Tutti

Xu, Dongzhu; Zhou, Anfu; Wang, Guixian; Zhang, Huanhuan; Li, Xiangyu; Pei, Jialiang; Ma, Huadóng

doi:10.1145/3495243.3560538

Cited by 12 publications

(2 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although current advancements in deep neural networks (DNNs) have shown superior performance in object detection [12,78,99,105], executing large networks on computation-constrained devices such as AR devices and IoT sensors with low latency remains a challenge. To address this, edge-supported architectures are needed to offload computation from the AR devices and IoT sensors and improve the end-to-end latency [70,114,201,205,216]. As the pervasive deployment of mobile AR will offer numerous opportunities for multi-user collaboration, prior works have also studied object detection that exploits the visual information captured by different AR devices [37,217].…”

Section: Background On Object Detection In Armentioning

confidence: 99%

Ambient Intelligence for Next-Generation AR

Scargill¹,

Eom²,

Chen³

et al. 2023

Preprint

View full text Add to dashboard Cite

Next-generation augmented reality (AR) promises a high degree of context-awareness -a detailed knowledge of the environmental, user, social and system conditions in which an AR experience takes place. This will facilitate both the closer integration of the real and virtual worlds, and the provision of context-specific content or adaptations. However, environmental awareness in particular is challenging to achieve using AR devices alone; not only are these mobile devices' view of an environment spatially and temporally limited, but the data obtained by onboard sensors is frequently inaccurate and incomplete. This, combined with the fact that many aspects of core AR functionality and user experiences are impacted by properties of the real environment, motivates the use of ambient IoT devices, wireless sensors and actuators placed in the surrounding environment, for the measurement and optimization of environment properties. In this book chapter we categorize and examine the wide variety of ways in which these IoT sensors and actuators can support or enhance AR experiences, including quantitative insights and proof-of-concept systems that will inform the development of future solutions. We outline the challenges and opportunities associated with several important research directions which must be addressed to realize the full potential of next-generation AR.

show abstract

Section: Background On Object Detection In Armentioning

confidence: 99%

Ambient Intelligence for Next-Generation AR

Scargill¹,

Eom²,

Chen³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Tutti [181] is a latency-critical video analytics system that integrates 5G radio access network (RAN) with mobile edge computing (MEC). Traditional MEC systems and 5G RAN function in an isolated manner.…”

Section: In-network Optimizationmentioning

confidence: 99%