SEDDI DOME Dataset

Existing devices for measuring material appearance in spatially-varying samples are limited to a single scale, either micro or mesoscopic. This is a practical limitation when the material has a complex multi-scale structure. In this paper, we present a system and methods to digitize materials at two scales, designed to include high-resolution data in spatially-varying representations at larger scales. We design and build a hemispherical light dome able to digitize flat material samples up to 11x11cm. We estimate geometric properties, anisotropic reflectance and transmittance at the microscopic level using polarized directional lighting with a single orthogonal camera. Then, we propagate this structured information to the mesoscale, using a neural network trained with the data acquired by the device and image-to-image translation methods. To maximize the compatibility of our digitization, we leverage standard BSDF models commonly adopted in the industry. Through extensive experiments, we demonstrate the precision of our device and the quality of our digitization process using a set of challenging real-world material samples and validation scenes. Further, we demonstrate the optical resolution and potential of our device for acquiring more complex material representations by capturing microscopic attributes which affect the global appearance: we characterize the properties of textile materials such as the yarn twist or the shape of individual fly-out fibers. We also release the SEDDIDOME dataset of materials, including raw data captured by the machine and optimized parameteres.当前用于测量空间变化样本材质外观的设备，仅支持单一尺度（微观或介观）的采集。当材质具备复杂多尺度结构时，这种设计会带来实际应用局限。本文提出一套可实现双尺度材质数字化的系统与方法，旨在针对大尺度空间变化表征采集高分辨率数据。我们设计并搭建了一款半球形光照穹顶，可对尺寸最大为11×11厘米的平面材质样本进行数字化采集。借助单台正交相机与偏振定向光照系统，我们可在微观尺度下估算材质的几何特性、各向异性反射率与透射率。随后，我们利用该设备采集的数据与图像到图像转换方法训练神经网络，将上述结构化信息迁移至介观尺度。为最大化数字化流程的兼容性，我们采用工业界通用的标准双向散射分布函数（Bidirectional Scattering Distribution Function, BSDF）模型。通过大量实验，我们基于一系列极具挑战性的真实世界材质样本与验证场景，验证了本设备的精度与数字化流程的质量。此外，我们通过捕捉影响全局外观的微观属性，验证了本设备的光学分辨率与复杂材质表征采集潜力：我们对纺织材质的特性进行了表征，例如纱线捻度或单根游离纤维的形态。我们还公开了SEDDIDOME材质数据集，包含设备采集的原始数据与优化后的参数。