PolarScale: A Physics-Grounded Benchmark for Radiometrically Consistent RGB-to-Stokes Estimation

Polarization imaging provides rich physical cues beyond RGB imaging, yet acquiring such information typically requires specialized polarization-sensitive hardware that increases system cost and deployment complexity. Recent methods have proposed to infer polarization information directly from standard RGB images, providing a hardware-free alternative. While useful for describing relative polarimetric structure, these representations do not fully assess whether estimated images preserve the radiometric scale and intensity-dependent physical information needed for full Stokes reconstruction. To address this limitation, we introduce PolarScale, a physics-grounded benchmark for radiometrically consistent RGB-to-Stokes estimation. Instead of treating polarization inference from RGB images as conventional image-to-image translation, PolarScale evaluates how scale-independent descriptors, scale-dependent Stokes components, and radiometric scale jointly support physically meaningful Stokes reconstruction. Building upon this formulation, we propose three prediction strategies, namely direct, joint, and decoupled prediction, to study how scale-independent and scale-dependent polarimetric cues should be modeled across representative restoration-based and generative frameworks. Our analysis reveals that restoration-based frameworks outperform generative ones for radiometrically consistent polarization estimation (e.g., 28.61 vs. 26.14 dB PSNR). Furthermore, we find that jointly modeling scale-independent and scale-dependent parameters yields more stable physical representations than direct scale-dependent prediction (e.g., 28.61 vs. 26.81 dB PSNR). Overall, PolarScale provides a physics-grounded benchmark foundation for evaluating whether vision models can recover radiometrically consistent polarimetric signals from standard RGB images.