偏振图像仿真技术的演进与展望

doi:10.16182/j.issn1004731x.joss.25-1221

摘要/Abstract

摘要：

偏振图像仿真技术是突破偏振数据获取瓶颈、推动偏振视觉发展的关键手段。系统论述了该技术的3种演进范式：物理机理仿真基于偏振双向反射分布函数与偏振光线追踪严格求解偏振光传输，具有高可解释性与可信度，但计算复杂，视觉真实感不足；数据驱动仿真借助神经辐射场等模型从数据中学习偏振外观，生成效率高、视觉逼真，但物理一致性与可解释性较弱；物理-数据融合仿真则通过可微分渲染将物理约束嵌入数据驱动模型，兼顾物理正确性与视觉真实性。总结了3种仿真范式的代表性工作，对比分析了不同范式的仿真性能及适用场景，梳理了相关数据集与仿真评价指标，并探讨了3种范式当前面临的挑战与未来发展方向。

关键词: 偏振成像, 物理建模, 数据驱动, 物理-数据融合仿真

Abstract:

Polarization image simulation technology is a key means to break through the bottleneck of polarization data acquisition and promote the development of polarization vision. This study systematically reviewed three evolutionary paradigms of this technology: Physical mechanism simulation, based on the polarization bidirectional reflectance distribution function and polarization ray tracing, strictly solves polarization light transmission, which has high interpretability and credibility, but it is computationally complex and lacks visual realism. Data-driven simulation, using models like neural radiance fields to learn polarization appearance from data, has high generation efficiency and visual fidelity but weaker physical consistency and interpretability. Physics-data fusion simulation embeds physical constraints into data-driven models through differentiable rendering, balancing physical correctness and visual realism. This study summarized representative works of the three simulation paradigms, compared and analyzed the simulation performance and applicable scenarios of different paradigms, reviewed related datasets and simulation evaluation metrics, and discussed the current challenges and future development directions of the three paradigms.

Key words: polarization imaging, physical modeling, data drive, physics-data fusion simulation

中图分类号:

TP391.9

李庚鹏,蔡伟,杨志勇等 . 偏振图像仿真技术的演进与展望[J]. 系统仿真学报, 2026, 38(7): 1783-1800.

Li Gengpeng,Cai Wei,Yang Zhiyong,et al . Evolution and Prospects of Polarization Image Simulation Technology[J]. Journal of System Simulation, 2026, 38(7): 1783-1800.

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主要步骤	意义	难点
三维建模	为偏振光线追踪提供几何基础；支持复杂表面法向量计算	真实世界的复杂性可能导致模型过于复杂，也存在模型过于简化导致逼真度不足
↓
偏振仿真计算	通过求解偏振特性方程，将三维场景的几何属性与物理属性转化为偏振量	光-物作用偏振建模复杂；准确的材质偏振参数获取困难
↓
偏振光线追踪	通过光线追踪模拟偏振光在复杂环境中的多次反射过程，生成中间偏振图像数据	在不牺牲结果准确性前提下提高计算速度和效率
↓
偏振可视化	利用偏振渲染方程得到不同像素处的斯托克斯矢量图像及偏振度图像、偏振角图像等	如何有效呈现大量偏振图像数据，同时保持结果的清晰度和可解释性

数据集	偏振态	波段数	场景数量	场景多样性
SfP-Wild Dataset ^[69]	LP	1	522	户外场景
UCLA DeepSfP^[70]	LP	1	300	室内物体
IRIMAS^[71]	LP	6	10	室内物体
KAUST^[72]	LP	3	40	室内物体
PANDORA^[61]	LP	3	3/2	室内多视角/合成多视角
Polarimetric Pose Dataset ^[73]	LP	3	6	室内多视角
PolarRR^[74]	LP	3	807	高反光物体
MCubeS^[75]	LP	3	500	户外场景
Synthetic Dataset^[76]	LP	3	44 300	合成场景
Unpol-Pol Image Pairs Dataset^[77]	LP	3	3 200	高反光物体
RGBP-Glass Dataset ^[78]	LP	3	4 500	透明物体
Sparse Polarization Dataset^[79]	LP	3	2 000	室内/户外场景
FSPMI^[80]	LP, CP	18	67	平面物体
NeSpoF^[60]	LP, CP	21/21	4	合成多视角/室内户外视角
Spectro-polarimetric Real-world Dataset ^[81]	LP, CP	3/21	2 022/311	室内/户外视角