PnP Toolbox

This toolbox is an extension of the toolbox provided by the authors of CEPPnP and OPnP (see below). We extended it to show the use of MLPnP. If you use this file it would be neat to cite our paper:

@INPROCEEDINGS {mlpnp2016,
  title={MLPNP - A REAL-TIME MAXIMUM LIKELIHOOD SOLUTION TO THE PERSPECTIVE-N-POINT PROBLEM},
  author={Urban, Steffen and Leitloff, Jens and Hinz, Stefan},
  booktitle={ISPRS Annals of Photogrammetry, Remote Sensing \& Spatial Information Sciences},
  pages={131-138},
  year={2016},
  volume={3}
}

Scripts to produce the synthetic results:

main_ordinary_3d.m
main_planar.m
main_ordinary_3d_sigma.m
main_planar_sigma.m
main_ordinary_3d_time.m
main_planar_time.m

Acknowledgements

This toolbox is based on the toolbox provided in:

Luis Ferraz, Xavier Binefa, Francesc Moreno-Noguer. Leveraging Feature Uncertainty in the PnP Problem. In Proceedings of BMVC, 2014.

Luis Ferraz, Xavier Binefa, Francesc Moreno-Noguer. Very Fast Solution to the PnP Problem with Algebraic Outlier Rejection. In Proceedings of CVPR, 2014.

Y. Zheng, Y. Kuang, S. Sugimoto, K. Astro ?m, and M. Okutomi. Revisiting the pnp problem: A fast, general and opti- mal solution. In ICCV, pages 4321?4328, 2013.

Y. Zheng, S. Sugimoto, and M. Okutomi. Aspnp: An accurate and scalable solution to the perspective-n-point problem. Trans. on Information and Systems, 96(7):1525?1535, 2013.

urbste/MLPnP_matlab_toolbox

PnP Toolbox

Scripts to produce the synthetic results:

Acknowledgements