Structured light 3D imaging based on Gray code requires accurate pixel light and dark judgment for high-quality reconstruction. This paper proposes a pixel point classification method for fringe images based on the U-Net network to achieve robust and efficient pixel binary classification. To make the network more effective in learning and utilizing the structural information of the fringes projected by the projector, a multi-channel network input is designed. To address the challenge of obtaining a large number of fringe images for network training in real scenes, this paper utilizes computer graphics to construct a virtual 3D structured light imaging system and produce usable datasets. The proposed method achieved a pixel classification error rate of 0.44% and a mean square error of 285.019 on the test data, outperforming the traditional method's 1.10% and 713.578, respectively. The proposed method can accurately recognize and classify pixel values of highly reflective regions in an image. And it can generate high dynamic range images with wider dynamic range and richer image details.
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