Volume data is commonly found in many scientific disciplines, like medicine, physics, and biology. Experts rely on robust scientific visualization techniques to extract valuable insights from the data. Recent years have shown path tracing to be the preferred approach for volumetric rendering, given its high levels of realism. However, real-time volumetric path tracing often suffers from stochastic noise and long convergence times, limiting interactive exploration. In this paper, we present a novel method to enable real-time global illumination for volume data visualization. We develop Photon Field Networks -- a phase-function-aware, multi-light neural representation of indirect volumetric global illumination. The fields are trained on multi-phase photon caches that we compute a priori. Training can be done within seconds, after which the fields can be used in various rendering tasks. To showcase their potential, we develop a custom neural path tracer, with which our photon fields achieve interactive framerates even on large datasets. We conduct in-depth evaluations of the method's performance, including visual quality, stochastic noise, inference and rendering speeds, and accuracy regarding illumination and phase function awareness. Results are compared to ray marching, path tracing and photon mapping. Our findings show that Photon Field Networks can faithfully represent indirect global illumination across the phase spectrum while exhibiting less stochastic noise and rendering at a significantly faster rate than traditional methods.

翻译：体数据广泛存在于医学、物理学和生物学等众多科学领域中。专家依赖鲁棒的科学可视化技术从数据中提取有价值的信息。近年来，路径追踪因其高度的真实感已成为体渲染的首选方法。然而，实时体路径追踪常常受到随机噪声和较长收敛时间的困扰，限制了交互式探索。本文提出了一种新方法，以实现体数据可视化的实时全局光照。我们开发了光子场网络——一种基于相位函数感知的多光源神经表示，用于间接体全局光照。这些场基于预先计算的多样本光子缓存进行训练，训练可在数秒内完成，此后该场可用于各种渲染任务。为展示其潜力，我们开发了一个自定义神经路径追踪器，即便在大型数据集上，我们的光子场也能实现交互式帧率。我们对该方法的性能进行了深入评估，包括视觉质量、随机噪声、推理与渲染速度，以及光照与相位函数感知的准确性。结果与光线步进、路径追踪和光子映射进行了对比。研究表明，光子场网络能在整个相位光谱范围内忠实地表示间接全局光照，且相比传统方法，其随机噪声更少，渲染速度显著更快。