Year: 2024
Type: conference-paper
Abstract: Environment sensing is a fundamental task in mobile augmented reality (AR). However, on-device sensing and computing resources often limit mobile AR sensing capability, making high-quality environment sensing challenging to achieve. In recent years, in-context sensing, a new sensing system design paradigm, has emerged with the promise of achieving accurate, efficient, and robust sensing results. In this work, we first formally define the in-context sensing design paradigm. We summarize its primary challenges as the uncertainty of environmental information availability. To quantify the impact of sensing context data, we present two in-depth case studies that show how it can impact different aspects of mobile AR sensing systems.
Authors: Yiqin Zhao, Ashkan Ganj, Tian Guo
Institution: Worcester Polytechnic Institute
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funders: National Science Foundation, VMware
OpenAlex ID: W4405014694