# RPBA -- Robust Parallel Bundle Adjustment Based on Covariance Information

arXiv: https://arxiv.org/abs/1910.08138 · HTML: https://mlsanity.com/paper/1910.08138/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment](https://mlsanity.com/paper/2112.01349/) (2021)
- [On-the-Fly SfM: What you capture is What you get](https://mlsanity.com/paper/2309.11883/) (2023)
- [Pointless Global Bundle Adjustment With Relative Motions Hessians](https://mlsanity.com/paper/2304.05118/) (2023)

## Abstract (arXiv)

A core component of all Structure from Motion (SfM) approaches is bundle adjustment. As the latter is a computational bottleneck for larger blocks, parallel bundle adjustment has become an active area of research. Particularly, consensus-based optimization methods have been shown to be suitable for this task. We have extended them using covariance information derived by the adjustment of individual three-dimensional (3D) points, i.e., "triangulation" or "intersection". This does not only lead to a much better convergence behavior, but also avoids fiddling with the penalty parameter of standard consensus-based approaches. The corresponding novel approach can also be seen as a variant of resection / intersection schemes, where we adjust during intersection a number of sub-blocks directly related to the number of threads available on a computer each containing a fraction of the cameras of the block. We show that our novel approach is suitable for robust parallel bundle adjustment and demonstrate its capabilities in comparison to the basic consensus-based approach as well as a state-of-the-art parallel implementation of bundle adjustment. Code for our novel approach is available on GitHub: https://github.com/helmayer/RPBA