利用极化信息在复杂介质中精准恢复表面法向，突破冰层等非线性干扰限制。
Structure-Aware Consistency Priors for Shape from Polarization in Complex Media
- 基于自相关函数构建结构感知极化先验，捕捉局部偏振一致性
- 提出双分支网络IceSfP，融合原始特征与先验，实现16.01°的法向误差
- 首个真实冰层极化三维重建数据集，适合高精度几何感知研究
在复杂介质中从单视角极化图像恢复表面法向仍具挑战。本文以冰为典型复杂介质，其复杂的光-物质相互作用导致极化观测与表面法向之间呈非线性映射。为此，提出一种基于自相关函数的结构感知极化先验，以捕捉局部角偏振度（AoLP）的空间一致性。在此基础上，设计双分支网络IceSfP，通过跨模态注意力与多尺度特征融合，将原始极化特征与先验有效结合，在复杂介质条件下实现精确的表面法向估计。为评估方法，构建了首个真实世界冰层极化三维重建数据集。实验结果表明，该方法在所有指标上均优于现有方法，平均绝对误差（MAE）达16.01°，比次优方法低2.74°。该框架为复杂介质中的高精度几何感知提供了通用解决方案。
原文摘要 · Abstract (English)
Recovering surface normals from single view polarization images in complex media remains challenging. This paper focuses on ice as a representative complex medium, where intricate light matter interactions lead to a nonlinear mapping between polarization observations and surface normals. To address this, a structure-aware polarization prior based on autocorrelation functions is proposed to capture the local spatial consistency of AoLP. Building on this, a dual-branch network (IceSfP) is designed to integrate raw polarization features with priors via cross modal attention and multi-scale feature fusion, enabling accurate surface normal estimation under complex media conditions. To evaluate the method, the first real-world ice SfP dataset is constructed. Experimental results show that the method outperforms existing approaches across all metrics, achieving a MAE of 16.01 deg, which is 2.74 deg lower than the second-best method. The framework provides a generalizable solution for high-precision geometric perception in complex media.
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