Motion-Induced Phase Error Compensation Using Three-Stream Neural Networks

Abstract

Phase-shifting profilometry (PSP) has been widely used in the measurement of dynamic scenes. However, the object motion will cause a periodical motion-induced error in the phase map, and there is still a challenge to eliminate it. In this paper, we propose a method based on three-stream neural networks to reduce the motion-induced error, while a general dataset establishment method for dynamic scenes is presented to complete three-dimensional (3D) shape measurement in a virtual fringe projection system. The numerous automatically generated data with various motion types is employed to optimize models. Three-step phase-shift fringe patterns captured along a time axis are divided into three groups and processed by trained three-stream neural networks to produce an accurate phase map. The actual experiment’s results demonstrate that the proposed method can significantly perform motion-induced error compensation and achieve about 90% improvement compared with the traditional three-step phase-shifting algorithm. Benefiting from the robust learning-based technique and convenient digital simulation, our method does not require empirical parameters or complex data collection, which are promising for high-speed 3D measurement.

Keywords

• fringe projection profilometry
• motion-induced error
• deep learning