Segment-based Optimization for SLAM](https://mlsanity.com/paper/2111.04101/) (2021)
- [CSS-BA: Gate-Guided Column Space Search for Bundle Adjustment](https://mlsanity.com/paper/2607.15652/) (2026)

## Abstract (arXiv)

Current bundle adjustment solvers such as the Levenberg-Marquardt (LM) algorithm are limited by the bottleneck in solving the Reduced Camera System (RCS) whose dimension is proportional to the camera number. When the problem is scaled up, this step is neither efficient in computation nor manageable for a single compute node. In this work, we propose a stochastic bundle adjustment algorithm which seeks to decompose the RCS approximately inside the LM iterations to improve the efficiency and scalability. It first reformulates the quadratic programming problem of an LM iteration based on the clustering of the visibility graph by introducing the equality constraints across clusters. Then, we propose to relax it into a chance constrained problem and solve it through sampled convex program. The relaxation is intended to eliminate the interdependence between clusters embodied by the constraints, so that a large RCS can be decomposed into independent linear sub-problems. Numerical experiments on unordered Internet image sets and sequential SLAM image sets, as well as distributed experiments on large-scale datasets, have demonstrated the high efficiency and scalability of the proposed approach. Codes are released at https://github.com/zlthinker/STBA.