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.

翻译：水下光学成像受散射效应严重制约，而偏振成像技术兼具去散射与偏振三维重建（SfP）的双重优势。为充分利用这些优势，本文提出UD-SfPNet——一种基于偏振线索提升三维表面法向预测精度的水下偏振去散射网络。该框架将基于偏振的图像去散射与SfP法向估计任务整合至统一流程中，避免了串行处理导致的误差累积，实现了跨任务的全局优化。UD-SfPNet进一步引入创新的色彩嵌入模块，通过挖掘色彩编码与表面朝向的关联性以增强几何一致性。同时，网络包含细节增强卷积模块，可有效恢复散射环境下损失的高频几何细节。在MuS-Polar3D数据集上的实验表明，所提方法显著提升了重建精度，取得了15.12$^\circ$的平均表面法向角误差（对比方法中最低）。这些结果验证了将去散射与偏振形状推断相结合的有效性，彰显了UD-SfPNet在复杂水下环境中进行光学三维成像的实际意义与应用潜力。代码已发布于https://github.com/WangPuyun/UD-SfPNet。