Research on global illumination exploration in dynamic scenes

Xiu Ji; Huamin Yang; Cheng Han

doi:10.1117/12.2684526

1 August 2023 Research on global illumination exploration in dynamic scenes

Xiu Ji, Huamin Yang, Cheng Han

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127541B (2023) https://doi.org/10.1117/12.2684526
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

Neural rendering can achieve satisfactory illumination effects with unknown light source locations and brightness. When training on dynamic scenarios (where object positions, textures, lighting, and viewpoints can vary), variations in object position, material properties, light intensity, and angle make the training results unsatisfactory due to the influence of global light sources. This paper proposes a novel global illumination exploration method, which uses Markov Chain Monte Carlo (MCMC) to perform small sample sampling of light points, and incorporates a new Bayesian optimization (Randomized Search) strategy to optimize the sample data combined with real-time data to remove sampling redundant information and improve training efficiency. The comprehensive experimental results show that our proposed method provides a practical and effective solution for the study of global illumination in dynamic scenes.

Citation Download Citation

Xiu Ji, Huamin Yang, and Cheng Han "Research on global illumination exploration in dynamic scenes", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127541B (1 August 2023); https://doi.org/10.1117/12.2684526

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