StyleRF-VolVis：用于表现力体可视化的神经辐射场风格迁移
StyleRF-VolVis: Style Transfer of Neural Radiance Fields for Expressive Volume Visualization
AI总结：
本文提出 StyleRF-VolVis，一种基于 NeRF 的体可视化风格迁移框架，通过几何提取、调色板与无约束颜色网络实现一致且灵活的任意三维风格化。
摘要：
在体可视化中，可视化合成因其能够在不遵循传统渲染流程的情况下生成新颖可视化而备受关注。然而，现有基于生成对抗网络的解决方案通常需要大量训练图像，并耗费显著的训练时间。低质量、一致性和灵活性等问题仍然存在。本文介绍 StyleRF-VolVis，这是一个通过神经辐射场（NeRF）实现表现力体可视化（VolVis）的创新风格迁移框架。StyleRF-VolVis 的表现力体现在：它能够准确分离底层场景几何（即内容）与颜色外观（即风格），方便地修改原始渲染的颜色、不透明度和光照，同时保持跨视图的视觉内容一致性，并有效地将参考图像中的任意风格迁移到重建的三维场景。为实现这些目标，我们设计了用于场景几何提取的基础 NeRF 模型、用于对辐射场区域进行分类以实现照片级真实编辑的调色板颜色网络，以及通过知识蒸馏解除调色板约束以实现非照片级真实感编辑的无约束颜色网络。我们通过在各种体渲染场景和参考图像上进行实验，并将 StyleRF-VolVis 与其他基于图像的（AdaIN）、基于视频的（ReReVST）以及基于 NeRF 的（ARF 和 SNeRF）风格渲染解决方案进行比较，证明了 StyleRF-VolVis 卓越的质量、一致性和灵活性。
英文摘要：
In volume visualization, visualization synthesis has attracted much attention due to its ability to generate novel visualizations without following the conventional rendering pipeline. However, existing solutions based on generative adversarial networks often require many training images and take significant training time. Still, issues such as low quality, consistency, and flexibility persist. This paper introduces StyleRF-VolVis, an innovative style transfer framework for expressive volume visualization (VolVis) via neural radiance field (NeRF). The expressiveness of StyleRF-VolVis is upheld by its ability to accurately separate the underlying scene geometry (i.e., content) and color appearance (i.e., style), conveniently modify color, opacity, and lighting of the original rendering while maintaining visual content consistency across the views, and effectively transfer arbitrary styles from reference images to the reconstructed 3D scene. To achieve these, we design a base NeRF model for scene geometry extraction, a palette color network to classify regions of the radiance field for photorealistic editing, and an unrestricted color network to lift the color palette constraint via knowledge distillation for non-photorealistic editing. We demonstrate the superior quality, consistency, and flexibility of StyleRF-VolVis by experimenting with various volume rendering scenes and reference images and comparing StyleRF-VolVis against other image-based (AdaIN), video-based (ReReVST), and NeRF-based (ARF and SNeRF) style rendering solutions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
