An end-to-end structured light depth prediction approach using Mamba networks

Single-shot structured light depth prediction based on deep learning has wide application demands in intelligent manufacturing and defect detection. In the depth prediction area, existing CNN has poor long-range global modeling capabilities, and Transformer architecture requires large computational resources. For the challenge, we first propose and adopt DPM-UNet (Depth Prediction Mamba UNet), an end-to-end structured light depth prediction model from singleshot grating integrating Mamba and U-Net architectures, which fully leverages the long-range capturing capability and low computational performance of the State Space Model, as well as the pixel-by-pixel reconstruction capability of the UNet. Compared with pure CNN and Transformer architecture, DPM-UNet is able to achieve more accurate prediction with a 26.4% error reduction on public datasets. Experiments showed that DPM-UNet is effective in improving the accuracy and robustness of depth maps, and demonstrates remarkable potential in structured light depth prediction tasks.