Welcome to the official page of the paper Power Bundle Adjustment for Large-Scale 3D Reconstruction (CVPR 2023). You can find a video presentation here.
The official implementation of PoBA is available here.
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 acceleates 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.
If you find our work useful in your research, please consider citing:
@inproceedings{weber2023poba,
author = {Simon Weber and Nikolaus Demmel and Tin Chon Chan and Daniel Cremers},
title = {Power Bundle Adjustment for Large-Scale 3D Reconstruction},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2023}
}