Deep learning-based single-shot fringe projection profilometry

Real-time fringe projection profilometry (FPP) is developed as a 3D vision system to plan and guide autonomous robotic intestinal suturing. Conventional FPP requires sinusoidal patterns with multiple frequencies, and phase shifts to generate tissue point clouds, resulting in a slow frame rate. Therefore, although FPP can reconstruct dense and accurate tissue point clouds, it is often too slow for dynamic measurements. To address this problem, we propose a deep learning-based single-shot FPP algorithm, which reconstructs tissue point clouds with a single sinusoidal pattern using a Swin-Unet. With this approach, we have achieved a FPP imaging frame rate of 50Hz while maintaining high point cloud measurement accuracy. System performance was trained and evaluated both by synthesized and an experimental dataset. An overall relative error of 1~3% was achieved.