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。

NeuBTF：神经场用于 BTF 编码和传输

NeuBTF: Neural fields for BTF encoding and transfer

Authoring high-quality digital materials is key to realism in 3D rendering.
Previous generative models for materials have been trained exclusively on
synthetic data; such data is limited in availability and has a visual gap to
real materials. We circumvent this limitation by proposing PhotoMat: the first
material generator trained exclusively on real photos of material samples
captured using a cell phone camera with flash. Supervision on individual
material maps is not available in this setting. Instead, we train a generator
for a neural material representation that is rendered with a learned relighting
module to create arbitrarily lit RGB images; these are compared against real
photos using a discriminator. We then train a material maps estimator to decode
material reflectance properties from the neural material representation. We
train PhotoMat with a new dataset of 12,000 material photos captured with
handheld phone cameras under flash lighting. We demonstrate that our generated
materials have better visual quality than previous material generators trained
on synthetic data. Moreover, we can fit analytical material models to closely
match these generated neural materials, thus allowing for further editing and
use in 3D rendering.

本研究提出了一种基于真实材料照片的首个材料生成器 PhotoMat，通过训练一个神经材料表征的生成器来创建任意光照的材料，从而弥补了不可用的合成数据，使得生成的材料在视觉上更加真实和精细，从而实现更加高质量的 3D 渲染。