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光子场网络用于动态实时体积测量全球照明
IEEE transactions on visualization and computer graphics
|October 26, 2023
概括
光子场网络可实现实时全球照明,用于体积数据可视化. 与传统方法相比,这种新的神经表现显著降低了噪音并加快了染速度.
科学领域:
- 科学可视化科学可视化
- 计算机图形 计算机图形
- 卷度染 卷度染
背景情况:
- 量数据可视化在科学学科中至关重要.
- 路径跟踪提供了现实主义,但在实时应用中与噪音和融合作斗争.
- 对大型体积数据集的交互式探索仍然是一个挑战.
研究的目的:
- 在体积数据可视化中引入实时全球照明的新方法.
- 开发光子场网络,用于间接体积全球照明的神经表示.
- 为了实现复杂的体积数据的更快,更低噪音的染.
主要方法:
- 开发了光子场网络,一个阶段功能意识的神经表征.
- 在预先计算的多相光子缓存上训练网络.
- 将集成网络集成到自定义的神经路径追踪器中,用于交互式染.
- 对射线行进,路径跟踪和光子映射进行评价的性能.
主要成果:
- 光子场网络在大型数据集上实现交互式率.
- 该方法表明,与传统技术相比,随机噪声减少了.
- 推断和染速度比现有方法快得多.
- 证实了间接全球照明和相位函数意识的准确表示.
结论:
- 光子场网络为体积数据的实时全球照明提供了可行的解决方案.
- 这种方法有效地平衡了视觉质量,速度和准确性.
- 这种方法增强了科学体积数据的交互式探索.
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