Power Bundle Adjustment for Large-Scale 3D Reconstruction

TL;DR

Power Bundle Adjustment introduces a novel inverse expansion method using power series to efficiently solve large-scale 3D reconstruction problems, outperforming existing iterative solvers in speed and accuracy.

Contribution

It presents a new family of inverse expansion solvers based on power series for large-scale bundle adjustment, with proven convergence and practical efficiency improvements.

Findings

Challenges state-of-the-art iterative methods in speed and accuracy

Significantly accelerates solution of the normal equation in large-scale problems

Enhances distributed bundle adjustment frameworks with improved performance

Abstract

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.