Beyond fidelity: Rethinking realism through human behavior in VR city modeling LOD design

This study redefines realism in virtual reality (VR) city modeling by proposing a perception-driven Level of Detail (LOD) framework informed by human gaze behavior. Challenging the conventional assumption that higher fidelity equates to greater realism, the study systematically investigates the interplay of Feature Complexity (FC), Appearance (AP), and Semantics (SE) in achieving "cognitive realism." Using eye-tracking technology integrated with VR, we conducted experiments in Guangzhou’s Xianfeng Community to identify LOD configurations that most closely align with real-world gaze distribution. Results reveal that the High Feature Complexity, Mid Appearance, Low Semantics (H-M-L) combination optimizes both global gaze patterns and perceptual coherence, outperforming higher semantic density configurations. The study also introduces an open-source tool for analyzing gaze behavior in urban VR environments, providing a scalable solution for immersive urban design research. These findings establish a foundation for advancing human-centric design in VR city modeling, emphasizing perceptual realism over technical fidelity to better align virtual environments with real-world human cognition. This work further positions VR-based perception-driven modeling as a key enabler for Urban Digital Twins, supporting the transition from static 3D models to dynamic, multi-source-data decision-making platforms.

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Publication Details

Journal
International Journal of Applied Earth Observation and Geoinformation
Published
2026-07-28
DOI
https://doi.org/10.1016/j.jag.2026.105484
Primary Topic
3D Modeling in Geospatial Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Beyond fidelity: Rethinking realism through human behavior in VR city modeling LOD design

Cai Wu, Yang Guosheng, Yixian Su
International Journal of Applied Earth Observation and Geoinformation
3D Modeling in Geospatial Applications
article

Beyond fidelity: Rethinking realism through human behavior in VR city modeling LOD design

Cai Wu, Yang Guosheng, Yixian Su
article en

Abstract

This study redefines realism in virtual reality (VR) city modeling by proposing a perception-driven Level of Detail (LOD) framework informed by human gaze behavior. Challenging the conventional assumption that higher fidelity equates to greater realism, the study systematically investigates the interplay of Feature Complexity (FC), Appearance (AP), and Semantics (SE) in achieving "cognitive realism." Using eye-tracking technology integrated with VR, we conducted experiments in Guangzhou’s Xianfeng Community to identify LOD configurations that most closely align with real-world gaze distribution. Results reveal that the High Feature Complexity, Mid Appearance, Low Semantics (H-M-L) combination optimizes both global gaze patterns and perceptual coherence, outperforming higher semantic density configurations. The study also introduces an open-source tool for analyzing gaze behavior in urban VR environments, providing a scalable solution for immersive urban design research. These findings establish a foundation for advancing human-centric design in VR city modeling, emphasizing perceptual realism over technical fidelity to better align virtual environments with real-world human cognition. This work further positions VR-based perception-driven modeling as a key enabler for Urban Digital Twins, supporting the transition from static 3D models to dynamic, multi-source-data decision-making platforms.

International Journal of Applied Earth Observation and GeoinformationVol. 152
Guangzhou University (CN), University of Hong Kong (HK)
National Natural Science Foundation of China, Guangzhou Municipal Science and Technology Bureau
Openalex Percentile: Top 100%
3D Modeling in Geospatial Applications
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Beyond fidelity: Rethinking realism through human behavior in VR city modeling LOD design — Cai Wu, Yang Guosheng, et al. · International Journal of Applied Earth Observation and Geoinformation (2026) | TGRS Research Map | TGRS