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复现多种真实材质随空间变化的表观特性。然而，现有技术（如NeuMIP）在处理具有强烈阴影效应或复杂镜面高光的材质时存在困难。本文提出一种新型神经表观模型，显著提升了精度。该模型的核心是采用基于初始结构的核心网络架构：通过并行运算内核捕获多尺度材质表观，并借助专用卷积层确保多阶段特征提取。此外，我们将输入编码至频域空间，引入梯度损失函数，并根据学习阶段动态调整其权重。通过合成数据与真实案例的对比实验，验证了该方法的有效性。