Classic photometric stereo is often extended to deal with real-world materials and work with unknown lighting conditions for practicability. To quantitatively evaluate non-Lambertian and uncalibrated photometric stereo, a photometric stereo image dataset containing objects of various shapes with complex reflectance properties and high-quality ground truth normals is still missing. In this paper, we introduce the `DiLiGenT' dataset with calibrated Directional Lightings, objects of General reflectance with different shininess, and `ground Truth' normals from high-precision laser scanning. We use our dataset to quantitatively evaluate state-of-the-art photometric stereo methods for general materials and unknown lighting conditions, selected from a newly proposed photometric stereo taxonomy emphasizing non-Lambertian and uncalibrated methods. The dataset and evaluation results are made publicly available, and we hope it can serve as a benchmark platform that inspires future research.

中文翻译：

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非朗伯和未校准光度立体的基准数据集和评估


为了实用性，经典的光度立体通常被扩展以处理现实世界的材料并在未知的照明条件下工作。为了定量评估非朗伯和未校准的光度立体图像，仍然缺少包含具有复杂反射特性和高质量地面实况法线的各种形状物体的光度立体图像数据集。在本文中，我们介绍了带有校准定向照明的“DiLiGenT”数据集、具有不同光泽度的一般反射率的物体以及来自高精度激光扫描的“ground Truth”法线。我们使用我们的数据集定量评估一般材料和未知照明条件的最先进的光度立体方法，这些方法选自新提出的强调非朗伯和未校准方法的光度立体分类法。数据集和评估结果是公开的，我们希望它能够作为基准平台，启发未来的研究。