Depth acquisition from dual-frequency fringes based on end-to-end learning

The end-to-end networks have been successfully applied in fringe projection profilometry in recent years for their high flexibility and fast speed. Most of them can predict the depth map from a single fringe. But the depth map inherits the fringe fluctuation and loses the local details of the measured object. To address this issue, an end-to-end network based on double spatially frequency fringes (dual-frequency based depth acquisition network) is proposed. To release the periodic error of the predicted depth map, a dual-branch structure is designed to learn the global contour and local details of the measured object from dual-frequency patterns. To fully exploit the contextual information of the fringe patterns, five novel modules are proposed to accomplish feature extraction, down-sampling/up-sampling, and information feeding. Ablation experiments verify the effectiveness of the presented modules. Competitive experiments demonstrate that the proposed lightweight network presents higher accuracy compared to the existing end-to-end learning algorithms. Noise immunity test and physical validation demonstrate the generalization of the network.