Research on Fuzzy Image Reconstruction Method Based on Real-Time Fusion Technology of VR and AR

Wang, Shian

doi:10.1109/icvris.2019.00020

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Some other advanced image restoration works exploited CNN architectures to reduce image compression artifacts, restore clean images from downscaled and blurred images, and reconstruct missing details [63]- [65]. It is noted that the differences in terms of image quality and video specification between the clean virtual contents and real displayed images/videos can appear in VR devices [66], [67]. These gaps can be filled effectively with AI-empowered image restoration methods, such as blur estimation, hazy removal, color correction, and texture reconstruction, but the computational complexity should satisfy the real-time video processing speed (usually measured by frames per second -FPS metric) [68] to guarantee high-class user experience in the metaverse.…”

Section: B Machine Visionmentioning

confidence: 99%

Artificial Intelligence for the Metaverse: A Survey

Huynh‐The¹,

Pham²,

Pham³

et al. 2022

Preprint

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Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in cyberspace. Many virtual environments with thousands of services and applications, from social networks to virtual gaming worlds, have been developed with immersive experience and digital transformation, but most are incoherent instead of being integrated into a platform. In this context, metaverse, a term formed by combining meta and universe, has been introduced as a shared virtual world that is fueled by many emerging technologies, such as fifth-generation networks and beyond, virtual reality, and artificial intelligence (AI). Among such technologies, AI has shown the great importance of processing big data to enhance immersive experience and enable humanlike intelligence of virtual agents. In this survey, we make a beneficial effort to explore the role of AI in the foundation and development of the metaverse. We first deliver a preliminary of AI, including machine learning algorithms and deep learning architectures, and its role in the metaverse. We then convey a comprehensive investigation of AI-based methods concerning six technical aspects that have potentials for the metaverse: natural language processing, machine vision, blockchain, networking, digital twin, and neural interface, and being potential for the metaverse. Subsequently, several AI-aided applications, such as healthcare, manufacturing, smart cities, and gaming, are studied to be deployed in the virtual worlds. Finally, we conclude the key contribution of this survey and open some future research directions in AI for the metaverse.

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Section: B Machine Visionmentioning

confidence: 99%

Artificial Intelligence for the Metaverse: A Survey

Huynh‐The¹,

Pham²,

Pham³

et al. 2022

Preprint

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“…Augmented reality is widely used in today's era [1] , and virtual reality lays a solid foundation for augmented reality [2] . AR has played a key role in many disciplines and various practical cases [3] . Augmented reality technology is characterized by the combination of reality and virtuality, in which virtual-real occlusion is the core of augmented reality and a key performance index of AR system.…”

Section: Introductionmentioning

confidence: 99%

A virtual-real occlusion based on monocular camera

Ji¹,

Yang²,

Han³

et al. 2023

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022)

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Virtual-real occlusion is a key step in augmented reality systems. The traditional virtual-real occlusion method has some problems, such as low accuracy and poor adaptation to the scene. On this basis, this paper proposes a virtual real occlusion method based on monocular camera. Firstly, AKAZE algorithm was combined with Tanimoto similarity estimation to find the optimal pair of feature points, and the relative depth of feature points on adjacent frames was calculated by using the triangulation method in SFM algorithm. Then, the real depth value was obtained by combining the translation scale. Finally, the depth value of virtual and real objects was compared to realize the virtual and real occlusion of monocular camera. The experimental results show that the virtual-real occlusion based on monocular camera is realized and the occlusion accuracy is high.

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