Deep Learning-Driven One-Shot Dual-View 3-D Reconstruction for Dual-Projector System

Fringe projection profilometry (FPP) is an extensively used active 3-D measurement technique. However, it faces challenges in achieving synchronous improvement of measurement range, speed, accuracy, and shadow issues. To meet the demand for rapid 3-D reconstruction of two projection views only using a single-shot phase-shifting grating, we initially propose a fast and large-scale 3-D reconstruction system based on deep learning with a dual-projector and single-camera configuration, named DL_DPSL. The key feature is that the entire measurement process only requires two projectors to project one image simultaneously. To validate the effect, we constructed a simulated and a real system and corresponding simulation (~3300 sets) and real dataset (~1000 sets), respectively. The experimental results show that based ResUNet the DL_DPSL system can recover the phases of two projection views from one-shot superimposed phase-shifting grating within 0.018 s inclusive projection time when using two NVIDIA GeForce RTX 3090 graphics cards. Additionally, despite reducing the projection time by half, in the simulation dataset compared to the sequential projection, the mean absolute error was reduced by a maximum of 20%. And similar performance improvements in the continuous area of the measured objects in the real dataset. The DL_DPSL system has better accuracy and stability compared to the current fastest deep learning system. DL_DPSL breaks the existing dual-projector structured light 3-D measurement system paradigm, providing a new solution to promote the development and industrial application of FPP technology, with broad application prospects. (Simulation and real datasets are publicly available on the following GitHub repository:
https://github.com/LiYiMingM/DPSL3D-measurement
.)