Revealing hidden scenes by photon-efficient occlusion-based opportunistic active imaging
Name
oe-26-8-9945.pdf
Description
Published version
Size
6.08 MB
Format
Adobe PDF
Checksum (MD5)
675a298965844fdd23ee38b2d7248c3f
Author(s)
Xu, Feihu
Shulkind, Gal
Thrampoulidis, Christos
Shapiro, Jeffrey H
Torralba, Antonio
Wong, Franco N. C.
Wornell, Gregory W
Date Issued
April 2018
Journal
Optics Express
Publisher
The Optical Society
Citation
Xu, Feihu et al., "Revealing hidden scenes by photon-efficient occlusion-based opportunistic active imaging." Optics Express 26, 8 (April 2018): 9945-62 doi. 10.1364/OE.26.009945 ©2018 Authors
Version
Final published version
Abstract
The ability to see around corners, i.e., recover details of a hidden scene from its reflections in the surrounding environment, is of considerable interest in a wide range of applications. However, the di use nature of light reflected from typical surfaces leads to mixing of spatial information in the collected light, precluding useful scene reconstruction. Here, we employ a computational imaging technique that opportunistically exploits the presence of occluding objects, which obstruct probe-light propagation in the hidden scene, to undo the mixing and greatly improve scene recovery. Importantly, our technique obviates the need for the ultrafast time-of-flight measurements employed by most previous approaches to hidden-scene imaging. Moreover, it does so in a photon-e cient manner (i.e., it only requires a small number of photon detections) based on an accurate forward model and a computational algorithm that, together, respect the physics of three-bounce light propagation and single-photon detection. Using our methodology, we demonstrate reconstruction of hidden-surface reflectivity patterns in a meter-scale environment from non-time-resolved measurements. Ultimately, our technique represents an instance of a rich and promising new imaging modality with important potential implications for imaging science. ©2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.
MIT Department
Massachusetts Institute of Technology. Research Laboratory of Electronics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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DOI of Published Version
https://doi.org/10.1364/OE.26.009945
