Stokes-Informed Diffusion for Robust Linear Polarization Estimation

Polarization cues benefit applications such as material de-tection and de-reflection, yet acquiring them typically requires dedicatedhardware. This motivates us to estimate the linear polarization from asingle RGB image. However, the task is inherently ill-posed, with theAngle of Polarization (AoP) becoming particularly unstable in weak-polarization regions, where the polarimetric signal is overwhelmed bynoise, leading to erratic angle estimates. To address these limitations, wepropose GenPolar, a Stokes-informed diffusion framework grounded inthe Mueller formalism from an intensity observation. Specifically, Gen-Polar predicts channel-wise linear Stokes components (S1 , S2 ) from inten-sity S0 , from which degree of linear polarization (DoLP) and AoP are an-alytically derived; AoP is further supervised with an observability-awareloss. In addition, to enable efficient and high-fidelity inference, we adopta two-stage training strategy. Firstly, a multi-step conditional diffusionmodel is trained with a physics-based loss. Subsequently, we distill it intoa one-step generator, which further supports stable Low-Rank Adapta-tion (LoRA) of the VAE encoder to mitigate domain-specific autoen-coding bias. Extensive experiments across rotating-polarizer, division-of-focal-plane, and hybrid datasets demonstrate that GenPolar achievesstate-of-the-art performance in both DoLP fidelity and AoP stability.Crucially, these improvements translate to significant and consistent gainsin downstream applications, including material detection and de-reflection.