We propose a learning-based method to recover normals, specularity, and roughness from a single diffuse image of a material, using microgeometry appearance as our primary cue. Previous methods that work on single images tend to produce over-smooth outputs with artifacts, operate at limited resolution, or train one model per class with little room for generalization. Previous methods that work on single images tend to produce over-smooth outputs with artifacts, operate at limited resolution, or train one model per class with little room for generalization. In contrast, in this work, we propose a novel capture approach that leverages a generative network with attention and a U-Net discriminator, which shows outstanding performance integrating global information at reduced computational complexity. We showcase the performance of our method with a real dataset of digitized textile materials and show that a commodity flatbed scanner can produce the type of diffuse illumination required as input to our method. Additionally, because the problem might be illposed -more than a single diffuse image might be needed to disambiguate the specular reflection- or because the training dataset is not representative enough of the real distribution, we propose a novel framework to quantify the model's confidence about its prediction at test time. Our method is the first one to deal with the problem of modeling uncertainty in material digitization, increasing the trustworthiness of the process and enabling more intelligent strategies for dataset creation, as we demonstrate with an active learning experiment.

翻译：我们提出一种基于学习的方法，通过微几何外观作为主要线索，从单张漫反射材质图像中恢复法线、镜面反射度和粗糙度。此前基于单图像的工作往往产生含有伪影的过度平滑输出，分辨率受限，或为每个类别训练单一模型且泛化能力不足。相比之下，本文提出一种新颖的捕获方法，利用带有注意力机制的生成网络和U-Net判别器，在降低计算复杂度的同时展现出整合全局信息的卓越性能。我们使用数字化纺织物材质的真实数据集展示了该方法的性能，并证明普通平板扫描仪即可产生方法所需的漫反射光照输入。此外，由于问题可能存在不适定性——可能需要多张漫反射图像来消除镜面反射歧义——或训练数据集不足以代表真实分布，我们提出一种新颖框架，可在测试时量化模型对其预测的置信度。我们的方法是首个处理材质数字化中不确定性建模问题的方案，通过主动学习实验证明，该方法提升了流程的可信度，并实现了更智能的数据集创建策略。