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

Mingfeng Chen; Yiming Li; Xinghui Li; Zinan Li; Weikang Chen; Chaobo Zhang; Xiaojun Liang

doi:10.1117/12.3035635

22 November 2024 An end-to-end structured light depth prediction approach using Mamba networks

Mingfeng Chen, Yiming Li, Xinghui Li, Zinan Li, Weikang Chen, Chaobo Zhang, Xiaojun Liang

Proceedings Volume 13239, Optoelectronic Imaging and Multimedia Technology XI; 132390Q (2024) https://doi.org/10.1117/12.3035635
Event: SPIE/COS Photonics Asia, 2024, Nantong, Jiangsu, China

Conference Poster

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingfeng Chen, Yiming Li, Xinghui Li, Zinan Li, Weikang Chen, Chaobo Zhang, and Xiaojun Liang "An end-to-end structured light depth prediction approach using Mamba networks", Proc. SPIE 13239, Optoelectronic Imaging and Multimedia Technology XI, 132390Q (22 November 2024); https://doi.org/10.1117/12.3035635

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