Self-supervised phase unwrapping in fringe projection profilometry

Abstract

Fast-speed and high-accuracy three-dimensional (3D) shape measurement has been the goal all along in fringe projection profilometry (FPP). The dual-frequency phase shifting (DF-PS) is one of the prominent technologies to achieve this goal. However, the period number of the high-frequency pattern of existing DF-PS approaches is usually limited by the amplified phase errors, setting a limit to measurement accuracy. Deep-learning-based phase unwrapping methods for single-camera FPP usually require labeled data for training. In this letter, a novel self-supervised phase unwrapping method for single-camera FPP systems is proposed. The trained network can retrieve the absolute fringe order from one phase map of 64-period and overperform DF-PS approaches in terms of depth accuracy. Experiments on real scenes demonstrate that the proposed method can unwrap the phase map of motion blur, isolated objects, low reflectivity, and phase discontinuity.