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.
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.
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.
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