MATLAB Implementation for Photometric Stereo with Code Description

In this text, we reference "MATLAB code for photometric stereo." While this description is concise and clear, providing more detailed explanations could be beneficial for better understanding. In the field of computer vision, photometric stereo is a technique used to recover surface normals and reflectance properties of objects from multiple images captured under varying lighting conditions. This methodology is commonly implemented using MATLAB due to its robust capabilities in scientific computing and matrix operations. The algorithm typically involves solving a linear system of equations where pixel intensities are related to lighting directions through the reflectance model. To implement photometric stereo in MATLAB, key functions include: - Reading and preprocessing multiple input images (imread, rgb2gray) - Constructing the lighting direction matrix from known light source positions - Solving the linear system using matrix operations (mldivide or pinv for pseudo-inverse) - Computing surface normals through least-squares optimization - Reconstructing albedo maps and visualizing 3D surface orientations For deeper understanding of this technique, we can explore the working principles and implementation details of photometric stereo. By examining these technical aspects, users can better comprehend the MATLAB code structure and make necessary adjustments for specific applications. Therefore, this text incorporates background information and implementation specifics about photometric stereo to assist readers in mastering this computer vision technique, including discussions about different reflectance models and normalization techniques for improved accuracy.