We present a complete system for real-time rendering of scenes with complex appearance previously reserved for offline use. This is achieved with a combination of algorithmic and system level innovations.

Our appearance model utilizes learned hierarchical textures that are interpreted using neural decoders, which produce reflectance values and importance-sampled directions. To best utilize the modeling capacity of the decoders, we equip the decoders with two graphics priors. The first prior—transformation of directions into learned shading frames—facilitates accurate reconstruction of mesoscale effects. The second prior—a microfacet sampling distribution—allows the neural decoder to perform importance sampling efficiently. The resulting appearance model supports anisotropic sampling and level-of-detail rendering, and allows baking deeply layered material graphs into a compact unified neural representation.

By exposing hardware accelerated tensor operations to ray tracing shaders, we show that it is possible to inline and execute the neural decoders efficiently inside a real-time path tracer. We analyze scalability with increasing number of neural materials and propose to improve performance using code optimized for coherent and divergent execution. Our neural material shaders can be over an order of magnitude faster than non-neural layered materials. This opens up the door for using film-quality visuals in real-time applications such as games and live previews.

我们提出了一个完整的系统，用于实时渲染以前保留供离线使用的复杂外观场景。这是通过算法和系统级创新的结合来实现的。

我们的外观模型利用学习到的分层纹理，这些纹理使用神经解码器进行解释，产生反射率值和重要性采样方向。为了最好地利用解码器的建模能力，我们为解码器配备了两个图形先验。第一个先验——将方向转换为学习的着色帧——有助于准确重建中尺度效应。第二个先验——微面采样分布——允许神经解码器有效地执行重要性采样。由此产生的外观模型支持各向异性采样和细节层次渲染，并允许将深层分层材质图烘焙成紧凑的统一神经表示。

通过将硬件加速张量操作暴露给光线追踪着色器，我们证明可以在实时路径追踪器中有效地内联和执行神经解码器。我们分析了越来越多的神经材料的可扩展性，并建议使用针对一致和发散执行而优化的代码来提高性能。我们的神经材质着色器比非神经分层材质快一个数量级。这为在游戏和实时预览等实时应用中使用电影质量的视觉效果打开了大门。