In lightweight augmented reality(AR)glasses,the light engines must be very compact while keeping a high optical efficiency to enable longtime comfortable wearing and high ambient contrast ratio.“Liquid-crystal-on-sil...In lightweight augmented reality(AR)glasses,the light engines must be very compact while keeping a high optical efficiency to enable longtime comfortable wearing and high ambient contrast ratio.“Liquid-crystal-on-silicon(LCoS)or micro-LED,who wins?”is recently a heated debate question.Conventional LCoS system is facing tremendous challenges due to its bulky illumination systems;it often incorporates a bulky polarizing beam splitter(PBS)cube.To minimize the formfactor of an LCoS system,here we demonstrate an ultracompact illumination system consisting of an in-coupling prism,and a light guide plate with multiple parallelepiped extraction prisms.The overall module volume including the illumination optics and an LCoS panel(4.4-μm pixel pitch and 1024x1024 resolution elements),but excluding the projection optics,is merely 0.25 cc(cm3).Yet,our system exhibits an excellent illuminance uniformity and an impressive optical efficiency(36%–41%for a polarized input light).Such an ultracompact and high-efficiency LCoS illumination system is expected to revolutionize the next-generation AR glasses.展开更多
Augmented reality(AR)displays,heralded as the next-generation platform for spatial computing,metaverse,and digital twins,empower users to perceive digital images overlaid with real-world environment,fostering a deeper...Augmented reality(AR)displays,heralded as the next-generation platform for spatial computing,metaverse,and digital twins,empower users to perceive digital images overlaid with real-world environment,fostering a deeper level of human-digital interactions.With the rapid evolution of couplers,waveguide-based AR displays have streamlined the entire system,boasting a slim form factor and high optical performance.However,challenges persist in the waveguide combiner,including low optical efficiency and poor image uniformity,significantly hindering the long-term usage and user experience.In this paper,we first analyze the root causes of the low optical efficiency and poor uniformity in waveguide-based AR displays.We then discover and elucidate an anomalous polarization conversion phenomenon inherent to polarization volume gratings(PVGs)when the incident light direction does not satisfy the Bragg condition.This new property is effectively leveraged to circumvent the tradeoff between in-coupling efficiency and eyebox uniformity.Through feasibility demonstration experiments,we measure the light leakage in multiple PVGs with varying thicknesses using a laser source and a liquid-crystal-on-silicon light engine.The experiment corroborates the polarization conversion phenomenon,and the results align with simulation well.To explore the potential of such a polarization conversion phenomenon further,we design and simulate a waveguide display with a 50°field of view.Through achieving first-order polarization conversion in a PVG,the in-coupling efficiency and uniformity are improved by 2 times and 2.3 times,respectively,compared to conventional couplers.This groundbreaking discovery holds immense potential for revolutionizing next-generation waveguide-based AR displays,promising a higher efficiency and superior image uniformity.展开更多
Artificial intelligence(AI)is crucial in driving scientific,technological,and industrial advancements,and it has given rise to an ambient intelligence that can potentially improve the physical execution of healthcare ...Artificial intelligence(AI)is crucial in driving scientific,technological,and industrial advancements,and it has given rise to an ambient intelligence that can potentially improve the physical execution of healthcare delivery[1,2].Among diverse advanced AI technologies,an intelligent agent with multi-parameter perception,decision-making,and execution capabilities demonstrates the potential for facilitating the development of next-generation optoelectronic devices.The intelligent agent is a physical or abstract entity that acts autonomously,perceives and interacts with its environment,and communicates with other agents[3].It could perceive dynamic environmental conditions,execute actions,and make appropriate decisions.Fabric emerges as an ideal carrier for human-centered intelligent agents,providing various properties such as perceptibility,adaptability,and wearability.Intelligent fabric,known for its unique functionality,has attracted considerable attention from academia and industry.In 2014,Germany proposed a national strategy called FutureTEX to upgrade the entire textile industry by promoting integration between textiles and other fields.Two years later,the United States announced the establishment of the Revolutionary Fibers and Textiles Manufacturing Innovation Institute,which intends to accelerate the revival of fabric manufacturing.Compared with conventional fibers,revolutionary fibers focus on the design of multiple materials and structures,enabling the integration of various functionalities into a single fiber.Particularly in the United States,the advent of the digital revolution,advancements in Internet of Things technology,and mature fiber technology significantly boost the development of the intelligent fiber industry.Notable commercial applications of intelligent fibers are gradually emerging.Project Jacquard,a collaborative effort by Google and Levi’s,presents an intelligent jacket that combines the washability and texture of standard fabrics with the interactive functionalities of electronic products.Apple Inc.has developed intelligent garments,accessories,and household items with capabilities to“read”physiological indicators such as weight,body temperature,and sedentary duration on sofas.展开更多
