Controlled capture of real-world material appearance yields tabulated sets of highly realistic reflectance data. In practice, however, its high memory footprint requires compressing into a representation that can be used efficiently in rendering while remaining faithful to the original. Previous works in appearance encoding often prioritized one of these requirements at the expense of the other, by either applying high-fidelity array compression strategies not suited for efficient queries during rendering, or by fitting a compact analytic model that lacks expressiveness. We present a compact neural network-based representation of BRDF data that combines high-accuracy reconstruction with efficient practical rendering via built-in interpolation of reflectance. We encode BRDFs as lightweight networks, and propose a training scheme with adaptive angular sampling, critical for the accurate reconstruction of specular highlights. Additionally, we propose a novel approach to make our representation amenable to importance sampling: rather than inverting the trained networks, we learn to encode them in a more compact embedding that can be mapped to parameters of an analytic BRDF for which importance sampling is known. We evaluate encoding results on isotropic and anisotropic BRDFs from multiple real-world datasets, and importance sampling performance for isotropic BRDFs mapped to two different analytic models.

中文翻译：

---

神经 BRDF 表示和重要性采样

真实世界材料外观的受控捕获产生高度逼真的反射率数据的列表集。然而，在实践中，它的高内存占用需要压缩成一种可以在渲染中有效使用的表示，同时保持对原始的忠实。以前在外观编码方面的工作通常优先考虑这些要求中的一个，以牺牲另一个为代价，方法是应用不适合渲染期间高效查询的高保真数组压缩策略，或者通过拟合缺乏表现力的紧凑分析模型。我们提出了一种基于紧凑型神经网络的 BRDF 数据表示，它通过内置的反射率插值将高精度重建与有效的实际渲染相结合。我们将 BRDF 编码为轻量级网络，并提出了一种具有自适应角度采样的训练方案，这对于准确重建镜面高光至关重要。此外，我们提出了一种新方法，使我们的表示适合重要性采样：我们学习将它们编码为更紧凑的嵌入，而不是反转训练过的网络，该嵌入可以映射到重要性采样已知的分析 BRDF 的参数。我们评估来自多个真实世界数据集的各向同性和各向异性 BRDF 的编码结果，以及映射到两个不同分析模型的各向同性 BRDF 的重要性采样性能。我们学习用更紧凑的嵌入对它们进行编码，该嵌入可以映射到重要性采样已知的分析 BRDF 的参数。我们评估来自多个真实世界数据集的各向同性和各向异性 BRDF 的编码结果，以及映射到两个不同分析模型的各向同性 BRDF 的重要性采样性能。我们学习用更紧凑的嵌入对它们进行编码，该嵌入可以映射到重要性采样已知的分析 BRDF 的参数。我们评估来自多个真实世界数据集的各向同性和各向异性 BRDF 的编码结果，以及映射到两个不同分析模型的各向同性 BRDF 的重要性采样性能。