Guo Heng, Mo Zhipeng, Shi Boxin, Lu Feng, Yeung Sai-Kit, Tan Ping, Matsushita Yasuyuki
IEEE Trans Pattern Anal Mach Intell. 2022 Nov;44(11):7809-7823. doi: 10.1109/TPAMI.2021.3115229. Epub 2022 Oct 4.
This paper presents a photometric stereo method that works with unknown natural illumination without any calibration objects or initial guess of the target shape. To solve this challenging problem, we propose the use of an equivalent directional lighting model for small surface patches consisting of slowly varying normals, and solve each patch up to an arbitrary orthogonal ambiguity. We further build the patch connections by extracting consistent surface normal pairs via spatial overlaps among patches and intensity profiles. Guided by these connections, the local ambiguities are unified to a global orthogonal one through Markov Random Field optimization and rotation averaging. After applying the integrability constraint, our solution contains only a binary ambiguity, which could be easily removed. Experiments using both synthetic and real-world datasets show our method provides even comparable results to calibrated methods.
本文提出了一种光度立体视觉方法，该方法可在无任何校准物体或目标形状初始猜测的情况下，适用于未知的自然光照环境。为解决这一具有挑战性的问题，我们建议对由缓慢变化的法线组成的小表面面片使用等效方向光照模型，并求解每个面片直至任意正交模糊度。我们进一步通过面片间的空间重叠和强度轮廓提取一致的表面法线对来建立面片连接。在这些连接的引导下，通过马尔可夫随机场优化和旋转平均将局部模糊度统一为全局正交模糊度。应用可积性约束后，我们的解决方案仅包含一个二元模糊度，可轻松消除。使用合成数据集和真实世界数据集进行的实验表明，我们的方法甚至能提供与校准方法相当的结果。
