CloudAR: A Cloud-based Framework for Mobile Augmented Reality
This addresses performance issues for mobile AR applications, offering incremental improvements over existing frameworks.
The paper tackles scalability and performance challenges in mobile augmented reality by proposing CloudAR, a cloud-based framework that offloads recognition tasks to minimize time and energy consumption, achieving 30 FPS with 1-2 pixel tracking errors and at least 97% accuracy in image recognition.
Computation capabilities of recent mobile devices enable natural feature processing for Augmented Reality (AR). However, mobile AR applications are still faced with scalability and performance challenges. In this paper, we propose CloudAR, a mobile AR framework utilizing the advantages of cloud and edge computing through recognition task offloading. We explore the design space of cloud-based AR exhaustively and optimize the offloading pipeline to minimize the time and energy consumption. We design an innovative tracking system for mobile devices which provides lightweight tracking in 6 degree of freedom (6DoF) and hides the offloading latency from users' perception. We also design a multi-object image retrieval pipeline that executes fast and accurate image recognition tasks on servers. In our evaluations, the mobile AR application built with the CloudAR framework runs at 30 frames per second (FPS) on average with precise tracking of only 1~2 pixel errors and image recognition of at least 97% accuracy. Our results also show that CloudAR outperforms one of the leading commercial AR framework in several performance metrics.