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.

本篇论文提出了一种名为 Photon Fields Networks 的新方法，能够实现基于体数据的全局实时光照。该方法基于神经网络，通过预先计算的多相位光子缓存进行训练，取得了优异的表现，并能够在大型数据集上实现交互式渲染。