RPBA -- Robust Parallel Bundle Adjustment Based on Covariance Information

TL;DR

This paper introduces RPBA, a robust parallel bundle adjustment method leveraging covariance information from 3D point triangulation to improve convergence and robustness in large-scale SfM problems.

Contribution

The paper presents a novel covariance-informed parallel bundle adjustment approach that enhances convergence and robustness, avoiding parameter tuning issues of existing consensus methods.

Findings

Improved convergence behavior in parallel bundle adjustment.

Enhanced robustness against outliers and noise.

Competitive performance compared to state-of-the-art methods.

Abstract

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