Abstract

Recent advancements in artificial intelligence have transformed three-dimensional (3D) optical imaging and metrology, enabling high-resolution and high-precision 3D surface geometry measurements from one single fringe pattern projection. However, the imaging speed of conventional fringe projection profilometry (FPP) 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 dual-frequency angular-multiplexed fringe projection profilometry (DFAMFPP), a deep learning-enabled 3D imaging technique that achieves high-speed, high-precision, and large-depth-range absolute 3D surface measurements at speeds 16 times faster than the sensor's native frame rate. By encoding multi-timeframe 3D information into a single multiplexed image using multiple pairs of dual-frequency fringes, high-accuracy absolute phase maps are reconstructed using specially trained two-stage number-theoretical-based deep neural networks.

We validate the effectiveness of DFAMFPP through dynamic scene measurements, achieving 10,000 Hz 3D imaging of a running turbofan engine prototype with only a 625 Hz camera. By overcoming the sensor hardware bottleneck, DFAMFPP significantly advances high-speed and ultra-high-speed 3D imaging, opening new avenues for exploring dynamic processes across diverse scientific disciplines.