Depth estimation from a single-shot fringe pattern based on DD-Inceptionv2-UNet

Abstract

The quick and accurate retrieval of an object’s depth from a single-shot fringe pattern in fringe projection profilometry has been a topic of ongoing research. In recent years, with the development of deep learning, a deep learning technique to FPP for single-shot 3D measurement is being used. To improve the accuracy of depth estimation from a single-shot fringe pattern, we propose the depthwise separable Dilation Inceptionv2-UNet (DD-Inceptionv2-UNet) by adjusting the depth and width of the network model simultaneously. And we evaluate the model on both simulated and experimental datasets. The experimental results show that the error between the depth map predicted by the proposed method and the label is smaller, and the depth curve map is closer to the ground truth. And on the simulated dataset, the MAE of the proposed method decreased by 35.22%, compared to UNet. On the experimental dataset, the MAE of the proposed method decreased by 34.62%, compared to UNet. The proposed method is relatively outstanding in both quantitative and qualitative evaluations, effectively improving the accuracy of 3D measurement results from a single-shot fringe pattern.