Material reconstruction from a photograph is a key component of 3D content
creation democratization. We propose to formulate this ill-posed problem as a
controlled synthesis one, leveraging the recent progress in generative deep
networks. We present ControlMat, a method which, given a single photograph with
uncontrolled illumination as input, conditions a diffusion model to generate
plausible, tileable, high-resolution physically-based digital materials. We
carefully analyze the behavior of diffusion models for multi-channel outputs,
adapt the sampling process to fuse multi-scale information and introduce rolled
diffusion to enable both tileability and patched diffusion for high-resolution
outputs. Our generative approach further permits exploration of a variety of
materials which could correspond to the input image, mitigating the unknown
lighting conditions. We show that our approach outperforms recent inference and
latent-space-optimization methods, and carefully validate our diffusion process
design choices. Supplemental materials and additional details are available at:
this https URL

通过生成深度网络和扩散模型，我们提出了一种方法来重建材料图像，使其生成逼真、平铺和高分辨率的基于物理的数码材料。

ControlMat: 材质捕捉的受控生成方法

ControlMat: A Controlled Generative Approach to Material Capture

Modern computer vision algorithms have brought significant advancement to 3D
geometry reconstruction. However, illumination and material reconstruction
remain less studied, with current approaches assuming very simplified models
for materials and illumination. We introduce Inverse Path Tracing, a novel
approach to jointly estimate the material properties of objects and light
sources in indoor scenes by using an invertible light transport simulation. We
assume a coarse geometry scan, along with corresponding images and camera
poses. The key contribution of this work is an accurate and simultaneous
retrieval of light sources and physically based material properties (e.g.,
diffuse reflectance, specular reflectance, roughness, etc.) for the purpose of
editing and re-rendering the scene under new conditions. To this end, we
introduce a novel optimization method using a differentiable Monte Carlo
renderer that computes derivatives with respect to the estimated unknown
illumination and material properties. This enables joint optimization for
physically correct light transport and material models using a tailored
stochastic gradient descent.

Inverse Path Tracing 通过可逆光传输模拟，提出了一种新颖的方法，可以在保证物理正确性的情况下，同时获取室内场景中的光源和材料属性，并使用可微分的蒙特卡罗渲染器进行优化，以实现编辑和重渲染。