Towards In-context Environment Sensing for Mobile Augmented Reality
Yiqin Zhao, Ashkan Ganj, Tian Guo · 2024
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.