Generating photo-realistic images from a text description is a challenging problem in computer vision.Previous works have shown promising performance to generate synthetic images conditional on text by Generative Adve...Generating photo-realistic images from a text description is a challenging problem in computer vision.Previous works have shown promising performance to generate synthetic images conditional on text by Generative Adversarial Networks(GANs).In this paper,we focus on the category-consistent and relativistic diverse constraints to optimize the diversity of synthetic images.Based on those constraints,a category-consistent and relativistic diverse conditional GAN(CRD-CGAN)is proposed to synthesize K photo-realistic images simultaneously.We use the attention loss and diversity loss to improve the sensitivity of the GAN to word attention and noises.Then,we employ the relativistic conditional loss to estimate the probability of relatively real or fake for synthetic images,which can improve the performance of basic conditional loss.Finally,we introduce a category-consistent loss to alleviate the over-category issues between K synthetic images.We evaluate our approach using the Caltech-UCSD Birds-200-2011,Oxford 102 flower and MS COCO 2014 datasets,and the extensive experiments demonstrate superiority of the proposed method in comparison with state-of-the-art methods in terms of photorealistic and diversity of the generated synthetic images.展开更多
文摘In lightweight augmented reality(AR)glasses,the light engines must be very compact while keeping a high optical efficiency to enable longtime comfortable wearing and high ambient contrast ratio.“Liquid-crystal-on-silicon(LCoS)or micro-LED,who wins?”is recently a heated debate question.Conventional LCoS system is facing tremendous challenges due to its bulky illumination systems;it often incorporates a bulky polarizing beam splitter(PBS)cube.To minimize the formfactor of an LCoS system,here we demonstrate an ultracompact illumination system consisting of an in-coupling prism,and a light guide plate with multiple parallelepiped extraction prisms.The overall module volume including the illumination optics and an LCoS panel(4.4-μm pixel pitch and 1024x1024 resolution elements),but excluding the projection optics,is merely 0.25 cc(cm3).Yet,our system exhibits an excellent illuminance uniformity and an impressive optical efficiency(36%–41%for a polarized input light).Such an ultracompact and high-efficiency LCoS illumination system is expected to revolutionize the next-generation AR glasses.
文摘Augmented reality(AR)displays,heralded as the next-generation platform for spatial computing,metaverse,and digital twins,empower users to perceive digital images overlaid with real-world environment,fostering a deeper level of human-digital interactions.With the rapid evolution of couplers,waveguide-based AR displays have streamlined the entire system,boasting a slim form factor and high optical performance.However,challenges persist in the waveguide combiner,including low optical efficiency and poor image uniformity,significantly hindering the long-term usage and user experience.In this paper,we first analyze the root causes of the low optical efficiency and poor uniformity in waveguide-based AR displays.We then discover and elucidate an anomalous polarization conversion phenomenon inherent to polarization volume gratings(PVGs)when the incident light direction does not satisfy the Bragg condition.This new property is effectively leveraged to circumvent the tradeoff between in-coupling efficiency and eyebox uniformity.Through feasibility demonstration experiments,we measure the light leakage in multiple PVGs with varying thicknesses using a laser source and a liquid-crystal-on-silicon light engine.The experiment corroborates the polarization conversion phenomenon,and the results align with simulation well.To explore the potential of such a polarization conversion phenomenon further,we design and simulate a waveguide display with a 50°field of view.Through achieving first-order polarization conversion in a PVG,the in-coupling efficiency and uniformity are improved by 2 times and 2.3 times,respectively,compared to conventional couplers.This groundbreaking discovery holds immense potential for revolutionizing next-generation waveguide-based AR displays,promising a higher efficiency and superior image uniformity.
