ML Anthology

A Probabilistic Approach to Camera Pose and Calibration from a Small Set of Point and Line Correspondences

Thomas Chaperon, Jacques Droulez, Guillaume Thibault
ICCVW 2009 pp. 1678-1685
doi:10.1109/ICCVW.2009.5457485 /iccvw/2009/chaperon2009iccvw-probabilistic/

Abstract

We present a new method for solving the problem of camera pose and calibration from a limited number of correspondences between noisy 2D and 3D features. We show that the probabilistic estimation problem can be expressed as a partially linear problem, where point and line correspondences are mixed using a common formulation. Our Sampling-Solving algorithm enables to robustly estimate the parameters and evaluate the probability distribution of the estimated parameters. It solves the problem of pose estimation with unknown focal length using a minimum of only four correspondences (five if the principal point is also unknown). To our knowledge, this is the first calibration method using so few correspondences of both points and lines. Experimental results show that the algorithm is very robust to Gaussian noise, even for minimal data sets. Finally, some tests show the potential of global uncertainty estimates on real data sets.

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Cite

Text

Chaperon et al. "A Probabilistic Approach to Camera Pose and Calibration from a Small Set of Point and Line Correspondences." IEEE/CVF International Conference on Computer Vision Workshops, 2009. doi:10.1109/ICCVW.2009.5457485

Markdown

[Chaperon et al. "A Probabilistic Approach to Camera Pose and Calibration from a Small Set of Point and Line Correspondences." IEEE/CVF International Conference on Computer Vision Workshops, 2009.](https://mlanthology.org/iccvw/2009/chaperon2009iccvw-probabilistic/) doi:10.1109/ICCVW.2009.5457485

BibTeX

@inproceedings{chaperon2009iccvw-probabilistic,
  title     = {{A Probabilistic Approach to Camera Pose and Calibration from a Small Set of Point and Line Correspondences}},
  author    = {Chaperon, Thomas and Droulez, Jacques and Thibault, Guillaume},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2009},
  pages     = {1678-1685},
  doi       = {10.1109/ICCVW.2009.5457485},
  url       = {https://mlanthology.org/iccvw/2009/chaperon2009iccvw-probabilistic/}
}