Semidefinite Relaxations for Robust Multiview Triangulation

Linus Härenstam-Nielsen, Niclas Zeller, Daniel Cremers

CVPR 2023 pp. 749-757

doi:10.1109/CVPR52729.2023.00079 /cvpr/2023/harenstamnielsen2023cvpr-semidefinite/

Abstract

We propose an approach based on convex relaxations for certifiably optimal robust multiview triangulation. To this end, we extend existing relaxation approaches to non-robust multiview triangulation by incorporating a least squares cost function. We propose two formulations, one based on epipolar constraints and one based on fractional reprojection constraints. The first is lower dimensional and remains tight under moderate noise and outlier levels, while the second is higher dimensional and therefore slower but remains tight even under extreme noise and outlier levels. We demonstrate through extensive experiments that the proposed approaches allow us to compute provably optimal reconstructions even under significant noise and a large percentage of outliers.

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Cite

Text

Härenstam-Nielsen et al. "Semidefinite Relaxations for Robust Multiview Triangulation." Conference on Computer Vision and Pattern Recognition, 2023. doi:10.1109/CVPR52729.2023.00079

Markdown

[Härenstam-Nielsen et al. "Semidefinite Relaxations for Robust Multiview Triangulation." Conference on Computer Vision and Pattern Recognition, 2023.](https://mlanthology.org/cvpr/2023/harenstamnielsen2023cvpr-semidefinite/) doi:10.1109/CVPR52729.2023.00079

BibTeX

@inproceedings{harenstamnielsen2023cvpr-semidefinite,
  title     = {{Semidefinite Relaxations for Robust Multiview Triangulation}},
  author    = {Härenstam-Nielsen, Linus and Zeller, Niclas and Cremers, Daniel},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2023},
  pages     = {749-757},
  doi       = {10.1109/CVPR52729.2023.00079},
  url       = {https://mlanthology.org/cvpr/2023/harenstamnielsen2023cvpr-semidefinite/}
}