Multidirectional Conjugate Gradients for Scalable Bundle Adjustment
This addresses scalability issues in 3D reconstruction, particularly for denser problems, but is incremental as it builds on classical preconditioned conjugate gradients.
The paper tackles the problem of large-scale bundle adjustment by proposing Multidirectional Conjugate Gradients, which accelerates the solution of the normal equation by up to 61%.
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.