We propose a novel compact and efficient neural BRDF offering highly versatile material representation, yet with very-light memory and neural computation consumption towards achieving real-time rendering. The results in Figure 1, rendered at full HD resolution on a current desktop machine, show that our system achieves real-time rendering with a wide variety of appearances, which is approached by the following two designs. On the one hand, noting that bidirectional reflectance is distributed in a very sparse high-dimensional subspace, we propose to project the BRDF into two low-dimensional components, i.e., two hemisphere feature-grids for incoming and outgoing directions, respectively. On the other hand, learnable neural reflectance primitives are distributed on our highly-tailored spherical surface grid, which offer informative features for each component and alleviate the conventional heavy feature learning network to a much smaller one, leading to very fast evaluation. These primitives are centrally stored in a codebook and can be shared across multiple grids and even across materials, based on the low-cost indices stored in material-specific spherical surface grids. Our neural BRDF, which is agnostic to the material, provides a unified framework that can represent a variety of materials in consistent manner. Comprehensive experimental results on measured BRDF compression, Monte Carlo simulated BRDF acceleration, and extension to spatially varying effect demonstrate the superior quality and generalizability achieved by the proposed scheme.

翻译：我们提出了一种新颖的紧凑高效神经BRDF，能够提供高度灵活的材料表达，同时以极轻量的内存和神经计算消耗实现实时渲染。在当前桌面计算机上以全高清分辨率渲染的图1结果表明，我们的系统能够以多种外观实现实时渲染，这通过以下两种设计实现。一方面，注意到双向反射分布函数分布在一个非常稀疏的高维子空间中，我们提出将BRDF投影到两个低维分量中，即分别用于入射方向和出射方向的两个半球特征网格。另一方面，可学习的神经反射基元分布在我们高度定制的球面表面网格上，为每个分量提供信息丰富的特征，并将传统沉重的特征学习网络简化为更小的网络，从而实现了极快的评估。这些基元集中存储在码本中，可以基于存储在材料特定球面表面网格中的低成本索引，跨多个网格甚至跨材料共享。我们的神经BRDF对材料无关，提供了一个统一的框架，能够以一致的方式表达多种材料。在实测BRDF压缩、蒙特卡洛模拟BRDF加速以及扩展到空间变化效果方面的综合实验结果，证明了所提出方案实现了优越的质量和泛化能力。