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Jingchen Gao

dblp:06/10297 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
0009-0002-5418-8005ORCID · reported

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

Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 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
Geometric modeling and processing · 100%
Human-computer interaction and pervasive computing
1 paper
Immersive interaction · 77% Usability and user experience research · 23%

Topics — the 3 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Geometric modeling and processing › mesh processing › mesh repair
hole filling
1.012026
Adaptive gap closing for complex triangular mesh repair using geometric and topological characteristics · Comput. Aided Des. 2026
Geometric modeling and processing › mesh processing
mesh repair
1.012026
Adaptive gap closing for complex triangular mesh repair using geometric and topological characteristics · Comput. Aided Des. 2026
Geometric modeling and processing › shape representation › mesh representation
triangle mesh
0.312026
Adaptive gap closing for complex triangular mesh repair using geometric and topological characteristics · Comput. Aided Des. 2026

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

topological analysis · 1.0geometric analysis · 1.0frame coding · 1.0between-subjects experiment · 1.0NASA-TLX · 1.0
YearPublicationVenuePosition
2026 Adaptive gap closing for complex triangular mesh repair using geometric and topological characteristics
Shuwei Shen, Zhoufang Xiao, Jingchen Gao
Comput. Aided Des.4
2026 Follow the Signs or the Crowd? Effects of Environmental Load and Crowd Dynamics in VR Evacuation
abstract
Emergency evacuation in VR must balance realism with clear guidance. However, most prior studies strengthen either sensory or social factors in isolation, leaving equal-geometry causal estimates of load versus crowd still lacking. We present SAFE-VR, a controlled testbed that orthogonally varies Environmental Load (low vs. high) and Crowd Dynamics (orderly vs. chaotic) while keeping layout, signage, and spawn constant. In a preregistered 2 x 2 between-subjects experiment (N=80), we analyzed time-to-exit, frame-coded behavior, presence, and workload to distangle sensory from social effects. Both factors impaired egress, with the High×Chaotic condition performing worst overall. For time-to-exit, effects were additive (no reliable Load×Crowd interaction); in contrast, Temporal demand showed a crossed interaction. High load increased effort, frustration, and object contacts; while chaotic flow increased route deviations, human contacts, and slowed exits. These patterns align with reliability-weighted cueing: as guidance becomes harder to perceive, participants may shift toward crowd-following, especially when flow is unstable. SAFE-VR thus delineates how load and crowd structure jointly shape route fidelity, collisions, and evacuation time, and highlights conditions where subjective time pressure diverges from objective delay.
Zheng Wei 0003, Jingchen Gao, Linjie Qiu, Yun Huang 0003, Huamin Qu
IEEE Trans. Vis. Comput. Graph.2