UD-SfPNet: An Underwater Descattering Shape-from-Polarization Network for 3D Normal Reconstruction

• URL: http://arxiv.org/abs/2603.00908v1
• Date: Sun, 01 Mar 2026 04:10:36 GMT
• Title: UD-SfPNet: An Underwater Descattering Shape-from-Polarization Network for 3D Normal Reconstruction
• Authors: Puyun Wang, Kaimin Yu, Huayang He, Feng Huang, Xianyu Wu, Yating Chen,
• Abstract summary: polarization imaging offers the unique dual advantages of descattering and shape-from-polarization (SfP) 3D reconstruction.<n>This paper proposes UD-SfPNet, an underwater descattering shape-from-polarization network that leverages polarization cues for improved 3D surface normal prediction.
• Score: 3.2610672252390724
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Underwater optical imaging is severely hindered by scattering, but polarization imaging offers the unique dual advantages of descattering and shape-from-polarization (SfP) 3D reconstruction. To exploit these advantages, this paper proposes UD-SfPNet, an underwater descattering shape-from-polarization network that leverages polarization cues for improved 3D surface normal prediction. The framework jointly models polarization-based image descattering and SfP normal estimation in a unified pipeline, avoiding error accumulation from sequential processing and enabling global optimization across both tasks. UD-SfPNet further incorporates a novel color embedding module to enhance geometric consistency by exploiting the relationship between color encodings and surface orientation. A detail enhancement convolution module is also included to better preserve high-frequency geometric details that are lost under scattering. Experiments on the MuS-Polar3D dataset show that the proposed method significantly improves reconstruction accuracy, achieving a mean surface normal angular error of 15.12$^\circ$ (the lowest among compared methods). These results confirm the efficacy of combining descattering with polarization-based shape inference, and highlight the practical significance and potential applications of UD-SfPNet for optical 3D imaging in challenging underwater environments. The code is available at https://github.com/WangPuyun/UD-SfPNet.

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