  Fast-speed and high-accuracy three-dimensional (3D) shape measurement has
been the goal all along in fringe projection profilometry (FPP). The
dual-frequency temporal phase unwrapping method (DF-TPU) is one of the
prominent technologies to achieve this goal. However, the period number of the
high-frequency pattern of existing DF-TPU approaches is usually limited by the
inevitable 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-TPU approaches in terms of depth accuracy. Experimental
results demonstrate the validation of the proposed method on real scenes of
motion blur, isolated objects, low reflectivity, and phase discontinuity.
