Gaussian Product Sampling for Rendering Layered Materials,Computer Graphics Forum

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Gaussian Product Sampling for Rendering Layered Materials
Computer Graphics Forum ( IF 2.7 ) Pub Date : 2019-10-31 , DOI: 10.1111/cgf.13883
Mengqi (Mandy) Xia ₁ , Bruce Walter ₁ , Christophe Hery _{2,

3} , Steve Marschner ₁

Affiliation

To increase diversity and realism, surface bidirectional scattering distribution functions (BSDFs) are often modelled as consisting of multiple layers, but accurately evaluating layered BSDFs while accounting for all light transport paths is a challenging problem. Recently, Guo et al. [GHZ18] proposed an accurate and general position‐free Monte Carlo method, but this method introduces variance that leads to longer render time compared to non‐stochastic layered models. We improve the previous work by presenting two new sampling strategies, pair‐product sampling and multiple‐product sampling. Our new methods better take advantage of the layered structure and reduce variance compared to the conventional approach of sequentially sampling one BSDF at a time. Our pair‐product sampling strategy importance samples the product of two BSDFs from a pair of adjacent layers. We further generalize this to multiple‐product sampling, which importance samples the product of a chain of three or more BSDFs. In order to compute these products, we developed a new approximate Gaussian representation of individual layer BSDFs. This representation incorporates spatially varying material properties as parameters so that our techniques can support an arbitrary number of textured layers. Compared to previous Monte Carlo layering approaches, our results demonstrate substantial variance reduction in rendering isotropic layered surfaces.

中文翻译：

用于渲染分层材料的高斯乘积采样

为了增加多样性和真实性，表面双向散射分布函数 (BSDF) 通常被建模为由多个层组成，但是在考虑所有光传输路径的同时准确评估分层 BSDF 是一个具有挑战性的问题。最近，郭等人。[GHZ18] 提出了一种准确且通用的无位置蒙特卡罗方法，但与非随机分层模型相比，该方法引入了导致渲染时间更长的方差。我们通过提出两种新的抽样策略，对产品抽样和多产品抽样来改进以前的工作。与一次顺序采样一个 BSDF 的传统方法相比，我们的新方法更好地利用了分层结构并减少了方差。我们的对积采样策略重要性从一对相邻层中采样两个 BSDF 的乘积。我们进一步将其推广到多产品采样，其重要性对三个或更多 BSDF 链的产品进行采样。为了计算这些乘积，我们开发了一种新的单层 BSDF 近似高斯表示。这种表示结合了空间变化的材料特性作为参数，因此我们的技术可以支持任意数量的纹理层。与之前的蒙特卡罗分层方法相比，我们的结果表明在渲染各向同性分层表面时显着减少了方差。我们开发了一种新的单层 BSDF 近似高斯表示。这种表示结合了空间变化的材料特性作为参数，因此我们的技术可以支持任意数量的纹理层。与之前的蒙特卡罗分层方法相比，我们的结果表明在渲染各向同性分层表面时显着减少了方差。我们开发了一种新的单层 BSDF 近似高斯表示。这种表示结合了空间变化的材料特性作为参数，因此我们的技术可以支持任意数量的纹理层。与之前的蒙特卡罗分层方法相比，我们的结果表明在渲染各向同性分层表面时显着减少了方差。

更新日期：2019-10-31

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