Reliable 3-D Reconstruction With Single-Shot Digital Grating and Physical Model-Supervised Machine Learning

Precise and rapid three-dimensional (3D) measurement of metal parts on production lines is essential. Modern artificial intelligence (AI) supports sensing devices to see the world in 3D vision. Digital fringe 3D profilometry reconstruction (DF-3D) based on deep learning has the potential to revolutionize society. Nevertheless, attaining the high precision and robust DF-3D through a single-shot end-to-end (SS-E2E) AI network remains an unresolved challenge. For fast and precise 3D reconstruction, this work proposes an SS-E2E absolute phase prediction network strategy, composed of a multi-path branch auxiliary supervision network and guided by the fringe-phase physical model (MPS_XNet). Specifically, MPS_XNet is based on the three key stages of the phase retrieval model, including numerator, denominator, and wrapped phase supervision, as auxiliary branches to assist the main path of absolute phase for long-distance computation in interpretable features. Besides, we designed a physics-based supervisory loss function to correctly guide the network training along the path defined by the physical model. Experiments have demonstrated that MPS_XNet outperforms the state-of-the-art (SOTA) regression network paradigm on five datasets (metal workpieces and complex contours) and our measurement system, which not only compensates for insufficient input information but also robustly and interpretably guides the network training through converting the long-distance challenge to multiple short-distance sub-tasks. Our strategy achieves a groundbreaking 90% MAE reduction in phase retrieval through physical model design and task decomposition in 20 ms. It remains promising for future SOTA networks, enabling real-time, high-precision 3D measurement in industrial and scientific applications. The source code is publicly available at https://github.com/LiYiMingM/Physical-Model-Supervised-Machine-Learning-3D-reconstruction.