Multidirectional Conjugate Gradients for Scalable Bundle Adjustment

TL;DR

This paper introduces Multidirectional Conjugate Gradients, a novel method that accelerates large-scale bundle adjustment by expanding the search space, resulting in up to 61% faster solutions especially for dense problems.

Contribution

The paper presents a new multidirectional conjugate gradient technique that improves convergence speed in large-scale bundle adjustment tasks.

Findings

Achieves up to 61% acceleration in solution time.

Robust to hyper-parameter variations.

Effective for dense reconstruction problems.

Abstract

We revisit the problem of large-scale bundle adjustment and propose a technique called Multidirectional Conjugate Gradients that accelerates the solution of the normal equation by up to 61%. The key idea is that we enlarge the search space of classical preconditioned conjugate gradients to include multiple search directions. As a consequence, the resulting algorithm requires fewer iterations, leading to a significant speedup of large-scale reconstruction, in particular for denser problems where traditional approaches notoriously struggle. We provide a number of experimental ablation studies revealing the robustness to variations in the hyper-parameters and the speedup as a function of problem density.