Computational mirrors: Blind inverse light transport by deep matrix factorization
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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.
Date issued
2019Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
Advances in Neural Information Processing Systems
Publisher
Morgan Kaufmann Publishers
Citation
Aittala, Miika et al. “Computational mirrors: Blind inverse light transport by deep matrix factorization.” Advances in Neural Information Processing Systems, 32 (2019) © 2019 The Author(s)
Version: Final published version
ISSN
1049-5258