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 prioritised 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 an 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：

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神经BRDF表示和重要性采样

现实世界中材料外观的受控捕获会产生高度现实的反射率数据列表。但是，实际上，它的高内存占用量需要压缩成可以在渲染时有效使用的表示形式，同时又要忠实于原始图像。外观编码的先前工作通常通过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达能力的紧凑分析模型，优先考虑这些要求中的一个，而以另一个为代价。我们提出了一种基于神经网络的BRDF数据的紧凑表示形式，该数据通过内置的反射率插值将高精度重建与有效的实际渲染结合在一起。我们将BRDF编码为轻量级网络，并提出一种具有自适应角度采样的训练方案，这对于镜面高光的准确重建至关重要。此外，我们提出了一种新颖的方法来使我们的表示适合于重要性抽样：我们无需反转训练后的网络，而是学习可以映射到已知重要性抽样的解析BRDF参数的嵌入。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。我们学习了一种嵌入，该嵌入可以映射到已知重要性采样的解析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。我们学习了一种嵌入，该嵌入可以映射到已知重要性采样的解析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。