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. Once an analytic BRDF evaluation is defined, one still needs to design an importance sampling method for it and evaluate the probability density function (pdf) of that sampling distribution, requiring further model-specific derivations. We present PureSample: a novel neural BRDF representation that allows learning a material's appearance purely by sampling forward random walks on the microgeometry, which is usually straightforward to implement. Our representation allows for efficient BRDF evaluation, importance sampling, and pdf 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 pdf value to a BRDF value for any pair of view and light directions. We demonstrate PureSample on challenging materials, including various microgeometries, multi-layered materials, and multiple-scattering microfacet materials.

翻译：摘要：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），通常通过考虑微基元（如小面、薄片或球体）的统计特性，有时结合多层和多次散射等多重弹射相互作用。这些推导过程往往复杂且具有模型特异性。一旦定义了解析BRDF评估方法，仍需为其设计重要性采样方法并评估该采样分布的概率密度函数（pdf），这需要进一步的模型特异性推导。我们提出PureSample：一种新型神经BRDF表示方法，它通过纯粹对微几何进行正向随机游走采样（通常易于实现）来学习材质外观。我们的表示方法能够对均质和空间变化材质实现高效的BRDF评估、重要性采样以及pdf评估。我们通过两个可学习组件实现这一目标：首先，采用流匹配神经网络对采样分布进行建模，使其同时支持重要性采样和pdf评估；其次，引入由轻量级神经网络捕获的视角相关反照率项，从而能够将任意视角与入射光方向组合的pdf值转换为BRDF值。我们通过包含多种微几何结构、多层材质以及多次散射微面材质的具挑战性示例，展示了PureSample的有效性。