  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.
