UC Riverside

Augmented reality (AR) continues to evolve with applications across fields such as entertainment, education, and public safety. As we envision the future of a rich AR ecosystem with world-scale AR and collaborative interactions, the demand for responsiveness in user experience becomes more challenging. In this work, I focus on the different aspects of responsiveness in different AR scenarios. In the setting of a single-user, world-scale AR environment, I propose a 3D model retrieval framework that makes intelligent decisions to reduce the communication latency of transferring models from an edge server. When multiple users are present, I introduce an automatic synthesis of a coordination protocol that enables low-latency coordination of virtual objects between users, while respecting real-world spatial constraints.Furthermore, the rapid growth of new 3D content data representations, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), has significantly expanded the potential for creating photorealistic scenes within the mixed reality domain. However, the massive data size of 3DGS poses challenges for efficient content delivery. To address this, I propose an optimized framework for scene delivery through customized, layered 3DGS scenes combined with intelligent scheduling algorithms, ensuring efficient and high-fidelity 3D content distributions. This work contributes to advancing AR by addressing latency, synchronization, and content delivery challenges, paving the way for seamless, immersive, and collaborative AR experiences.