cid 6bd1f472659edfef4b0294ae8720112d219c8d3a875ec875c7139c745d265715 · original · via openalex
Title: GLEAM Authors: Siddhant Prakash; Alireza Bahremand; Linda D. Nguyen; Robert LiKamWa Venue: International Conference on Mobile Systems, Applications, and Services Year: 2019 Canonical: doi:10.1145/3307334.3326098 URL: https://doi.org/10.1145/3307334.3326098 Collector: openalex OA: false ABSTRACT Mixed reality mobile platforms attempt to co-locate virtual scenes with physical environments, towards creating immersive user experiences. However, to create visual harmony between virtual and physical spaces, the virtual scene must be accurately illuminated with realistic lighting that matches the physical environment. To this end, we design GLEAM, a framework that provides robust illumination estimation in real-time by integrating physical light-probe estimation with current mobile AR systems. GLEAM visually observes reflective objects to compose a realistic estimation of physical lighting. Optionally, GLEAM can network multiple devices to sense illumination from different viewpoints and compose a richer estimation to enhance realism and fidelity. Using GLEAM, AR developers gain the freedom to use a wide range of materials, which is currently limited by the unrealistic appearance of materials that need accurate illumination, such as liquids, glass, and smooth metals. Our controlled environment user studies across 30 participants reveal the effectiveness of GLEAM in providing robust and adaptive illumination estimation over commercial status quo solutions, such as pre-baked directional lighting and ARKit 2.0 illumination estimation. Our benchmarks reveal the need for situation driven tradeoffs to optimize for quality factors in situations requiring freshness over quality and vice-versa. Optimizing for different quality factors in different situations, GLEAM can update scene illumination as fast as 30ms by sacrificing richness and fidelity in highly dynamic scenes, or prioritize quality by allowing an update interval as high as 400ms in scenes that require high-fidelity estimation.