Temporally super-resolved multiplexed fringe projection structured light 3D imaging empowered by deep learning

Recent advancements in artificial intelligence have revolutionized 3D optical imaging and metrology, enabling high-speed, high-precision surface measurements. However, the imaging speed of conventional fringe projection profilometry remains limited by the native sensor refresh rates due to the inherent "one-to-one" synchronization mechanism between pattern projection and image acquisition in standard structured light techniques. Here, we present Multiplexed Fringe Projection Profilometry (MFPP), a deep learning-enabled 3D imaging technique that achieves 3D imaging at speeds over 10 times faster than the sensor's native frame rate. By encoding multi-timeframe 3D information into a single multiplexed image, high-accuracy phase maps are reconstructed via a two-stage neural network. Demonstrated with dynamic scenes, MFPP achieves 10,000Hz 3D imaging using a 625Hz camera, enabling new applications in studying high-speed dynamic processes.