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arXiv · 2110.04015

Multidirectional Conjugate Gradients for Scalable Bundle Adjustment

Simon Weber·Nikolaus Demmel·Daniel Cremers
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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.

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Simon Weber, Nikolaus Demmel, Daniel Cremers. 2021-10-08. Multidirectional Conjugate Gradients for Scalable Bundle Adjustment. https://arxiv.org/abs/2110.04015

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