The variance reduction speed of physically-based rendering is heavily affected by the adopted importance sampling technique. In this paper we propose a novel online framework to learn the spatial-varying density model with a single small neural network using stochastic ray samples. To achieve this task, we propose a novel closed-form density model called the normalized anisotropic spherical gaussian mixture, that can express complex irradiance fields with a small number of parameters. Our framework learns the distribution in a progressive manner and does not need any warm-up phases. Due to the compact and expressive representation of our density model, our framework can be implemented entirely on the GPU, allowing it produce high quality images with limited computational resources.

翻译：物理基础渲染的方差缩减速度在很大程度上取决于所采用的重要性采样技术。本文提出了一种新颖的在线框架，通过使用随机光线样本，借助单个小型神经网络学习空间变化的密度模型。为实现这一目标，我们提出了一种称为归一化各向异性球面高斯混合的新型闭式密度模型，该模型能够用少量参数表达复杂的辐照度场。我们的框架以渐进方式学习分布，无需任何预热阶段。由于密度模型的紧凑性和高表达能力，该框架可完全在GPU上实现，从而在有限计算资源下生成高质量图像。