Monocular shape-from-polarization (SfP) leverages the intrinsic relationship between light polarization properties and surface geometry to recover surface normals from single-view polarized images, providing a compact and robust approach for three-dimensional (3D) reconstruction. Despite its potential, existing monocular SfP methods suffer from azimuth angle ambiguity, an inherent limitation of polarization analysis, that severely compromises reconstruction accuracy and stability. This paper introduces a novel segmentation-driven monocular SfP (SMSfP) framework that reformulates global shape recovery into a set of local reconstructions over adaptively segmented convex sub-regions. Specifically, a polarization-aided adaptive region growing (PARG) segmentation strategy is proposed to decompose the global convexity assumption into locally convex regions, effectively suppressing azimuth ambiguities and preserving surface continuity. Furthermore, a multi-scale fusion convexity prior (MFCP) constraint is developed to ensure local surface consistency and enhance the recovery of fine textural and structural details. Extensive experiments on both synthetic and real-world datasets validate the proposed approach, showing significant improvements in disambiguation accuracy and geometric fidelity compared with existing physics-based monocular SfP techniques.

翻译：单目偏振三维重建（SfP）利用光偏振特性与表面几何之间的内在关联，从单视角偏振图像中恢复表面法线，为三维重建提供了一种紧凑且鲁棒的方法。尽管具有潜力，现有单目SfP方法受限于偏振分析固有的方位角模糊性问题，严重影响了重建精度与稳定性。本文提出一种新颖的分割驱动单目SfP（SMSfP）框架，将全局形状恢复问题转化为在自适应分割的凸子区域上进行局部重建的集合。具体而言，我们提出一种偏振辅助自适应区域生长（PARG）分割策略，将全局凸性假设分解为局部凸区域，有效抑制方位角模糊性并保持表面连续性。此外，本文开发了多尺度融合凸性先验（MFCP）约束，以确保局部表面一致性并增强细微纹理与结构细节的恢复能力。在合成与真实数据集上的大量实验验证了所提方法的有效性，相较于现有基于物理的单目SfP技术，本方法在解歧精度与几何保真度方面均展现出显著提升。