Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Qingtai Dong

dblp:436/7280 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

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
1 paper
Virtual and augmented reality · 100%
Human-computer interaction and pervasive computing
1 paper
Immersive interaction · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Virtual and augmented reality › immersive display
head-mounted display
1.012026
Mask Balancing: Perception-Driven Dynamic Visibility Enhancement for Occlusion-Capable Optical See-Through Head-Mounted Displays · IEEE Trans. Vis. Comput. Graph. 2026
Virtual and augmented reality › augmented reality display
optical see-through display
1.012026
Mask Balancing: Perception-Driven Dynamic Visibility Enhancement for Occlusion-Capable Optical See-Through Head-Mounted Displays · IEEE Trans. Vis. Comput. Graph. 2026
Immersive interaction
augmented reality interaction
0.312026
Mask Balancing: Perception-Driven Dynamic Visibility Enhancement for Occlusion-Capable Optical See-Through Head-Mounted Displays · IEEE Trans. Vis. Comput. Graph. 2026

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

polarization blending · 2.0perception-driven optimization · 2.0mask balancing · 2.0
YearPublicationVenuePosition
2026 Mask Balancing: Perception-Driven Dynamic Visibility Enhancement for Occlusion-Capable Optical See-Through Head-Mounted Displays
abstract
The poor transparency of occlusion-capable optical see-through head-mounted displays (OC-OSTHMDs) deteriorates the visibility of the real scene, hindering the practical application of the devices. Previous works mitigate the issue by upgrading the transmittance of the spatial light modulator (SLM). However, the strategy soon reaches a limit because further optimization requires improving the transmittance of all optical elements, e.g., lenses and beam splitters. Moreover, pixelated occlusion usually relies on polarizing the real scene light, inevitably cutting the input optical power by half. To overcome this limitation, we propose a mask balancing method that improves real-scene brightness through polarization blending. Specifically, the s-polarized component, which passes through the optical system to provide occlusion-capable vision, is blended with the p-polarized component, which bypasses the system to preserve the raw view of the real scene. The blending is realized by simply modulating the cross-angle between a polarizing beam splitter and a linear polarizer, benefiting the robustness and versatility of the proposed method. We introduce a perception-driven blending approach, where the cross-angle is optimized in real-time to balance the visibility of the real scene and the texture and lighting of the virtual object. A benchtop prototype is built. A user study with 12 participants is conducted to quantify the visibility threshold of the texture and lighting of virtual objects. Then, a user study with 12 participants proves that the proposed method improves the visibility of the real scene while keeping a good appearance of the virtual object. We believe the proposed method is an important step toward developing practical solutions for OC-OSTHMDs.
Yan Zhang 0101, Rundong Chu, Qingtai Dong, Xiaodan Hu, Keyao You, Zixuan Guo 0003, Hangyu Zhou, Kiyoshi Kiyokawa, Xubo Yang
IEEE Trans. Vis. Comput. Graph.3