{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/119568"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/119568","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Non-line-of-sight imaging using data-driven approaches","abstract":"Non-line-of-sight (NLOS) imaging is desirable for its many potential applications such as detecting a vehicle occluded by a building's corner or imaging through fog. Traditional NLOS imaging techniques solve an inverse problem and are limited by computational complexity and forward model accuracy. This thesis proposes the application of data-driven techniques to NLOS imaging to leverage the convolutional neural network's ability to learn invariants to scene variations. We demonstrate the classification of an object hidden behind a scattering media along with the localization and classification of an object occluded by a corner. In addition we demonstrate the use of generative neural networks to construct images from viewpoints that extend the original camera's field of view.","abstract_html":"Non-line-of-sight (NLOS) imaging is desirable for its many potential applications such as detecting a vehicle occluded by a building's corner or imaging through fog. Traditional NLOS imaging techniques solve an inverse problem and are limited by computational complexity and forward model accuracy. This thesis proposes the application of data-driven techniques to NLOS imaging to leverage the convolutional neural network's ability to learn invariants to scene variations. We demonstrate the classification of an object hidden behind a scattering media along with the localization and classification of an object occluded by a corner. In addition we demonstrate the use of generative neural networks to construct images from viewpoints that extend the original camera's field of view.","abstract_has_math":false,"creators":["Tancik, Matthew"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Ramesh Raskar."],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018","date_published":"2018","updated_at":"2026-07-22T22:21:05Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/119568","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Ramesh Raskar."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. 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In addition we demonstrate the use of generative neural networks to construct images from viewpoints that extend the original camera's field of view."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Non-line-of-sight imaging using data-driven approaches"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ramesh Raskar."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Tancik, Matthew"],"dc:date.accessioned":["2018-12-11T20:40:29Z"],"dc:date.available":["2018-12-11T20:40:29Z"],"dc:date.issued":["2018"],"dc:description":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 63-69)."],"dc:description.abstract":["Non-line-of-sight (NLOS) imaging is desirable for its many potential applications such as detecting a vehicle occluded by a building's corner or imaging through fog. Traditional NLOS imaging techniques solve an inverse problem and are limited by computational complexity and forward model accuracy. This thesis proposes the application of data-driven techniques to NLOS imaging to leverage the convolutional neural network's ability to learn invariants to scene variations. 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