Square Root Bundle Adjustment for Large-Scale Reconstruction
Demmel N, Sommer C, Cremers D, Usenko V (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: IEEE Computer Society
Pages Range: 11718-11727
Conference Proceedings Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Event location: Virtual, Online, USA
ISBN: 9781665445092
DOI: 10.1109/CVPR46437.2021.01155
Abstract
We propose a new formulation for the bundle adjustment problem which relies on nullspace marginalization of landmark variables by QR decomposition. Our approach, which we call square root bundle adjustment, is algebraically equivalent to the commonly used Schur complement trick, improves the numeric stability of computations, and allows for solving large-scale bundle adjustment problems with single-precision floating-point numbers. We show in real-world experiments with the BAL datasets that even in single precision the proposed solver achieves on average equally accurate solutions compared to Schur complement solvers using double precision. It runs significantly faster, but can require larger amounts of memory on dense problems. The proposed formulation relies on simple linear algebra operations and opens the way for efficient implementations of bundle adjustment on hardware platforms optimized for single-precision linear algebra processing.
Involved external institutions
Germany (DE)How to cite
APA:
Demmel, N., Sommer, C., Cremers, D., & Usenko, V. (2021). Square Root Bundle Adjustment for Large-Scale Reconstruction. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 11718-11727). Virtual, Online, USA: IEEE Computer Society.
MLA:
Demmel, Nikolaus, et al. "Square Root Bundle Adjustment for Large-Scale Reconstruction." Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021, Virtual, Online, USA IEEE Computer Society, 2021. 11718-11727.
BibTeX: Download