Digitizing woven fabrics would be valuable for many applications, from digital humans to interior design. Previous work introduces a lightweight woven fabric acquisition approach by capturing a single reflection image and estimating the fabric parameters with a differentiable geometric and shading model. The renderings of the estimated fabric parameters can closely match the photo; however, the captured reflection image is insufficient to fully characterize the fabric sample reflectance. For instance, fabrics with different thicknesses might have similar reflection images but lead to significantly different transmission. We propose to recover the woven fabric parameters from two captured images: reflection and transmission. At the core of our method is a differentiable bidirectional scattering distribution function (BSDF) model, handling reflection and transmission, including single and multiple scattering. We propose a two-layer model, where the single scattering uses an SGGX phase function as in previous work, and multiple scattering uses a new azimuthally-invariant microflake definition, which we term ASGGX. This new fabric BSDF model closely matches real woven fabrics in both reflection and transmission. We use a simple setup for capturing reflection and transmission photos with a cell phone camera and two point lights, and estimate the fabric parameters via a lightweight network, together with a differentiable optimization. We also model the out-of-focus effects explicitly with a simple solution to match the thin-lens camera better. As a result, the renderings of the estimated parameters can agree with the input images on both reflection and transmission for the first time. The code for this paper is at https://github.com/lxtyin/FabricBTDF-Recovery.

翻译：机织面料的数字化对于从数字人到室内设计等众多应用具有重要价值。先前的研究提出了一种轻量级的机织面料采集方法，通过拍摄单张反射图像，并利用可微分的几何与着色模型估算面料参数。虽然估算参数的渲染结果能与照片高度吻合，但仅凭反射图像不足以完整表征面料样本的反射特性。例如，不同厚度的面料可能具有相似的反射图像，但其透射特性却存在显著差异。为此，我们提出通过拍摄反射与透射两张图像来重建机织面料参数。本方法的核心是一个可微分的双向散射分布函数（BSDF）模型，该模型同时处理反射与透射，包含单次散射与多次散射。我们提出了一个双层模型：单次散射沿用先前工作中的SGGX相位函数，而多次散射则采用一种新的方位角无关微面片定义，我们称之为ASGGX。这种新的面料BSDF模型在反射和透射方面均能高度匹配真实机织面料。我们使用简易装置（手机摄像头与两个点光源）采集反射与透射照片，并通过轻量级网络结合可微分优化来估算面料参数。此外，我们通过一种简洁方案显式建模离焦效应，以更好地匹配薄透镜相机模型。最终，本方法首次实现了估算参数的渲染结果在反射与透射两方面均与输入图像一致。本文代码位于 https://github.com/lxtyin/FabricBTDF-Recovery。