Cloudar: a Cloud-Based Framework for Mobile Augmented Reality Wenxiao ZHANG, Sikun LIN, Farshid Hassani Bijarbooneh, Hao Fei CHENG, and Pan HUI, Fellow, IEEE
1 CloudAR: A Cloud-based Framework for Mobile Augmented Reality Wenxiao ZHANG, Sikun LIN, Farshid Hassani Bijarbooneh, Hao Fei CHENG, and Pan HUI, Fellow, IEEE Abstract—Computation capabilities of recent mobile devices fixed contents based on detected surfaces, and limiting their enable natural feature processing for Augmented Reality (AR). usage in gaming or simple demonstration. However, mobile AR applications are still faced with scalability The key enabler of practical AR applications is context and performance challenges. In this paper, we propose CloudAR, a mobile AR framework utilizing the advantages of cloud and awareness, with which AR applications recognize the objects edge computing through recognition task offloading. We explore and incidents within the vicinity of the users to truly assist the design space of cloud-based AR exhaustively and optimize the their daily life. Large-scale image recognition is a crucial offloading pipeline to minimize the time and energy consumption. component of context-aware AR systems, leveraging the vision We design an innovative tracking system for mobile devices which input of mobile devices and having extensive applications in provides lightweight tracking in 6 degree of freedom (6DoF) and hides the offloading latency from users’ perception. We retail, education, tourism, advertisement, etc.. For example, an also design a multi-object image retrieval pipeline that executes AR assistant application could recognize road signs, posters, fast and accurate image recognition tasks on servers. In our or book covers around users in their daily life, and overlays evaluations, the mobile AR application built with the CloudAR useful information on top of those physical images. framework runs at 30 frames per second (FPS) on average with Despite the promising benefits, large-scale image recogni- precise tracking of only 1∼2 pixel errors and image recognition of at least 97% accuracy.
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