从单张照片进行纤维级机织面料捕捉
Fiber-level Woven Fabric Capture from a Single Photo
AI总结：
本文提出仅用单张低成本显微镜图像重建机织面料纤维级几何与外观的统一框架，结合神经网络初始化、可微光栅化与可微路径追踪优化，并引入两尺度渲染实现高质量重渲染。
摘要：
精确渲染面料外观具有挑战性，因为其具有复杂的三维微结构和特殊的光学性质。如果我们将面料的几何与光学建模到纤维级别，就可以实现前所未有的渲染真实感，但这也提高了创作或捕捉纤维级资产的难度。现有方法需要借助特殊设备（例如 CT）或复杂的人工设计程序化流程（手动调整一组参数）才能获得纤维级几何。本文提出一个统一框架，仅使用一张低成本显微镜图像来捕捉机织面料的纤维级几何与外观。我们首先使用一个简单神经网络预测几何与外观模型的初始参数。以此为起点，我们进一步通过可微光栅化优化程序化纤维几何和近似着色模型的参数，以更精确地匹配显微镜照片。最后，我们通过可微路径追踪细化纤维外观参数，收敛到准确的纤维光学参数；这些参数适用于基于物理的光照模拟，可生成高质量渲染结果。我们相信，本方法是首个在微观尺度利用可微渲染、支持来自显式纤维集合的基于物理散射的方法。我们的面料参数估计在远景和特写视图中均实现了对所测机织面料样本的高质量重渲染。这些结果还可进一步用于高效渲染，或转换为下游表示。我们还提出了一种块空间纤维几何程序化生成方法和一个两尺度路径追踪框架，用于高效渲染面料场景。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
