2020
CVPR
CVPR 2020
Polarized Non-Line-of-Sight Imaging
Abstract
This paper presents a method of passive non-line-of-sight (NLOS) imaging using polarization cues. A key observation is that the oblique light has a different polarimetric signal. It turns out this effect is due to the polarization axis rotation, a phenomena which can be used to better condition the light transport matrix for non-line-of-sight imaging. Our analysis and results show that the use of a polarization for NLOS is both a standalone technique, as well as an enhancement technique to boost the results of other forms of passive NLOS imaging. We make a surprising finding that, despite 50% light attenuation from polarization optics, the gains from polarized NLOS are overall superior to unpolarized NLOS.
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Interdisciplinary Bridge
— Computer Science and Computer Vision
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Keyword Pioneer
— passive imaging
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Interdisciplinary, Machine Learning, Mathematics & Optimization, Reinforcement Learning