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.

翻译：我们提出一种神经公式来估计复杂灯具的外观。我们聚焦于具有复杂光传输（例如由多个镜面层包裹的小型发射器）的挑战性灯具，这些灯具对于（双向）路径追踪而言难以处理。为此，我们采用光线追踪法构建从发射器到出射表面的路径，并将外观估计视为一个分布学习问题。具体而言，我们使用大型归一化流网络对出射表面上的出射辐射概率密度函数(pdf)进行建模，并通过估计的pdf与光通量的乘积恢复出射辐射。为提高推理效率，我们将学习到的外观蒸馏为一个轻量级多层感知机，该感知机直接估计出射表面上的辐射。此外，我们训练一个采样网络以高效计算灯具的直接照明，并训练一个融合网络将灯具合成到场景中。我们的公式使得在任意场景中以低采样数渲染挑战性灯具成为可能。