Power Bundle Adjustment for Large-Scale 3D Reconstruction
This work addresses computational bottlenecks in 3D reconstruction for applications like computer vision and robotics, offering an incremental improvement over existing iterative methods.
The authors tackled large-scale bundle adjustment problems in 3D reconstruction by introducing Power Bundle Adjustment, a new solver based on power series expansion, which accelerates solution of the normal equation and improves speed and accuracy in distributed optimization, as demonstrated on the BAL dataset.
We introduce Power Bundle Adjustment as an expansion type algorithm for solving large-scale bundle adjustment problems. It is based on the power series expansion of the inverse Schur complement and constitutes a new family of solvers that we call inverse expansion methods. We theoretically justify the use of power series and we prove the convergence of our approach. Using the real-world BAL dataset we show that the proposed solver challenges the state-of-the-art iterative methods and significantly accelerates the solution of the normal equation, even for reaching a very high accuracy. This easy-to-implement solver can also complement a recently presented distributed bundle adjustment framework. We demonstrate that employing the proposed Power Bundle Adjustment as a sub-problem solver significantly improves speed and accuracy of the distributed optimization.