| dc.contributor.author | Kadambi, Achuta | |
| dc.contributor.author | Schiel, Jamie | |
| dc.contributor.author | Raskar, Ramesh | |
| dc.date.accessioned | 2020-04-22T14:28:22Z | |
| dc.date.available | 2020-04-22T14:28:22Z | |
| dc.date.issued | 2016-07 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/124786 | |
| dc.description.abstract | A 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. ©2016 | en_US |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
| dc.relation.isversionof | 10.1109/CVPR.2016.103 | en_US |
| dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | en_US |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en_US |
| dc.source | MIT web domain | en_US |
| dc.title | Macroscopic interferometry: rethinking depth estimation with frequency-domain time-of-flight | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Kadambi, 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.department | Program in Media Arts and Sciences (Massachusetts Institute of Technology) | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Media Laboratory | en_US |
| dc.relation.journal | IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | en_US |
| dc.eprint.version | Author's final manuscript | 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 | 2019-08-02T12:28:03Z | |
| dspace.date.submission | 2019-08-02T12:28:05Z | |
| mit.journal.volume | 2016 | en_US |
| mit.metadata.status | Complete | |