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dc.contributor.authorMurmann, Lukas
dc.contributor.authorGharbi, Michael Yanis
dc.contributor.authorAittala, Miika
dc.contributor.authorDurand, Frederic
dc.date.accessioned2021-02-04T18:51:36Z
dc.date.available2021-02-04T18:51:36Z
dc.date.issued2019-11
dc.identifier.issn1063-6919
dc.identifier.urihttps://hdl.handle.net/1721.1/129681
dc.description.abstractCollections of images under a single, uncontrolled illumination have enabled the rapid advancement of core computer vision tasks like classification, detection, and segmentation. But even with modern learning techniques, many inverse problems involving lighting and material understanding remain too severely ill-posed to be solved with single-illumination datasets. The data simply does not contain the necessary supervisory signals. Multi-illumination datasets are notoriously hard to capture, so the data is typically collected at small scale, in controlled environments, either using multiple light sources, or robotic gantries. This leads to image collections that are not representative of the variety and complexity of real world scenes. We introduce a new multi-illumination dataset of more than 1000 real scenes, each captured in high dynamic range and high resolution, under 25 lighting conditions. We demonstrate the richness of this dataset by training state-of-the-art models for three challenging applications: Single-image illumination estimation, image relighting, and mixed-illuminant white balance.en_US
dc.description.sponsorshipUnited States. Defense Advanced Research Projects Agency. Revolutionary Enhancement of Visibility by Exploiting Active Light-fields Program (Contract HR0011- 16-C-0030)en_US
dc.language.isoen
dc.publisherIEEEen_US
dc.relation.isversionof10.1109/ICCV.2019.00418en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleA Dataset of Multi-Illumination Images in the Wilden_US
dc.typeArticleen_US
dc.identifier.citationMurmann, Lukas et al. “A Dataset of Multi-Illumination Images in the Wild.” Paper in the Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 27 Oct.-2 Nov. 2019, IEEE © 2019 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journalProceedings of the IEEE International Conference on Computer Visionen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2020-12-11T16:52:08Z
dspace.orderedauthorsMurmann, L; Gharbi, M; Aittala, M; Durand, Fen_US
dspace.date.submission2020-12-11T16:52:14Z
mit.journal.volume2019-Octoberen_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusComplete


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