EMAR: Edge-Assisted Multi-User Mobile Augmented Reality

Abstract

Multi-user Augmented Reality (AR) applications allow multiple users to interact within the same physical environment. However, existing multi-user systems lack the ability to recognize the complex physical environment. It is also challenging to achieve real-time multi-user AR under dynamic network conditions and different computing workloads. In this paper, we present EMAR, an edge-assisted multi-user mobile AR system. The system uses an edge cooperative optimization strategy to improve the accuracy of scene recognition, and adopts adaptive offloading and AR dynamic update strategy to ensure the real-time and effectiveness of AR recovery. We implement EMAR on mobile smartphones and an edge server. We verify its performance in different environments. The results show that EMAR achieves precision above 84.7% and recall above 92% for accurately identifying the same scene. Compared to the baseline method, our system is more robust in the dynamic environment, showing significant performance improvements.