发表机构
Zhejiang University; School of Engineering, Westlake University; School of Automation, Central South University; College of Computer Science and Technology, Zhejiang University of Technology; School of Computer Science, Peking University（浙江大学; 西湖大学工学院; 中南大学自动化学院; 浙江工业大学计算机科学与技术学院; 北京大学计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究从单张RGB图像估计线性偏振问题，提出基于穆勒形式主义的GenPolar框架，通过预测斯托克斯分量并结合可观测性感知损失监督偏振角，采用两阶段训练策略，在多数据集实验中性能达先进水平，提升下游应用效果。
AI 中文摘要
偏振线索对材料检测和去反射等应用有益，但获取它们通常需要专用硬件。因此我们旨在从单张RGB图像估计线性偏振。然而该任务本质上不适定，在弱偏振区域偏振角不稳定。为此我们提出GenPolar，一个基于强度观测的穆勒形式主义的斯托克斯信息扩散框架。它从强度S0预测通道线性斯托克斯分量，通过可观测性感知损失监督偏振角。采用两阶段训练策略，实验表明GenPolar在偏振度保真度和偏振角稳定性上达到先进性能，在下游应用中也有显著提升。
英文摘要
Polarization cues benefit applications such as material detection and de-reflection, yet acquiring them typically requires dedicated hardware. This motivates us to estimate the linear polarization from a single RGB image. However, the task is inherently ill-posed, with the Angle of Polarization (AoP) becoming particularly unstable in weak polarization regions, where the polarimetric signal is overwhelmed by noise, leading to erratic angle estimates. To address these limitations, we propose GenPolar, a Stokes-informed diffusion framework grounded in the Mueller formalism from an intensity observation. Specifically, GenPolar predicts channel-wise linear Stokes components (S1,S2) from intensity S0, from which degree of linear polarization (DoLP) and AoP are analytically derived; AoP is further supervised with an observability-aware loss. In addition, to enable efficient and high-fidelity inference, we adopt a two-stage training strategy. Firstly, a multi-step conditional diffusion model is trained with a physics-based loss. Subsequently, we distill it into a one-step generator, which further supports stable Low-Rank Adaptation (LoRA) of the VAE encoder to mitigate domain-specific autoencoding bias. Extensive experiments across rotating-polarizer, division-of-focal-plane, and hybrid datasets demonstrate that GenPolar achieves state-of-the-art performance in both DoLP fidelity and AoP stability. Crucially, these improvements translate to significant and consistent gains in downstream applications, including material detection and de-reflection.
