Photometric stereo three-dimensional (3D) reconstruction is a hot topic in the fields of machine vision and photometry. This method is widely used because of its simple equipment, low cost, and high resolution. With the rapid advancement of artificial intelligence and deep learning technology in recent years, the development of photometric stereo technology has entered a new era. This paper reviews the progress in the application of depth learning technology to photometric stereo 3D reconstruction. First, the research background and the basic principles of photometric 3D reconstruction are introduced. Next, various types of photometric stereo 3D reconstruction methods are summarized. Then, the commonly used synthetic and real-photoed datasets are briefly introduced. Further, a detailed description of the applications of depth learning technology in photometric stereo 3D reconstruction is provided, wherein the physical model-based photometric stereo technology is transformed into a “data-driven” technology to achieve high prediction accuracy. Finally, the paper analyzes and summarizes the challenges and opportunities for future research in the application of deep learning technology to photometric stereo reconstruction.