Ada: A Distributed, Power-Aware, Real-Time Scene Provider for XR
Year: 2025
Type: article
Abstract: Real-time scene provisioning-reconstructing and delivering scene data to requesting XR applications during runtime-is central to enabling spatial computing in modern XR systems. However, existing solutions struggle to balance latency, power and scene fidelity under XR device constraints, and often rely on designs that are either closed, application-specific designs, or both. We present Ada, the first open distributed, power-aware, application-agnostic real-time scene provisioning system. Through computation offloading along with algorithmic and system innovations, Ada provides high-fidelity scenes with stable performance across all evaluated scene sizes and with low power consumption. To isolate the benefits of Ada's algorithmic and design innovations over the closest prior work [82], which is on-device and CPU-based, we configure a comparable on-device, CPU-based variant of Ada (AdaLocal-CPU). We show this variant achieves up to 6.8× lower scene request latency and higher scene fidelity compared to the prior work. Furthermore, Ada's final distributed GPU-accelerated implementation reduces latency by an additional 2×, highlighting the benefits of GPU acceleration and distributed computing. Additionally, Ada also lowers the incremental power cost of scene provisioning by 24% compared to the best on-device variant (AdaLocal-GPU). Finally, Ada flexibly adapts to diverse latency, power, scene fidelity, and network bandwidth requirements.
Language: English
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,645
Funder: National Science Foundation
Awards: Collaborative Research: SLES: Guaranteed Tubes for Safe Learning across Autonomy Architectures, Collaborative Research: Introspective Counterfactual Reasoning for Robust and Resilient Autonomy, CAREER: Digitize and Simulate the Large Physical World via Knowledge-Grounded Scene Representation, CCRI: New: An Open End-to-End Extended Reality System Infrastructure: Enabling Domain-Specific Edge Systems Research, CPS:MEDIUM: Radar-based Perception and Control for Small Autonomous Robots, Collaborative Research: PPoSS: LARGE: Scalable Specialization in Distributed Edge-Cloud Systems – The Extended Reality Case, RI: Small: Semantic 3D Neural Rendering Field Models that are Accurate, Complete, Flexible, and Scalable
OpenAlex ID: W4414799005