论文标题
对真实材料的深度SVBRDF估计
Deep SVBRDF Estimation on Real Materials
论文作者
论文摘要
最近的工作表明，可以成功地使用深度学习方法来恢复对表面上空间变化的BRDF（SVBRDF）的准确估计，从单个图像却很少。然而，仔细检查表明，文献中的大多数方法纯粹是基于合成数据的，虽然多样而现实，但通常不代表现实世界的丰富性。在本文中，我们表明，在对真实数据进行测试时，专门培训有关合成数据的网络不足以获得足够的结果。我们的分析利用了一个新的使用新型便携式多光捕获设备获得的真实材料的新数据集。通过一系列广泛的实验以及使用新颖的深度学习体系结构，我们探索了两种改善实际数据结果的策略：鉴定和每材料优化程序。我们表明，将网络权重适应实际数据至关重要，从而产生了一种方法，该方法极大地超过了先前对真实材料的SVBRDF估计的方法。数据集和代码可在https://lvsn.github.io/real-svbrdf上找到
Recent work has demonstrated that deep learning approaches can successfully be used to recover accurate estimates of the spatially-varying BRDF (SVBRDF) of a surface from as little as a single image. Closer inspection reveals, however, that most approaches in the literature are trained purely on synthetic data, which, while diverse and realistic, is often not representative of the richness of the real world. In this paper, we show that training such networks exclusively on synthetic data is insufficient to achieve adequate results when tested on real data. Our analysis leverages a new dataset of real materials obtained with a novel portable multi-light capture apparatus. Through an extensive series of experiments and with the use of a novel deep learning architecture, we explore two strategies for improving results on real data: finetuning, and a per-material optimization procedure. We show that adapting network weights to real data is of critical importance, resulting in an approach which significantly outperforms previous methods for SVBRDF estimation on real materials. Dataset and code are available at https://lvsn.github.io/real-svbrdf
