Uncalibrated and Unsupervised Photometric Stereo with Piecewise Regularizer

Photometric stereo is a technique for recovering a rigid object’s 3D shape, reflectance properties, lighting conditions, and specular highlights from multiple images captured under varying lighting conditions. Variational, uncalibrated, and unsupervised formulations have recently provided detailed and robust solutions to the problem, reducing the need for prior knowledge about shape geometry or lighting conditions. However, uncalibrated methods, especially when applied to real-world data, may be susceptible to noise and depth errors near boundaries or self-occlusions, stemming from missing or noisy data, surface orientation ambiguity, and calibration issues. In this paper, we introduce a novel piecewise depth regularizer to mitigate these errors, enhancing stability and improving robustness against initialization errors. We demonstrate the effectiveness of our approach through evaluations on both synthetic and real-world data, showcasing its promise in enhancing the accuracy and reliability of photometric stereo for practical applications.