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.

翻译：快速且高精度的三维形貌测量一直是条纹投影轮廓术（FPP）的追求目标。双频时间相位解包裹方法（DF-TPU）是实现该目标的突出技术之一。然而，现有DF-TPU方法中高频条纹的周期数通常受不可避免的相位误差限制，从而限制了测量精度。基于深度学习的单相机FPP相位解包裹方法通常需要标注数据进行训练。本文提出了一种适用于单相机FPP系统的自监督相位解包裹新方法。训练后的网络能从一幅64周期相位图中恢复出绝对条纹级次，并在深度精度上优于DF-TPU方法。实验结果验证了所提方法在运动模糊、孤立物体、低反射率及相位不连续等真实场景中的有效性。