通过分段重构解决单目偏振三维重建的方位角歧义问题
Segmentation-Driven Monocular Shape from Polarization based on Physical Model
- 将全局重建分解为自适应分割的局部凸区域，逐块恢复表面法向
- 在合成与真实数据集上，重建精度和几何保真度显著优于现有方法
- 适合需要高精度单视角3D重建的应用场景，如机器人视觉
单目偏振三维重建（SfP）利用光偏振特性与表面几何的内在关系，从单张偏振图像中恢复表面法向，是一种紧凑且鲁棒的三维重建方法。然而，现有单目SfP方法受偏振分析固有的方位角歧义影响，严重降低重建准确性和稳定性。本文提出一种新的分段驱动单目偏振三维重建框架（SMSfP），将全局形状恢复转化为对自适应分割的凸子区域进行局部重建。具体地，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，有效抑制方位角歧义并保持表面连续性。此外，设计多尺度融合凸性先验（MFCP）约束，确保局部表面一致性，提升细纹理与结构细节的恢复能力。在合成与真实世界数据集上的大量实验验证了该方法的有效性，相比现有基于物理模型的单目SfP技术，在去歧义准确率和几何保真度方面均有显著提升。
原文摘要 · Abstract (English)
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.
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