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We introduce Power Bundle Adjustment as an expansion type algorithm forsolving large-scale bundle adjustment problems. It is based on the power seriesexpansion of the inverse Schur complement and constitutes a new family ofsolvers that we call inverse expansion methods. We theoretically justify theuse of power series and we prove the convergence of our approach. Using thereal-world BAL dataset we show that the proposed solver challenges thestate-of-the-art iterative methods and significantly accelerates the solutionof the normal equation, even for reaching a very high accuracy. Thiseasy-to-implement solver can also complement a recently presented distributedbundle adjustment framework. We demonstrate that employing the proposed PowerBundle Adjustment as a sub-problem solver significantly improves speed andaccuracy of the distributed optimization.

Power Bundle Adjustment for Large-Scale 3D Reconstruction