ML Anthology

A Nine-Point Algorithm for Estimating Para-Catadioptric Fundamental Matrices

Christopher Geyer, Henrik Stewénius
CVPR 2007
doi:10.1109/CVPR.2007.383065 /cvpr/2007/geyer2007cvpr-nine/

Abstract

We present a minimal-point algorithm for finding fundamental matrices for catadioptric cameras of the parabolic type. Central catadioptric cameras-an optical combination of a mirror and a lens that yields an imaging device equivalent within hemispheres to perspective cameras-have found wide application in robotics, tele-immersion and providing enhanced situational awareness for remote operation. We use an uncalibrated structure-from-motion framework developed for these cameras to consider the problem of estimating the fundamental matrix for such cameras. We present a solution that can compute the para-catadioptirc fundamental matrix with nine point correspondences, the smallest number possible. We compare this algorithm to alternatives and show some results of using the algorithm in conjunction with random sample consensus (RANSAC).

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Cite

Text

Geyer and Stewénius. "A Nine-Point Algorithm for Estimating Para-Catadioptric Fundamental Matrices." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383065

Markdown

[Geyer and Stewénius. "A Nine-Point Algorithm for Estimating Para-Catadioptric Fundamental Matrices." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/geyer2007cvpr-nine/) doi:10.1109/CVPR.2007.383065

BibTeX

@inproceedings{geyer2007cvpr-nine,
  title     = {{A Nine-Point Algorithm for Estimating Para-Catadioptric Fundamental Matrices}},
  author    = {Geyer, Christopher and Stewénius, Henrik},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2007},
  doi       = {10.1109/CVPR.2007.383065},
  url       = {https://mlanthology.org/cvpr/2007/geyer2007cvpr-nine/}
}