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Edge enhanced depth perception with binocular meta-lens
双目元透镜边缘增强深度感知
双眼メタレンズエッジ強調深さ感知
두 눈 렌즈 가장자리 깊이 인식 강화
Percepción de profundidad mejorada del borde de la lente binocular
Lentille binoculaire Edge améliore la perception de la profondeur
Углубление восприятия глубины на краю бинокулярной линзы
Xiaoyuan Liu 刘小源 ¹ ² ³, Jingcheng Zhang 张景程 ¹, Borui Leng 冷柏锐 ¹, Yin Zhou ¹, Jialuo Cheng ¹, Takeshi Yamaguchi ⁴ ⁵ ⁶, Takuo Tanaka ⁴ ⁵ ⁶, Mu Ku Chen 陈沐谷 ¹ ² ³
¹ Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR 999077, China
中国 香港 香港城市大学 电机工程学系
² Centre for Biosystems, Neuroscience, and Nanotechnology, City University of Hong Kong, Hong Kong SAR 999077, China
中国 香港 香港城市大学 生物系统、神经科学和纳米技术中心
³ The State Key Laboratory of Terahertz and Millimeter Waves, and Nanotechnology, City University of Hong Kong, Hong Kong SAR 999077, China
中国 香港 香港城市大学太赫兹及毫米波国家重点实验室
⁴ Innovative Photon Manipulation Research Team, RIKEN Center for Advanced Photonics, 351-0198, Japan
⁵ Metamaterial Laboratory, RIKEN Cluster for Pioneering Research, 351-0198, Japan
⁶ Institute of Post-LED Photonics, Tokushima University, 770-8506, Japan
Opto-Electronic Science, 2 April 2024
Abstract

The increasing popularity of the metaverse has led to a growing interest and market size in spatial computing from both academia and industry. Developing portable and accurate imaging and depth sensing systems is crucial for advancing next-generation virtual reality devices.

This work demonstrates an intelligent, lightweight, and compact edge-enhanced depth perception system that utilizes a binocular meta-lens for spatial computing. The miniaturized system comprises a binocular meta-lens, a 532 nm filter, and a CMOS sensor. For disparity computation, we propose a stereo-matching neural network with a novel H-Module.

The H-Module incorporates an attention mechanism into the Siamese network. The symmetric architecture, with cross-pixel interaction and cross-view interaction, enables a more comprehensive analysis of contextual information in stereo images. Based on spatial intensity discontinuity, the edge enhancement eliminates ill-posed regions in the image where ambiguous depth predictions may occur due to a lack of texture.

With the assistance of deep learning, our edge-enhanced system provides prompt responses in less than 0.15 seconds. This edge-enhanced depth perception meta-lens imaging system will significantly contribute to accurate 3D scene modeling, machine vision, autonomous driving, and robotics development.
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