用光线追踪训练神经网络，高效还原复杂灯具的光照效果。
PureLight: Learning Complex Luminaires with Light Tracing
- 通过光线追踪构建路径，将灯具外观建模为分布学习问题
- 仅需少量采样即可在任意场景中渲染复杂灯具
- 适合需要高精度灯具模拟的影视与游戏渲染领域
我们提出一种神经方法来估计复杂灯具的视觉外观。针对包含多个镜面层的小光源等难以用（双向）路径追踪处理的复杂光传输情况，我们采用光线追踪从光源到出射表面构建路径，并将外观估计问题建模为分布学习。具体而言，使用大型归一化流网络对出射表面的出射辐射率概率密度函数（pdf）进行建模，并通过估计的pdf与通量的乘积恢复出射辐射率。为实现高效推理，我们将学习到的外观压缩为轻量级MLP，直接预测出射表面的辐射率。此外，我们还训练了采样网络以实现高效的直接照明计算，以及混合网络将灯具无缝融入场景。该方法使得在任意场景中仅用低样本数即可渲染具有挑战性的灯具。
原文摘要 · Abstract (English)
We propose a neural formulation for estimating the appearance of complex luminaires. We focus on challenging luminaires with complex light transport (e.g., small emitters enclosed by multiple specular layers) that are difficult for (bidirectional) path tracing. To this end, we use light tracing to construct paths from emitters to the exit surfaces and formulate appearance estimation as a distribution learning problem. Specifically, we model the probability density function (pdf) of outgoing radiance on the exit surfaces using a large normalizing flow network, and recover the outgoing radiance as the product of the estimated pdf and flux. To enable efficient inference, we distill the learned appearance into a lightweight MLP that directly estimates radiance on the exit surfaces. We additionally train a sampling network for effective direct illumination computation from the luminaire, and a blending network to composite the luminaire into the scene. Our formulation makes it feasible to render challenging luminaires using low sample counts in arbitrary scenes.
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