基金supported by the National Natural Science Foundation of China(T2425018 and 62175082 to Guangming Tao,and 62371138 to Cuiwei Yang)the Interdisciplinary Research Program of Huazhong University of Science and Technology(2023JCYJ039 to Guangming Tao)+3 种基金the National Key Research and Development Program of China(2022YFB3805800 to Chong Hou)The Open Project Program of Wuhan National Laboratory for Optoelectronics(2023083 to Ning Zhou)Huazhong University of Science and Technology Double First-Class Funds for Humanities and Social Sciences(Sports Industry Research Center of Huazhong University of Science and Technology)China Postdoctoral Science Foundation(2023M731184 to Maiping Yang).
文摘Artificial intelligence(AI)is crucial in driving scientific,technological,and industrial advancements,and it has given rise to an ambient intelligence that can potentially improve the physical execution of healthcare delivery[1,2].Among diverse advanced AI technologies,an intelligent agent with multi-parameter perception,decision-making,and execution capabilities demonstrates the potential for facilitating the development of next-generation optoelectronic devices.The intelligent agent is a physical or abstract entity that acts autonomously,perceives and interacts with its environment,and communicates with other agents[3].It could perceive dynamic environmental conditions,execute actions,and make appropriate decisions.Fabric emerges as an ideal carrier for human-centered intelligent agents,providing various properties such as perceptibility,adaptability,and wearability.Intelligent fabric,known for its unique functionality,has attracted considerable attention from academia and industry.In 2014,Germany proposed a national strategy called FutureTEX to upgrade the entire textile industry by promoting integration between textiles and other fields.Two years later,the United States announced the establishment of the Revolutionary Fibers and Textiles Manufacturing Innovation Institute,which intends to accelerate the revival of fabric manufacturing.Compared with conventional fibers,revolutionary fibers focus on the design of multiple materials and structures,enabling the integration of various functionalities into a single fiber.Particularly in the United States,the advent of the digital revolution,advancements in Internet of Things technology,and mature fiber technology significantly boost the development of the intelligent fiber industry.Notable commercial applications of intelligent fibers are gradually emerging.Project Jacquard,a collaborative effort by Google and Levi’s,presents an intelligent jacket that combines the washability and texture of standard fabrics with the interactive functionalities of electronic products.Apple Inc.has developed intelligent garments,accessories,and household items with capabilities to“read”physiological indicators such as weight,body temperature,and sedentary duration on sofas.
基金supported by the National Natural Science Foundation of China(Grant Nos.61972298 and 61962019)by the National Cultural and Tourism Science and Technology Innovation Project(2021064)the Training Program of High Level Scientific Research Achievements of Hubei Minzu University under Grant PY22011.
文摘Generating photo-realistic images from a text description is a challenging problem in computer vision.Previous works have shown promising performance to generate synthetic images conditional on text by Generative Adversarial Networks(GANs).In this paper,we focus on the category-consistent and relativistic diverse constraints to optimize the diversity of synthetic images.Based on those constraints,a category-consistent and relativistic diverse conditional GAN(CRD-CGAN)is proposed to synthesize K photo-realistic images simultaneously.We use the attention loss and diversity loss to improve the sensitivity of the GAN to word attention and noises.Then,we employ the relativistic conditional loss to estimate the probability of relatively real or fake for synthetic images,which can improve the performance of basic conditional loss.Finally,we introduce a category-consistent loss to alleviate the over-category issues between K synthetic images.We evaluate our approach using the Caltech-UCSD Birds-200-2011,Oxford 102 flower and MS COCO 2014 datasets,and the extensive experiments demonstrate superiority of the proposed method in comparison with state-of-the-art methods in terms of photorealistic and diversity of the generated synthetic images.
