SPARC: Proximity-aware Scheduling of AR Mapping and Cloud-based GenAI Upsampling for Efficient Multi-User SLAM

The scalability of multi-user SLAM is fundamentally limited by the constrained network and computational resources. Existing approaches either focus on SLAM for single-user scenarios or overload networks and servers by streaming dense, uniform camera data and treating all users equally. This results in poor pose estimation accuracy or slow updates to multiple users. Our key insight is that the sparsity and heterogeneity of user activity reveal that not all users or frames contribute equally to the shared map. Building on this, we propose SPARC - Proximity-aware Scheduling of AR Mapping and a blur-aware adaptive Cloud-based GenAI sampling method, which together form a cloud-native framework for efficient multi-user SLAM. On the client side, adaptive, context-aware frame transmission selectively forwards high-value frames. On the server side, generative AI (GenAI)-based upsampling reconstructs dense scene features from sparse inputs, while a proximity-aware scheduler prioritizes updates for users with higher drift or critical interactions. Together, these components reduce redundant transmission, improve resource allocation, and enable fairness without sacrificing accuracy. We show through extensive experimentation that our method reduces the latency by 2× to 4× compared to state-of-the-art while maintaining similar or better tracking accuracy. More broadly, this work reimagines SLAM as a cloud-native service, paving the way for scalable, real-time AR/VR applications where many users seamlessly interact in shared environments.