Abstract
Polarization imaging provides physical cues beyond intensity imaging but typically requires specialized hardware. Recent methods infer polarization from RGB-like inputs, yet predict only normalized Stokes components or relative descriptors, from which the radiometric scale needed for full Stokes reconstruction has been divided out. We introduce PolarScale, a benchmark that makes this scale an explicit prediction and evaluation target. Built on existing trichromatic full-Stokes measurements, PolarScale takes the per-scene normalized total-intensity image $s_0$ (a scene-referred linear image, not a consumer sRGB photograph) and asks models to predict normalized Stokes components, AoLP/DoLP/DoCP, and a per-scene scale. Because the scale is divided out of the input, it is not physically identifiable; PolarScale therefore evaluates dataset-conditioned semantic scale estimation against a constant-scale control, together with angular, self-consistency, and physical-bound metrics. Across seven restoration-based and generative backbones and three prediction strategies, the strongest restoration models estimate the scale with 3.6-4.3% mean relative error versus 5.7% for the constant control and violate physical bounds on fewer than 0.25% of pixels, whereas two generative baselines collapse to a near-zero scale; explicit descriptor supervision improves descriptor accuracy (23.66 vs. 18.88 dB PSNR for MAE). Predicted full-Stokes representations improve diffuse/specular separation, material segmentation, and glare classification, although in diffuse/specular separation the learned scale performs only on par with the constant control.
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Cite this article
APA 7
Lin, B., Chen, T., Zhang, X., Zhao, W., Li, D., & Yuan, Z. (2026). PolarScale: A Physics-Grounded Benchmark for Radiometrically Consistent RGB-to-Stokes Estimation. https://omanscience.com/en/articles/polarscale-a-physics-grounded-benchmark-for-radiometrically-consistent-rgb-to-stokes-estimation
MLA 9
Lin, Beibei, et al. "PolarScale: A Physics-Grounded Benchmark for Radiometrically Consistent RGB-to-Stokes Estimation." https://omanscience.com/en/articles/polarscale-a-physics-grounded-benchmark-for-radiometrically-consistent-rgb-to-stokes-estimation.
Chicago (author–date)
Lin, Beibei, Tingting Chen, Xin Zhang, Wenhao Zhao, Dongjun Li, and Zifeng Yuan. 2026. "PolarScale: A Physics-Grounded Benchmark for Radiometrically Consistent RGB-to-Stokes Estimation." https://omanscience.com/en/articles/polarscale-a-physics-grounded-benchmark-for-radiometrically-consistent-rgb-to-stokes-estimation.
Harvard
Lin, B., Chen, T., Zhang, X., Zhao, W., Li, D. and Yuan, Z. (2026) 'PolarScale: A Physics-Grounded Benchmark for Radiometrically Consistent RGB-to-Stokes Estimation', Available at: https://omanscience.com/en/articles/polarscale-a-physics-grounded-benchmark-for-radiometrically-consistent-rgb-to-stokes-estimation.
Vancouver
Lin B, Chen T, Zhang X, Zhao W, Li D, Yuan Z. PolarScale: A Physics-Grounded Benchmark for Radiometrically Consistent RGB-to-Stokes Estimation. https://omanscience.com/en/articles/polarscale-a-physics-grounded-benchmark-for-radiometrically-consistent-rgb-to-stokes-estimation
IEEE
B. Lin, T. Chen, X. Zhang, W. Zhao, D. Li, and Z. Yuan, "PolarScale: A Physics-Grounded Benchmark for Radiometrically Consistent RGB-to-Stokes Estimation," https://omanscience.com/en/articles/polarscale-a-physics-grounded-benchmark-for-radiometrically-consistent-rgb-to-stokes-estimation.