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Phase unwrapping is an important research direction in fringe projection profilometry. Improving the accuracy of phase unwrapping from a single wrapped phase map has been a research focus. Existing the deep learning mathods for phase unwrapping from a single wrapped phase map suffer from accuracy issues when dealing with noise, the large variation range of phase surfaces, or isolated area. In this paper, we propose a novel approach to address these challenges. We treat the phase unwrapping problem as a semantic segmentation problem and introduce a new stage to the high resolution network. Additionally, we add an object contextual representation module. This approach allows us to predict the fringe order map from a single wrapped phase map without the need for any pre-process or post-process. Our method can accurately recover the phase information of objects under various challenging conditions. We validate the effectiveness and superiority of our approach by comparing it with Three deep learning methods for spatial phase unwrapping and one traditional spatial phase unwrapping method, qualitatively and quantitatively.

C-HRNet: High Resolution Network Based on Contexts for Single-Frame Phase Unwrapping