Distributed Bundle Adjustment

TL;DR

This paper introduces a distributed bundle adjustment algorithm using ADMM that improves scalability and robustness, enabling large-scale structure from motion tasks to be performed efficiently in distributed environments.

Contribution

The paper presents a novel distributed BA algorithm based on ADMM, addressing scalability and distribution limitations of existing methods.

Findings

Comparable accuracy to centralized methods on synthetic and real data

Linear runtime scaling with the number of observed points

Robust formulations enhance performance in distributed settings

Abstract

Most methods for Bundle Adjustment (BA) in computer vision are either centralized or operate incrementally. This leads to poor scaling and affects the quality of solution as the number of images grows in large scale structure from motion (SfM). Furthermore, they cannot be used in scenarios where image acquisition and processing must be distributed. We address this problem with a new distributed BA algorithm. Our distributed formulation uses alternating direction method of multipliers (ADMM), and, since each processor sees only a small portion of the data, we show that robust formulations improve performance. We analyze convergence of the proposed algorithm, and illustrate numerical performance, accuracy of the parameter estimates, and scalability of the distributed implementation in the context of synthetic 3D datasets with known camera position and orientation ground truth. The results are comparable to an alternate state-of-the-art centralized bundle adjustment algorithm on synthetic and real 3D reconstruction problems. The runtime of our implementation scales linearly with the number of observed points.