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一种用于智能深度感知的元设备。

A Meta-Device for Intelligent Depth Perception.

作者信息

Chen Mu Ku, Liu Xiaoyuan, Wu Yongfeng, Zhang Jingcheng, Yuan Jiaqi, Zhang Zhengnan, Tsai Din Ping

机构信息

Department of Electrical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, 999077, P. R. China.

Centre for Biosystems, Neuroscience and Nanotechnology, City University of Hong Kong, Kowloon, Hong Kong, 999077, P. R. China.

出版信息

Adv Mater. 2023 Aug;35(34):e2107465. doi: 10.1002/adma.202107465. Epub 2022 Aug 29.

Abstract

The optical illusion affects depth-sensing due to the limited and specific light-field information acquired by single-lens imaging. The incomplete depth information or visual deception would cause cognitive errors. To resolve this problem, an intelligent and compact depth-sensing meta-device that is miniaturized, integrated, and applicable for diverse scenes in all light levels is demonstrated. The compact and multifunction stereo vision system adopts an array with 3600 achromatic meta-lenses and a size of 1.2 × 1.2 mm to measure the depth over a 30 cm range with deep-learning support. The meta-lens array can act as multiple imaging lenses to collect light field information. It can also work with a light source as an active optical device to project a structured light. The meta-lens array can serve as the core functional component of a light-field imaging system under bright conditions or a structured-light projection system in the dark. The depth information in both ways can be analyzed and extracted by the convolutional neural network. This work provides a new avenue for the applications such as autonomous driving, machine vision, human-computer interaction, augmented reality, biometric identification, etc.

摘要

由于单镜头成像获取的光场信息有限且特定,视觉错觉会影响深度感知。不完整的深度信息或视觉欺骗会导致认知错误。为解决这一问题,展示了一种智能且紧凑的深度感知超材料器件,它实现了小型化、集成化,适用于各种光照条件下的不同场景。这种紧凑的多功能立体视觉系统采用了一个由3600个消色差超透镜组成的阵列,尺寸为1.2×1.2毫米,在深度学习的支持下可在30厘米范围内测量深度。超透镜阵列可充当多个成像透镜来收集光场信息。它还可以与光源配合作为有源光学器件投射结构化光。在明亮条件下,超透镜阵列可作为光场成像系统的核心功能组件;在黑暗中,可作为结构化光投射系统。这两种方式下的深度信息都可由卷积神经网络进行分析和提取。这项工作为自动驾驶、机器视觉、人机交互、增强现实、生物特征识别等应用提供了一条新途径。

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