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Optimizing Polynomial Solvers for Minimal Geometry Problems

Oleg Naroditsky, Kostas Daniilidis
ICCV 2011 pp. 975-982
doi:10.1109/ICCV.2011.6126341 /iccv/2011/naroditsky2011iccv-optimizing/

Abstract

In recent years polynomial solvers based on algebraic geometry techniques, and specifically the action matrix method, have become popular for solving minimal problems in computer vision. In this paper we develop a new method for reducing the computational time and improving numerical stability of algorithms using this method. To achieve this, we propose and prove a set of algebraic conditions which allow us to reduce the size of the elimination template (polynomial coefficient matrix), which leads to faster LU or QR decomposition. Our technique is generic and has potential to improve performance of many solvers that use the action matrix method. We demonstrate the approach on specific examples, including an image stitching algorithm where computation time is halved and single precision arithmetic can be used.

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Cite

Text

Naroditsky and Daniilidis. "Optimizing Polynomial Solvers for Minimal Geometry Problems." IEEE/CVF International Conference on Computer Vision, 2011. doi:10.1109/ICCV.2011.6126341

Markdown

[Naroditsky and Daniilidis. "Optimizing Polynomial Solvers for Minimal Geometry Problems." IEEE/CVF International Conference on Computer Vision, 2011.](https://mlanthology.org/iccv/2011/naroditsky2011iccv-optimizing/) doi:10.1109/ICCV.2011.6126341

BibTeX

@inproceedings{naroditsky2011iccv-optimizing,
  title     = {{Optimizing Polynomial Solvers for Minimal Geometry Problems}},
  author    = {Naroditsky, Oleg and Daniilidis, Kostas},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2011},
  pages     = {975-982},
  doi       = {10.1109/ICCV.2011.6126341},
  url       = {https://mlanthology.org/iccv/2011/naroditsky2011iccv-optimizing/}
}