Deep Photometric Stereo

A deep learning enabled photometric stereo method for end-to-end surface height and normal reconstruction.

Three-dimensional (3D) measurement provides essential geometric information for quality control and process monitoring in many manufacturing applications. Photometric stereo is one of the potential solutions for in-process metrology and active geometry compensation, which takes multiple images of an object under different illuminations as inputs and recovers its surface normal map based on a reflectance model.

Deep learning approaches have shown their potential in solving the highly nonlinear problem for photometric stereo, but the main challenge preventing their practical application in process metrology lies in the difficulties in the generation of a comprehensive dataset for training the deep learning model.

This project proposes a new Deep-learning based Point-light Photometric Stereo method, DPPS, which utilizes a multi-channel deep convolutional neural network (CNN) to achieve end-to-end prediction for both the surface normal and height maps in a semi-calibrated fashion (Yang et al., 2023). The key contribution is a new dataset generation method combining both physics-based and data-driven approaches, which minimizes the training cost and enables DPPS to handle reflective metal surfaces with unknown surface roughness.

Even trained only with fully synthetic and high-fidelity dataset, our DPPS surpasses the state-of-the-art and its real-life experimental results are on par with commercial 3D scanners. The demonstrated results provide guidance on improving the generalizability and robustness of deep-learning based computer vision metrology with minimized training cost as well as show the potential for in-process 3D metrology in advanced manufacturing processes.

Codes are released on GitHub.



  1. DPPS.jpg
    Ru Yang ,  Yaoke Wang ,  Shuheng Liao ,  and  Ping Guo
    Measurement, 2023