风格RF-VolVis:神经辐射场的风格转移用于表达式体积可视化
IEEE transactions on visualization and computer graphics
|September 10, 2024
概括
StyleRF-VolVis通过使用神经辐射场 (NeRF) 将内容和风格分开来提供富有表现力的体积可视化 (VolVis). 这一框架在生成新的3D可视化中提高了质量,一致性和灵活性.
科学领域:
- 计算机图形 计算机图形
- 科学可视化科学可视化
- 人工智能的人工智能
背景情况:
- 体积可视化 (VolVis) 合成产生了超越传统染的新可视化.
- 现有的生成对抗网络 (GAN) 方法遭受长时间的培训时间,低质量和不一致.
- 神经辐射场 (NeRF) 为先进的可视化技术提供了一个有希望的途径.
研究的目的:
- 介绍StyleRF-VolVis,一种用于表达式体积可视化的新型风格转移框架.
- 在3D场景中,启用场景几何 (内容) 和颜色外观 (风格) 的分离.
- 提供灵活控制颜色,不透明度和照明,同时保持视觉一致性.
主要方法:
- 开发了一个基本的NeRF模型用于场景几何提取.
- 设计了一个调色板颜色网络,用于摄影现实风格编辑.
- 实现了一个不受限制的颜色网络,用于非摄影现实主义编辑的知识蒸.
- 利用神经辐射场 (NeRF) 进行表达式体积可视化.
主要成果:
- StyleRF-VolVis在体积数据中准确地分离了内容和风格.
- 该框架允许方便地修改视觉属性,如颜色和不透明度.
- 与现有方法 (AdaIN,ReReVST,ARF,SNeRF) 相比,证明了更高的质量,一致性和灵活性.
- 成功地将任意风格转移到重建的3D场景中.
结论:
- StyleRF-VolVis为表达式体积可视化提供了一种有效和灵活的方法.
- 提出的方法克服了以前基于GAN和基于NeRF的风格转移技术的局限性.
- StyleRF-VolVis推进了用于科学可视化的神经染领域.
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