Non-line-of-sight imaging using data-driven approaches
Name
1076274978-MIT.pdf
Description
Full printable version
Size
33.33 MB
Format
Adobe PDF
Checksum (MD5)
1527cfe8ee718cd24587d3bd5ae70b62
Author(s)
Tancik, Matthew
Advisor(s)
Ramesh Raskar.
Alternative Title
NLOS imaging using data-driven approaches
Date Issued
2018
Publisher
Massachusetts Institute of Technology
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.
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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