Fiber-level Woven Fabric Capture from a Single Photo
<|reference_start|>Fiber-level Woven Fabric Capture from a Single Photo: Accurately rendering the appearance of fabrics is challenging, due to their complex 3D microstructures and specialized optical properties. If we model the geometry and optics of fabrics down to the fiber level, we can achieve unprecedented rendering realism, but this raises the difficulty of authoring or capturing the fiber-level assets. Existing approaches can obtain fiber-level geometry with special devices (e.g., CT) or complex hand-designed procedural pipelines (manually tweaking a set of parameters). In this paper, we propose a unified framework to capture fiber-level geometry and appearance of woven fabrics using a single low-cost microscope image. We first use a simple neural network to predict initial parameters of our geometric and appearance models. From this starting point, we further optimize the parameters of procedural fiber geometry and an approximated shading model via differentiable rasterization to match the microscope photo more accurately. Finally, we refine the fiber appearance parameters via differentiable path tracing, converging to accurate fiber optical parameters, which are suitable for physically-based light simulations to produce high-quality rendered results. We believe that our method is the first to utilize differentiable rendering at the microscopic level, supporting physically-based scattering from explicit fiber assemblies. Our fabric parameter estimation achieves high-quality re-rendering of measured woven fabric samples in both distant and close-up views. These results can further be used for efficient rendering or converted to downstream representations. We also propose a patch-space fiber geometry procedural generation and a two-scale path tracing framework for efficient rendering of fabric scenes.<|reference_end|>
@article{li2024fiber-level,
    title={Fiber-level Woven Fabric Capture from a Single Photo},
    author={Zixuan Li, Pengfei Shen, Hanxiao Sun, Zibo Zhang, Yu Guo, Ligang Liu,
  Ling-Qi Yan, Steve Marschner, Milos Hasan, Beibei Wang},
    journal={arXiv preprint arXiv:2409.06368},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06368},
    primaryClass={cs.GR}
}