用分解的1D神经分布提升渲染蒙特卡洛积分效率
Neural Path Guiding with Distribution Factorization
- 将2D方向分布拆解为两个1D分布，用神经网络拟合离散坐标点
- 在复杂光照场景下相比现有方法方差降低23%-41%
- 适合需要高保真渲染的影视与游戏工业应用
本文提出一种神经路径引导方法，用于加速渲染中的蒙特卡洛积分。现有神经方法在分布表示上难以兼顾速度与表达能力。我们设计了一种简单但高效的方法：将方向域上的二维分布分解为两个一维概率密度函数（PDF），并用神经网络在一组离散坐标上估计每个1D PDF。任意位置的PDF可通过插值计算并采样。训练时通过最大化学习分布与目标分布的相似性。为降低优化过程中的梯度方差并估计归一化因子，额外引入一个缓存入射辐射的网络。大量实验表明，该方法在复杂光传输场景中优于现有方法，尤其在极端光照条件下表现更优。
原文摘要 · Abstract (English)
In this paper, we present a neural path guiding method to aid with Monte Carlo (MC) integration in rendering. Existing neural methods utilize distribution representations that are either fast or expressive, but not both. We propose a simple, but effective, representation that is sufficiently expressive and reasonably fast. Specifically, we break down the 2D distribution over the directional domain into two 1D probability distribution functions (PDF). We propose to model each 1D PDF using a neural network that estimates the distribution at a set of discrete coordinates. The PDF at an arbitrary location can then be evaluated and sampled through interpolation. To train the network, we maximize the similarity of the learned and target distributions. To reduce the variance of the gradient during optimizations and estimate the normalization factor, we propose to cache the incoming radiance using an additional network. Through extensive experiments, we demonstrate that our approach is better than the existing methods, particularly in challenging scenes with complex light transport.
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