| dc.contributor.author | Aittala, Miika | |
| dc.contributor.author | Sharma, Prafull | |
| dc.contributor.author | Murmann, Lukas | |
| dc.contributor.author | Yedidia, Adam B. | |
| dc.contributor.author | Wornell, Gregory W. | |
| dc.contributor.author | Freeman, William T | |
| dc.contributor.author | Durand, Frederic | |
| dc.date.accessioned | 2021-09-09T15:44:21Z | |
| dc.date.available | 2021-02-24T16:24:08Z | |
| dc.date.available | 2021-09-09T15:44:21Z | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019 | |
| dc.identifier.issn | 1049-5258 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/129992.2 | |
| dc.description.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. | en_US |
| dc.description.sponsorship | United States. Defense Advanced Research Projects Agency (Contract HR0011-16-C-0030) | en_US |
| dc.description.sponsorship | National Science Foundation (U.S.) (Grant CCF-1816209) | en_US |
| dc.language.iso | en | |
| dc.publisher | Morgan Kaufmann Publishers | en_US |
| dc.relation.isversionof | https://papers.nips.cc/paper/2019/hash/5a2afca61e35f45a7dd44ca46e0225f4-Abstract.html | en_US |
| dc.rights | Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. | en_US |
| dc.source | Neural Information Processing Systems (NIPS) | en_US |
| dc.title | Computational mirrors: Blind inverse light transport by deep matrix factorization | en_US |
| dc.type | Article | en_US |
| dc.identifier.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) | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
| dc.relation.journal | Advances in Neural Information Processing Systems | en_US |
| dc.eprint.version | Final published version | en_US |
| dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
| eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
| dc.date.updated | 2020-12-11T16:56:18Z | |
| dspace.orderedauthors | Aittala, M; Sharma, P; Murmann, L; Yedidia, AB; Wornell, GW; Freeman, WT; Durand, F | en_US |
| dspace.date.submission | 2020-12-11T16:56:22Z | |
| mit.journal.volume | 32 | en_US |
| mit.license | PUBLISHER_POLICY | |
| mit.metadata.status | Complete | en_US |
