Shi Boxin, Mo Zhipeng, Wu Zhe, Duan Dinglong, Yeung Sai-Kit, Tan Ping
IEEE Trans Pattern Anal Mach Intell. 2019 Feb;41(2):271-284. doi: 10.1109/TPAMI.2018.2799222. Epub 2018 Feb 5.
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.
经典光度立体视觉通常会进行扩展，以处理现实世界中的材料，并在未知光照条件下工作，以提高实用性。为了定量评估非朗伯体和未校准的光度立体视觉，仍然缺少一个包含具有复杂反射特性的各种形状物体以及高质量真实法线的光度立体图像数据集。在本文中，我们引入了“DiLiGenT”数据集，该数据集具有校准的定向照明、具有不同光泽度的一般反射物体，以及来自高精度激光扫描的“真实”法线。我们使用我们的数据集对从新提出的强调非朗伯体和未校准方法的光度立体视觉分类法中选择的用于一般材料和未知光照条件的最先进光度立体视觉方法进行定量评估。该数据集和评估结果已公开提供，我们希望它能作为一个基准平台，激发未来的研究。
