Multidirectional Conjugate Gradients for Scalable Bundle Adjustment - Weber, Simon; Demmel, Nikolaus; Cremers, Daniel (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021)
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.
CRIS-Team Scopus Importer:2023-03-08
Keywords: Bundle adjustment; Large-scale reconstruction; Preconditioned conjugate gradients