Effect of Luminance Contrast and Background Complexity on Depth Perception in Optical See-Through AR
DOI:
https://doi.org/10.66238/gavqp581Keywords:
Optical see-through AR, luminance contrast, background complexity, depth accuracy, visual perception, usabilityAbstract
OST-AR systems overlay virtual imagery onto real-world backgrounds, where luminance contrast may affect spatial discrimination. This study investigates how contrast ratio and background complexity influence depth accuracy. Forty-four participants performed a 3D placement task under three contrast ratios (low, medium, high) and two background conditions (uniform vs. cluttered). High contrast rendering reduced mean depth error from 16.8 mm to 9.1 mm in cluttered environments. Usability scores improved by 34.5% under optimized contrast. Contrast-aware rendering significantly improves depth perception in visually complex environments.
References
1. Wang, Y., Wen, Y., Wu, X., Wang, L., & Cai, H. (2024). Modulation of Gut Microbiota and Glucose Homeostasis through High-Fiber Dietary Intervention in Type 2 Diabetes Management.
2. Rendon-Cardona, C., Burcklen, M. A., Legras, R., & Sandor, C. (2025). Augmented Vision Systems: Paradigms and Applications. IEEE Transactions on Visualization and Computer Graphics.
3. Jiao, Y., Wang, A., Zhao, B., & Shi, T. (2026). Quantitative Study on the Construction and Application Effectiveness of Graffiti Wall Painting Teaching Models in Public Space Contexts.
4. Hussain, R., Chessa, M., & Solari, F. (2023). Improving depth perception in immersive media devices by addressing vergence-accommodation conflict. IEEE transactions on visualization and computer graphics, 30(9), 6334-6346.
5. Yang, Y., Xie, X., Wang, X., Zhang, H., Yu, C., Xiong, X., ... & Baik, F. (2025, October). Impact of target and tool visualization on depth perception and usability in optical see-through AR. In 2025 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) (pp. 700-703). IEEE.
6. Dresp-Langley, B., & Reeves, A. J. (2024). Environmental lighting conditions, phenomenal contrast, and the conscious perception of near and far. Brain sciences, 14(10), 966.
7. Liu, S., Liu, X., & Feng, H. (2025, November). Research on AI-Driven Visual Design and Immersive Interactive Experiences Based on Multimodal Cognition and User. In Proceedings of the 2025 International Conference on Digital Society and Intelligent Computing (pp. 734-740).
8. Christou, C., & Parker, A. (2023). Visual realism and virtual reality: a psychological perspective. In Simulated and virtual realities (pp. 53-84). CRC Press.
9. Wang, Y., Wen, Y., Wu, X., & Cai, H. (2024). Comprehensive Evaluation of GLP1 Receptor Agonists in Modulating Inflammatory Pathways and Gut Microbiota.
10. Paulin, G., & Ivasic‐Kos, M. (2023). Review and analysis of synthetic dataset generation methods and techniques for application in computer vision. Artificial intelligence review, 56(9).
11. Zhang, Y., & Wang, J. (2026). Design and Implementation of a Computer-Aided Full Lifecycle Quality Management System for Wind Farms in Upgrades, Renovations, and Subcontractor Supervision.
12. Mazancieux, A., Pereira, M., Faivre, N., Mamassian, P., Moulin, C. J., & Souchay, C. (2023). Towards a common conceptual space for metacognition in perception and memory. Nature Reviews Psychology, 2(12), 751-766.
13. Jiao, Y., Zhao, B., Wang, A., & Shi, T. (2026). Construction and Empirical Study of a Modularized Teaching System for Art Courses Based on a Unified Training Pathway.
14. McQuaigue, M. (2024). Influence of Visual Field Congruence and Realism Through Global Illumination on Egocentric Depth Perception in Virtual and Augmented Environments (Doctoral dissertation, The University of North Carolina at Charlotte).
15. Liu, S., & Yim, J. (2025). Research on Generative AI Creation Systems Based on Visual Language Modeling: Human-Machine Collaboration and Cognitive Feedback Mechanisms. Available at SSRN 6139770.
16. Cardenas, D. R., Kandoi, C. R., Wickens, C., & Arefin, M. S. (2026). Effects of Environmental Clutter, Head-Mounted Display Clutter, and Head-Mounted Display Transparency on Human Efficiency, Safety, and Mental Demand During Navigation. Human Factors, 00187208261425741.
17. Yu, X. (2026). AI-Driven Personalization across Domains for Local Categorical Query Understanding and Context-Aware Retrieval.
18. Clarke, T. J., Mayer, W., Zucco, J. E., Matthews, B. J., & Smith, R. T. (2022, October). Adapting VST AR X-Ray Vision Techniques to OST AR. In 2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) (pp. 495-500). IEEE.
19. Wang, Y., Wang, L., Wen, Y., Wu, X., & Cai, H. (2025). Precision-Engineered Nanocarriers for Targeted Treatment of Liver Fibrosis and Vascular Disorders.
20. Åberg Lindell, R., & Williams, T. (2025). The Impact of Screen Contrast on Cognitive Load and Mental Effort-A Health Psychology Perspective.
21. Gao, G., Gao, R., Gao, R., & Zhou, H. (2026). Engineering Analysis and Quantitative Research on the Platform-Based Evolution of Enterprise Communication Systems.
22. Dickens, A. (2025). A Systematic Review: Investigating the Use of Current Measurement Techniques for the Development of an Objective Cognitive Fatigue Metric.
23. Wang, Y., Yin, X., Chen, J., & Wang, Y. (2026). Evidence-Based Study on Low-Burden Digital Phenotyping for Precision Screening of Oral Anti-Obesity Drug Efficacy.
24. Albert, D. A., & Smilek, D. (2023). Comparing attentional disengagement between Prolific and MTurk samples. Scientific Reports, 13(1), 20574.
25. Zhang, Y., Gu, W., & Wang, J. (2025). Research on First Article Inspection (FAI)-Driven Quality Assurance Methods for Wind Turbine Installation and Operation & Maintenance and Their Effect on Reliability Improvement. Available at SSRN 6094206.
26. Kim, J., Oh, S. W., Choi, J., Park, S., & Kim, W. (2022). Optical see-through head-mounted display including transmittance-variable display for high visibility. Journal of Information Display, 23(2), 121-127.
27. Xiao, H., Zhu, W., Qiu, Z., Zeng, Z., Zhang, S., & Xiao, R. (2025, September). Anti-Drosophila Habituation Clustering for Enhanced Anomaly Detection in Data Streams. In 2025 10th International Conference on Computer and Information Processing Technology (ISCIPT) (pp. 636-644). IEEE.
28. Khaohoen, A., Powcharoen, W., Sornsuwan, T., Chaijareenont, P., Rungsiyakull, C., & Rungsiyakull, P. (2024). Accuracy of implant placement with computer-aided static, dynamic, and robot-assisted surgery: a systematic review and meta-analysis of clinical trials. BMC Oral Health, 24(1), 359.
29. Chen, J., Xiao, H., Zeng, Z., Du, X., & Xiao, R. (2026). Diffusion-step attention consistency for multivariate time series anomaly detection. Knowledge-Based Systems, 115716.
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Copyright (c) 2026 Hiroshi Tanaka, Daichi Sato, Yuki Nakamura (Author)
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