Structured light three-dimensional reconstruction method based on GFU-Net network

In streak projection profilometry, how to reconstruct the 3D shape of an object quickly and accurately from a single streak pattern has been an important research area. Although the CNN-based fringe-to-depth method can directly reconstruct 3D from a single fringe, its accuracy is currently not as good as the traditional phase-shift technique. In order to improve the accuracy of 3D reconstruction, this paper proposes a U-Net-based global feature fusion network (GFU-Net), which introduces a global feature fusion module to fuse the global context information, and uses a feature fusion upsampling module to recover the spatial detail information, which solves the problem that it is difficult to accurately obtain the depth information of an object from a single fringe map. The experimental results show that the method proposed in this paper decreases the RMSE by 17.8% compared to the U-Net network, and the 3D reconstruction accuracy is higher and the error is smaller, which verifies the effectiveness and robustness of the method.