RemoteVIO: Offloading Head Tracking in an End-to-End XR System
Year: 2025
Type: conference-paper
Abstract: Power consumption, and the resulting limitation to computational load, is a first-order constraint in designing comfortable all-day-wear extended reality (XR) devices that can provide rich immersive experiences. This paper concerns reducing XR device power consumption by offloading head tracking, one of the top CPU and power consumers, to a remote server. We present RemoteVIO, the first open-source end-to-end XR system that offloads head tracking (visual inertial odometry or VIO) to a remote server. Our work distinguishes itself from past studies on computation offloading in XR by properly addressing two under-explored but critical aspects: 1) a comprehensive evaluation of user experience in a complete end-to-end XR system and 2) a quantification of the net power savings on real hardware.
Authors: Qinjun Jiang, Yihan Pang, William Sentosa, Steven Gao, Muhammad Huzaifa, Jeffrey Zhang, Javier Perez-Ramirez, Dibakar Das, David Gonzalez-Aguirre, Brighten Godfrey, Sarita Adve
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4408870154