Importance sampling is one of the most widely used variance reduction strategies in Monte Carlo rendering. In this paper, we propose a novel importance sampling technique that uses a neural network to learn how to sample from a desired density represented by a set of samples. Our approach considers an existing Monte Carlo rendering algorithm as a black box. During a scene-dependent training phase, we learn to generate samples with a desired density in the primary sample space of the rendering algorithm using maximum likelihood estimation. We leverage a recent neural network architecture that was designed to represent real-valued non-volume preserving ('Real NVP') transformations in high dimensional spaces. We use Real NVP to non-linearly warp primary sample space and obtain desired densities. In addition, Real NVP efficiently computes the determinant of the Jacobian of the warp, which is required to implement the change of integration variables implied by the warp. A main advantage of our approach is that it is agnostic of underlying light transport effects, and can be combined with many existing rendering techniques by treating them as a black box. We show that our approach leads to effective variance reduction in several practical scenarios.

翻译：重要性采样是蒙特卡洛渲染中最广泛使用的方差缩减策略之一。本文提出一种新型重要性采样技术，利用神经网络学习如何从样本集合所代表的目标概率密度中进行采样。我们将现有蒙特卡洛渲染算法视为黑箱。在场景相关训练阶段，通过最大似然估计学习在渲染算法的原始样本空间中生成具有目标密度的样本。我们采用近期提出的高维空间实值非体积保持变换（'Real NVP'）神经网络架构。使用Real NVP对原始样本空间进行非线性扭曲以获得目标密度。此外，Real NVP能够高效计算扭曲的雅可比行列式，这是实现扭曲所隐含的积分变量变换的关键。本方法的主要优势在于与底层光传输效应无关，可将现有渲染算法视为黑箱与之结合。实验表明，本方法在多个实际场景中实现了有效的方差缩减。