Single-shot Hyperspectral-Depth Imaging with Learned Diffractive Optics

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dc.contributor.authorBaek, Seung-Hwanko
dc.contributor.authorIkoma, Hayatoko
dc.contributor.authorJeon, Danielko
dc.contributor.authorLi, Yuqiko
dc.contributor.authorHeidrich, Wolfgangko
dc.contributor.authorWetzstein, Gordonko
dc.contributor.authorKim, Min Hyukko
dc.date.accessioned2021-10-15T00:30:48Z-
dc.date.available2021-10-15T00:30:48Z-
dc.date.created2021-08-16-
dc.date.created2021-08-16-
dc.date.created2021-08-16-
dc.date.issued2021-10-11-
dc.identifier.citationInternational Conference on Computer Vision (ICCV)-
dc.identifier.issn15505499-
dc.identifier.urihttp://hdl.handle.net/10203/288202-
dc.description.abstractImaging depth and spectrum have been extensively studied in isolation from each other for decades. Recently, hyperspectral-depth (HS-D) imaging emerges to capture both information simultaneously by combining two different imaging systems; one for depth, the other for spectrum. While being accurate, this combinational approach induces increased form factor, cost, capture time, and alignment/registration problems. In this work, departing from the combinational principle, we propose a compact single-shot monocular HS-D imaging method. Our method uses a diffractive optical element (DOE), the point spread function of which changes with respect to both depth and spectrum. This enables us to reconstruct spectrum and depth from a single captured image. To this end, we develop a differentiable simulator and a neural-network-based reconstruction that are jointly optimized via automatic differentiation. To facilitate learning the DOE, we present a first HS-D dataset by building a benchtop HS-D imager that acquires high-quality ground truth. We evaluate our method with synthetic and real experiments by building an experimental prototype and achieve state-of-the-art HS-D imaging results.-
dc.languageEnglish-
dc.publisherIEEE-
dc.titleSingle-shot Hyperspectral-Depth Imaging with Learned Diffractive Optics-
dc.typeConference-
dc.type.rimsCONF-
dc.citation.publicationnameInternational Conference on Computer Vision (ICCV)-
dc.identifier.conferencecountryCN-
dc.identifier.conferencelocationVirtual-
dc.contributor.localauthorKim, Min Hyuk-
dc.contributor.nonIdAuthorIkoma, Hayato-
dc.contributor.nonIdAuthorLi, Yuqi-
dc.contributor.nonIdAuthorHeidrich, Wolfgang-
dc.contributor.nonIdAuthorWetzstein, Gordon-
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CS-Conference Papers(학술회의논문)
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