Neural reflectance models are capable of reproducing the spatially-varying appearance of many real-world materials at different scales. Unfortunately, existing techniques such as NeuMIP have difficulties handling materials with strong shadowing effects or detailed specular highlights. In this paper, we introduce a neural appearance model that offers a new level of accuracy. Central to our model is an inception-based core network structure that captures material appearances at multiple scales using parallel-operating kernels and ensures multi-stage features through specialized convolution layers. Furthermore, we encode the inputs into frequency space, introduce a gradient-based loss, and employ it adaptive to the progress of the learning phase. We demonstrate the effectiveness of our method using a variety of synthetic and real examples.

翻译：神经反射模型能够以不同尺度再现许多真实世界材质的空间变化外观。然而，现有的技术如NeuMIP在处理具有强烈阴影效应或精细高光细节的材质时存在困难。本文提出了一种神经外观模型，提供了新的精度水平。该模型的核心是基于初始化的核心网络结构，通过并行运行的内核捕捉多尺度的材质外观，并通过专门的卷积层确保多阶段特征。此外，我们将输入编码到频率空间，引入基于梯度的损失函数，并根据学习阶段的进展自适应地使用该函数。我们通过多种合成和真实示例展示了该方法的有效性。