Globally Optimal DLS Method for PnP Problem with Cayley parameterization

Gaku Nakano

Abstract

This paper proposes a globally optimal direct least squares (DLS) method for the PnP problem with Cayley parameterization. First we derive a new optimality condition without Lagrange multipliers, which is independent of any rotation representations. Then, we show that the new equation can be solved by several types of parameterizations and among them, Cayley parameterization is the most efficient. According to the experimental results, the proposed method represented by Cayley parameterization is more than three times faster than the state-of-the-art method while maintaining equivalent accuracy.

Session

Poster 1

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DOI

10.5244/C.29.78
https://dx.doi.org/10.5244/C.29.78

Citation

Gaku Nakano. Globally Optimal DLS Method for PnP Problem with Cayley parameterization. In Xianghua Xie, Mark W. Jones, and Gary K. L. Tam, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 78.1-78.11. BMVA Press, September 2015.

Bibtex

@inproceedings{BMVC2015_78,
	title={Globally Optimal DLS Method for PnP Problem with Cayley parameterization},
	author={Gaku Nakano},
	year={2015},
	month={September},
	pages={78.1-78.11},
	articleno={78},
	numpages={11},
	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.29.78},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.78}
}