论文标题
MS-PS：具有新的综合培训数据集的光度计立体声网络
MS-PS: A Multi-Scale Network for Photometric Stereo With a New Comprehensive Training Dataset
论文作者
论文摘要
光度法立体声（PS）问题包括重建对象的3D表面，这要归功于在不同的照明方向下拍摄的一组照片。在本文中，我们提出了PS的多尺度体系结构，该体系结构与新数据集相结合，得出最先进的结果。我们提出的架构是灵活的：它允许考虑可变数量的图像以及可变图像大小而不会损失性能。此外，我们定义了一组约束，以允许生成相关的合成数据集训练PS问题的卷积神经网络。我们提出的数据集比预先存在的数据集大得多，并且包含许多具有各向异性反射率（例如金属，玻璃）的具有挑战性的材料。我们在公开可用的基准上表明，这两种贡献的组合与以前的最新方法相比，这两种贡献都大大提高了估计的正常领域的准确性。
The photometric stereo (PS) problem consists in reconstructing the 3D-surface of an object, thanks to a set of photographs taken under different lighting directions. In this paper, we propose a multi-scale architecture for PS which, combined with a new dataset, yields state-of-the-art results. Our proposed architecture is flexible: it permits to consider a variable number of images as well as variable image size without loss of performance. In addition, we define a set of constraints to allow the generation of a relevant synthetic dataset to train convolutional neural networks for the PS problem. Our proposed dataset is much larger than pre-existing ones, and contains many objects with challenging materials having anisotropic reflectance (e.g. metals, glass). We show on publicly available benchmarks that the combination of both these contributions drastically improves the accuracy of the estimated normal field, in comparison with previous state-of-the-art methods.
