Neural material representations are becoming a popular way to represent materials for rendering. They are more expressive than analytic models and occupy less memory than tabulated BTFs. However, existing neural materials are immutable, meaning that their output for a certain query of UVs, camera, and light vector is fixed once they are trained. While this is practical when there is no need to edit the material, it can become very limiting when the fragment of the material used for training is too small or not tileable, which frequently happens when the material has been captured with a gonioreflectometer. In this paper, we propose a novel neural material representation which jointly tackles the problems of BTF compression, tiling, and extrapolation. At test time, our method uses a guidance image as input to condition the neural BTF to the structural features of this input image. Then, the neural BTF can be queried as a regular BTF using UVs, camera, and light vectors. Every component in our framework is purposefully designed to maximize BTF encoding quality at minimal parameter count and computational complexity, achieving competitive compression rates compared with previous work. We demonstrate the results of our method on a variety of synthetic and captured materials, showing its generality and capacity to learn to represent many optical properties.

翻译：神经材质表示正成为渲染领域中流行的材质表示方式。它们比解析模型更具表现力，且相比表格化BTF占用更少内存。然而，现有神经材质具有不可变性，即一旦训练完成，针对特定UV坐标、相机及光线方向的查询输出便固定不变。虽然这种特性在无需编辑材质时较为实用，但当用于训练的材质片段过小或不可平铺时（例如使用测角光度计捕获材质时常出现的情况），则会带来极大限制。本文提出一种新型神经材质表示方法，可联合解决BTF压缩、平铺与外推问题。在测试阶段，本方法以引导图像作为输入，使神经BTF适应输入图像的结构特征。随后，该神经BTF可像常规BTF一样通过UV坐标、相机及光线方向进行查询。我们框架中的每个组件均经过精心设计，旨在以最小参数量和计算复杂度最大化BTF编码质量，相比前人工作实现了具有竞争力的压缩率。我们在一系列合成与捕获材质上验证了本方法的结果，展示了其通用性与学习表征多种光学特性的能力。