Importance sampling techniques significantly reduce variance in physically based rendering. In this article, we propose a novel online framework to learn the spatial-varying distribution of the full product of the rendering equation, with a single small neural network using stochastic ray samples. The learned distributions can be used to efficiently sample the full product of incident light. To accomplish this, we introduce a novel closed-form density model, called the Normalized Anisotropic Spherical Gaussian mixture, that can model a complex light field with a small number of parameters and that can be directly sampled. Our framework progressively renders and learns the distribution, without requiring any warm-up phases. With the compact and expressive representation of our density model, our framework can be implemented entirely on the GPU, allowing it to produce high-quality images with limited computational resources. The results show that our framework outperforms existing neural path guiding approaches and achieves comparable or even better performance than state-of-the-art online statistical path guiding techniques.

重要性采样技术显着减少了基于物理的渲染的差异。在本文中，我们提出了一种新颖的在线框架，通过使用随机射线样本的单个小型神经网络来学习渲染方程完整乘积的空间变化分布。学习到的分布可用于有效地对入射光的完整产物进行采样。为了实现这一目标，我们引入了一种新颖的封闭形式密度模型，称为归一化各向异性球面高斯混合，它可以用少量参数对复杂光场进行建模，并且可以直接采样。我们的框架逐步呈现并学习分布，而不需要任何预热阶段。凭借我们的密度模型的紧凑和富有表现力的表示，我们的框架可以完全在 GPU 上实现，从而使其能够用有限的计算资源生成高质量的图像。结果表明，我们的框架优于现有的神经路径引导方法，并实现了与最先进的在线统计路径引导技术相当甚至更好的性能。