Traditional physically-based material models rely on analytically derived bidirectional reflectance distribution functions (BRDFs), typically by considering statistics of micro-primitives such as facets, flakes, or spheres, sometimes combined with multi-bounce interactions such as layering and multiple scattering. These derivations are often complex and model-specific, and typically consider a statistical aggregate of a large surface area, ignoring spatial variation. Once an analytic BRDF's evaluation is defined, one still needs to design an importance sampling method for it, and a way to evaluate the pdf of that sampling distribution, requiring further model-specific derivations. We present PureSample: a novel neural BRDF representation that allows learning a material's behavior purely by sampling forward random walks on the microgeometry, which is usually straightforward to implement. Our representation allows for efficient importance sampling, pdf evaluation, and BRDF evaluation, for homogeneous as well as spatially varying materials. We achieve this by two learnable components: first, the sampling distribution is modeled using a flow matching neural network, which allows both importance sampling and pdf evaluation; second, we introduce a view-dependent albedo term, captured by a lightweight neural network, which allows for converting a scalar pdf value to a colored BRDF value for any pair of view and light directions. We demonstrate PureSample on challenging materials, including multi-layered materials, multiple-scattering microfacet materials, and various other microstructures.

翻译：传统的基于物理的材质模型依赖于解析推导的双向反射分布函数（BRDF），通常通过考虑微元（如微面、薄片或球体）的统计特性来构建，有时还结合多层结构与多重散射等多重相互作用。这些推导过程通常复杂且模型特定，并且一般考虑大面积表面的统计聚合，忽略了空间变化性。即使定义了解析BRDF的求值方法，仍需为其设计重要性采样策略以及评估该采样分布概率密度函数的方法，这需要进一步的模型特定推导。本文提出PureSample：一种新颖的神经BRDF表示方法，能够纯粹通过对微几何上的随机游走进行前向采样来学习材质行为，而此类采样通常易于实现。我们的表示方法支持对均匀材质及空间变化材质进行高效的重要性采样、概率密度函数评估与BRDF求值。这一目标通过两个可学习组件实现：首先，采用流匹配神经网络对采样分布进行建模，同时支持重要性采样与概率密度函数评估；其次，我们引入了由轻量级神经网络捕获的视角相关反照率项，该组件可将标量概率密度值转换为任意视角-光照方向对的彩色BRDF值。我们在多层材质、多重散射微面材质及多种其他微结构等挑战性材质上验证了PureSample的有效性。