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dc.contributor.authorKadambi, Achuta
dc.contributor.authorSchiel, Jamie
dc.contributor.authorRaskar, Ramesh
dc.date.accessioned2020-04-22T14:28:22Z
dc.date.available2020-04-22T14:28:22Z
dc.date.issued2016-07
dc.identifier.urihttps://hdl.handle.net/1721.1/124786
dc.description.abstractA form of meter-scale, macroscopic interferometry is proposed using conventional time-of-flight (ToF) sensors. Today, ToF sensors use phase-based sampling, where the phase delay between emitted and received, high-frequency signals encodes distance. This paper examines an alternative ToF architecture, inspired by micron-scale, microscopic interferometry, that relies only on frequency sampling: we refer to our proposed macroscopic technique as Frequency-Domain Time of Flight (FD-ToF). The proposed architecture offers several benefits over existing phase ToF systems, such as robustness to phase wrapping and implicit resolution of multi-path interference, all while capturing the same number of subframes. A prototype camera is constructed to demonstrate macroscopic interferometry at meter scale. ©2016en_US
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionof10.1109/CVPR.2016.103en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleMacroscopic interferometry: rethinking depth estimation with frequency-domain time-of-flighten_US
dc.typeArticleen_US
dc.identifier.citationKadambi, Achuta, Jamie Schiel, and Ramesh Raskar, "Macroscopic interferometry: rethinking depth estimation with frequency-domain time-of-flight." Proceedings, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 26 June-1 Jul;y 2016, Las Vegas, Nevada (Piscataway, N.J.: IEEE, 2016): p. 893-902 doi 10.1109/CVPR.2016.103 ©2016 Author(s)en_US
dc.contributor.departmentProgram in Media Arts and Sciences (Massachusetts Institute of Technology)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratoryen_US
dc.relation.journalIEEE Conference on Computer Vision and Pattern Recognition (CVPR)en_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.updated2019-08-02T12:28:03Z
dspace.date.submission2019-08-02T12:28:05Z
mit.journal.volume2016en_US
mit.metadata.statusComplete


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