Single‐Shot 3D Measurement of Reflective Surface With a Deep Learning‐Based Fringe Projection Profilometry Technique

The structured light projection profilometry technique is widely applied in various fields. However, for smooth surfaces, the technology faces challenges caused by specular reflection during application. These reflections introduce distortions into the modulation information of fringe patterns, causing phase errors in the subsequent image processing and impeding the accurate demodulation of corresponding 3D information. To address this issue, we propose a single‐shot 3D measurement algorithm for reflective surfaces based on deep learning. The algorithm takes a single reflective fringe image as input and produces an optimized, high‐quality fringe image without reflection interferences, along with generating the corresponding depth map. Experiments demonstrate the algorithm’s efficacy in achieving reflective fringe image optimization with minimal phase accuracy loss (0.0382 radians) and high accuracy in single‐shot 3D measurement with a mean absolute error (MAE) of ~0.48 mm. Notably, the algorithm requires only a single reflective image as input without additional hardware assistance, making it well‐suited for dynamic measurement applications. The processing time of the algorithm is ~0.12 s per image. Furthermore, the algorithm can serve as a preprocessing tool for reflective fringe patterns, seamlessly integrating with various 3D measurement algorithms, demonstrating strong flexibility and transferability.