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Linda D. Nguyen

dblp:243/0044 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2019
0009-0006-2340-8318ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
2 papers
Computational photography and imaging · 56% Virtual and augmented reality · 28% Rendering · 17%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational photography and imaging
illumination estimation
0.822019
GLEAM - An Illumination Estimation Framework for Real-time Photorealistic Augmented Reality on Mobile Devices · MobiSys 2019
GLEAM: An Illumination Estimation Framework for Real-time Photorealistic Augmented Reality on Mobile Devices · MobiSys 2019
Virtual and augmented reality › augmented reality
mobile augmented reality
0.412019
GLEAM: An Illumination Estimation Framework for Real-time Photorealistic Augmented Reality on Mobile Devices · MobiSys 2019

Methods — techniques the papers use, named apart from their topics

light-probe estimation · 0.8
YearPublicationVenuePosition
2019 GLEAM: An Illumination Estimation Framework for Real-time Photorealistic Augmented Reality on Mobile Devices
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.
Siddhant Prakash, Alireza Bahremand, Linda D. Nguyen, Robert LiKamWa
MobiSys3
2019 GLEAM - An Illumination Estimation Framework for Real-time Photorealistic Augmented Reality on Mobile Devices
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. We present a demo implementation of GLEAM by means of an AR application that estimates environmental illumination and renders the scene with real-time illumination updates. We demonstrate the efficacy of GLEAM's estimation against a current commercial status quo solution, Apple's ARKit, with the same application.
Siddhant Prakash, Alireza Bahremand, Linda D. Nguyen, Robert LiKamWa
MobiSys3