TransGI:实时动态全球照明与以对象为中心的神经传输模型
IEEE transactions on visualization and computer graphics
|August 22, 2025
概括
这项研究介绍了TransGI,一种用于实时全球照明的神经染方法. 它可以使用光泽材料和动态照明实现高保真染,克服计算机图形中的延迟限制.
科学领域:
- 计算机图形
- 人工智能
背景情况:
- 实时染面临着延迟挑战, 在动态照明下限制神经图形算法.
- 现有的方法要么缺乏物质表达力,要么为了速度而牺牲质量.
研究的目的:
- 开发一种用于实时,高准确度全球照明的新型神经染方法.
- 解决动态场景的材料表现和照明效率的局限性.
主要方法:
- 拟议的TransGI,具有以对象为中心的神经传输模型,用于紧和富有表现力的材料表示.
- 引入了具有局部光探测器的辐射共享照明系统,以在不同的光条件下提供高效的照明.
- 在实时染引擎中使用计算遮光器和基于CUDA的神经网络实现.
主要成果:
- 实现实时性能,染时间低于10毫秒.
- 与基线方法相比,显著提高染质量.
- 支持具有低内存的光泽材料效果.
结论:
- TransGI提供了一个可行的解决方案,用于实时,高准确度的全球照明.
- 这种方法有效地平衡了染质量,效率和材料表达性.
- 在延迟受限制的应用程序中实现高级染功能.
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