Provably-Convergent Iterative Methods for Projective Structure from Motion

Shyjan Mahamud, Martial Hebert, Yasuhiro Omori, Jean Ponce

CVPR 2001 pp. I:1018-1025

doi:10.1109/CVPR.2001.990642 /cvpr/2001/mahamud2001cvpr-provably/

Abstract

The estimation of the projective structure of a scene from image correspondences can be formulated as the minimization of the mean-squared distance between predicted and observed image points with respect to the projection matrices, the scene point positions, and their depths. Since these unknowns are not independent, constraints must be chosen to ensure that the optimization process. is well posed. This paper examines three plausible choices, and shows that the first one leads to the Sturm-Triggs projective factorization algorithm, while the other two lead to new provably-convergent approaches. Experiments with synthetic and real data are used to compare the proposed techniques to the Sturm-Triggs algorithm and bundle adjustment.

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Text

Mahamud et al. "Provably-Convergent Iterative Methods for Projective Structure from Motion." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001. doi:10.1109/CVPR.2001.990642

Markdown

[Mahamud et al. "Provably-Convergent Iterative Methods for Projective Structure from Motion." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001.](https://mlanthology.org/cvpr/2001/mahamud2001cvpr-provably/) doi:10.1109/CVPR.2001.990642

BibTeX

@inproceedings{mahamud2001cvpr-provably,
  title     = {{Provably-Convergent Iterative Methods for Projective Structure from Motion}},
  author    = {Mahamud, Shyjan and Hebert, Martial and Omori, Yasuhiro and Ponce, Jean},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2001},
  pages     = {I:1018-1025},
  doi       = {10.1109/CVPR.2001.990642},
  url       = {https://mlanthology.org/cvpr/2001/mahamud2001cvpr-provably/}
}