Metasurface-driven full-space structured light for three-dimensional imaging

Gyeongtae Kim, Yeseul Kim, Jooyeong Yun, Seong-Won Moon, Seokwoo Kim, Jaekyung Kim, Junkyeong Park, Trevon Badloe, Inki Kim () and Junsuk Rho ()
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Gyeongtae Kim: Pohang University of Science and Technology (POSTECH)
Yeseul Kim: Pohang University of Science and Technology (POSTECH)
Jooyeong Yun: Pohang University of Science and Technology (POSTECH)
Seong-Won Moon: Pohang University of Science and Technology (POSTECH)
Seokwoo Kim: Pohang University of Science and Technology (POSTECH)
Jaekyung Kim: Pohang University of Science and Technology (POSTECH)
Junkyeong Park: Pohang University of Science and Technology (POSTECH)
Trevon Badloe: Pohang University of Science and Technology (POSTECH)
Inki Kim: Pohang University of Science and Technology (POSTECH)
Junsuk Rho: Pohang University of Science and Technology (POSTECH)

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: Abstract Structured light (SL)-based depth-sensing technology illuminates the objects with an array of dots, and backscattered light is monitored to extract three-dimensional information. Conventionally, diffractive optical elements have been used to form laser dot array, however, the field-of-view (FOV) and diffraction efficiency are limited due to their micron-scale pixel size. Here, we propose a metasurface-enhanced SL-based depth-sensing platform that scatters high-density ~10 K dot array over the 180° FOV by manipulating light at subwavelength-scale. As a proof-of-concept, we place face masks one on the beam axis and the other 50° apart from axis within distance of 1 m and estimate the depth information using a stereo matching algorithm. Furthermore, we demonstrate the replication of the metasurface using the nanoparticle-embedded-resin (nano-PER) imprinting method which enables high-throughput manufacturing of the metasurfaces on any arbitrary substrates. Such a full-space diffractive metasurface may afford ultra-compact depth perception platform for face recognition and automotive robot vision applications.

Date: 2022
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DOI: 10.1038/s41467-022-32117-2

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