High-fidelity 3D assets exhibit intriguing global illumination effects like subsurface scattering, glossy interreflections, and fine-scale fiber scatterings, which often involve long scattering paths that are expensive to simulate. We introduce 8D neural assets (8DNA) to pre-bake these light transport effects into neural representations. Unlike prior methods that assume far-field lighting and precompute light transport into 6D functions, 8DNA learns the full 8D light transport, enabling accurate rendering under near-field illumination. Our training leverages a distribution-learning formulation that learns light transport from forward path-traced samples, which produces less optimization variance with lower training budget than the prior regression-based approaches. Experiments show our 8DNA rendering closely matches path-traced results under various scene configurations, yet it achieves improved variance reduction and fast inference speeds on challenging assets.

翻译：高保真3D资产呈现出引人入胜的全局光照效果，如次表面散射、光泽相互反射和精细尺度纤维散射，这些效果通常涉及模拟成本高昂的长散射路径。我们引入8维神经资产（8DNA）将这类光传输效应预烘焙至神经表征中。不同于假设远场光照并将光传输预计算为6维函数的既有方法，8DNA学习完整的8维光传输，从而能够实现近场光照下的精确渲染。我们的训练采用一种基于分布学习的公式，该方法从正向路径追踪样本中学习光传输，相较于基于回归的既有方法，能在更低训练预算下产生更少的优化方差。实验表明，在各种场景配置下，我们的8DNA渲染结果与路径追踪结果高度吻合，同时在挑战性资产上实现了更优的方差缩减和快速推理速度。