Camera Pose and Focal Length Estimation Using Regularized Distance Constraints

Ekaterina Kanaeva, Lev Gurevich and Alexander Vakhitov

Abstract

We propose a new method for camera pose estimation with unknown focal length (PnPf problem). We combine projection equations and distance constraints in a single statistically significant cost function in the form of least squares. We fix the space of the search as a linear combination of several right singular vectors of the least squares system matrix. We use linear programming techniques to find feasible solutions faster. Then we do nonlinear refinement with Levenberg-Marquardt. Numerical experiments demonstrated that the method is faster than the state-of-the-art methods for point numbers up to several hundreds, and real-life structure-from-motion demonstrates its applicability for models having $10^3$-$10^5$ points. It has the same accuracy of estimates as the the state-of-the-art methods. We show that the method offers a tradeoff between speed and accuracy, allowing the estimation to run several times faster while slightly increasing the mean reprojection error.

Session

Poster 2

Files

Extended Abstract (PDF, 119K)
Paper (PDF, 8M)

DOI

10.5244/C.29.162
https://dx.doi.org/10.5244/C.29.162

Citation

Bibtex

@inproceedings{BMVC2015_162,
	title={Camera Pose and Focal Length Estimation Using Regularized Distance Constraints},
	author={Ekaterina Kanaeva and Lev Gurevich and Alexander Vakhitov},
	year={2015},
	month={September},
	pages={162.1-162.12},
	articleno={162},
	numpages={12},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.162},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.162}
}