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.

提出了一种新颖的神经材料表示方法，能够压缩、平铺和外推 Bidirectional Texture Functions (双向纹理函数)，并能够学习表示许多光学特性。