Computational Imaging on the Electric Grid

Mark Sheinin, Yoav Y. Schechner, Kiriakos N. Kutulakos

CVPR 2017

doi:10.1109/CVPR.2017.254 /cvpr/2017/sheinin2017cvpr-computational/

Abstract

Night beats with alternating current (AC) illumination. By passively sensing this beat, we reveal new scene information which includes: the type of bulbs in the scene, the phases of the electric grid up to city scale, and the light transport matrix. This information yields unmixing of reflections and semi-reflections, nocturnal high dynamic range, and scene rendering with bulbs not observed during acquisition. The latter is facilitated by a database of bulb response functions for a range of sources, which we collected and provide. To do all this, we built a novel coded-exposure high-dynamic-range imaging technique, specifically designed to operate on the grid's AC lighting.

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Cite

Text

Sheinin et al. "Computational Imaging on the Electric Grid." Conference on Computer Vision and Pattern Recognition, 2017. doi:10.1109/CVPR.2017.254

Markdown

[Sheinin et al. "Computational Imaging on the Electric Grid." Conference on Computer Vision and Pattern Recognition, 2017.](https://mlanthology.org/cvpr/2017/sheinin2017cvpr-computational/) doi:10.1109/CVPR.2017.254

BibTeX

@inproceedings{sheinin2017cvpr-computational,
  title     = {{Computational Imaging on the Electric Grid}},
  author    = {Sheinin, Mark and Schechner, Yoav Y. and Kutulakos, Kiriakos N.},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2017},
  doi       = {10.1109/CVPR.2017.254},
  url       = {https://mlanthology.org/cvpr/2017/sheinin2017cvpr-computational/}
}