Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization
Abstract
We recover a video of the motion taking place in a hidden scene by observing changes in indirect illumination in a nearby uncalibrated visible region. We solve this problem by factoring the observed video into a matrix product between the unknown hidden scene video and an unknown light transport matrix. This task is extremely ill-posed, as any non-negative factorization will satisfy the data. Inspired by recent work on the Deep Image Prior, we parameterize the factor matrices using randomly initialized convolutional neural networks trained in a one-off manner, and show that this results in decompositions that reflect the true motion in the hidden scene.
Cite
Text
Aittala et al. "Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization." Neural Information Processing Systems, 2019.Markdown
[Aittala et al. "Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization." Neural Information Processing Systems, 2019.](https://mlanthology.org/neurips/2019/aittala2019neurips-computational/)BibTeX
@inproceedings{aittala2019neurips-computational,
title = {{Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization}},
author = {Aittala, Miika and Sharma, Prafull and Murmann, Lukas and Yedidia, Adam and Wornell, Gregory and Freeman, Bill and Durand, Fredo},
booktitle = {Neural Information Processing Systems},
year = {2019},
pages = {14311-14321},
url = {https://mlanthology.org/neurips/2019/aittala2019neurips-computational/}
}