3D Imaging Using Extreme Dispersion in Optical Metasurfaces

被引:37
|
作者
Tan, Shiyu [1 ]
Yang, Frank [1 ]
Boominathan, Vivek [1 ]
Veeraraghavan, Ashok [1 ]
Naik, Gururaj, V [1 ]
机构
[1] Rice Univ, Elect & Comp Engn, Houston, TX 77005 USA
关键词
metasurface; 3D reconstruction; depth from defocus; chromatic dispersion; convolutional neural network; METALENS;
D O I
10.1021/acsphotonics.1c00110
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
Metasurfaces have the potential to revolutionize imaging technologies due to their extreme control of phase, polarization, and amplitude of the incident light. They rely upon enhanced local interaction of light to achieve the desired phase profile. As a consequence of the enhanced local interaction of light, metasurfaces are highly dispersive. This strong dispersion has been recognized as a primary limitation as it relates to realizing conventional imaging with metasurfaces. Here, we argue that this strong dispersion is an added degree of design freedom for computational imaging, potentially opening up novel applications. In particular, we exploit this strongly dispersive property of metasurfaces to propose a compact, single-shot, and passive 3D imaging camera. Our device consists of a metalens engineered to focus different wavelengths at different depths and two deep networks to recover depth and RGB texture information from chromatic, defocused images acquired by the system. In contrast with other metasurface-based 3D sensors, our design can operate in the full visible range with a larger field-of-view (FOV) and can potentially generate dense depth maps of complicated 3D scenes. Our simulation results on a 1 mm diameter metalens demonstrate its ability to capture 3D depth and texture information ranging from 0.12 to 0.6 m.
引用
收藏
页码:1421 / 1429
页数:9
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