DC Field | Value | Language |
---|---|---|
dc.contributor.author | Baek, Seung-Hwan | ko |
dc.contributor.author | Ikoma, Hayato | ko |
dc.contributor.author | Jeon, Daniel | ko |
dc.contributor.author | Li, Yuqi | ko |
dc.contributor.author | Heidrich, Wolfgang | ko |
dc.contributor.author | Wetzstein, Gordon | ko |
dc.contributor.author | Kim, Min Hyuk | ko |
dc.date.accessioned | 2021-10-15T00:30:48Z | - |
dc.date.available | 2021-10-15T00:30:48Z | - |
dc.date.created | 2021-08-16 | - |
dc.date.created | 2021-08-16 | - |
dc.date.created | 2021-08-16 | - |
dc.date.created | 2021-08-16 | - |
dc.date.issued | 2021-10-11 | - |
dc.identifier.citation | 18th IEEE/CVF International Conference on Computer Vision (ICCV), pp.2631 - 2640 | - |
dc.identifier.issn | 15505499 | - |
dc.identifier.uri | http://hdl.handle.net/10203/288202 | - |
dc.description.abstract | Imaging 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.language | English | - |
dc.publisher | IEEE | - |
dc.title | Single-shot Hyperspectral-Depth Imaging with Learned Diffractive Optics | - |
dc.type | Conference | - |
dc.identifier.wosid | 000797698902082 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 2631 | - |
dc.citation.endingpage | 2640 | - |
dc.citation.publicationname | 18th IEEE/CVF International Conference on Computer Vision (ICCV) | - |
dc.identifier.conferencecountry | CN | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1109/ICCV48922.2021.00265 | - |
dc.contributor.localauthor | Kim, Min Hyuk | - |
dc.contributor.nonIdAuthor | Ikoma, Hayato | - |
dc.contributor.nonIdAuthor | Li, Yuqi | - |
dc.contributor.nonIdAuthor | Heidrich, Wolfgang | - |
dc.contributor.nonIdAuthor | Wetzstein, Gordon | - |
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