cs.CVOct 6, 2026

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

Authors: Beibei Lin, Tingting Chen, Xin Zhang, Wenhao Zhao, Dongjun Li, Zifeng Yuan

Organizations: National University of Singapore · University of Michigan, Ann Arbor

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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 s0s_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.

Figures & tables

Figure 1: Overview of PolarScale . Prior RGB-based methods [ 22 , 21 ] predict normalized Stokes components or relative polarimetric descriptors, from which the radiometric scale has been divided out. PolarScale additionally predicts the per-scene radiometric scale σ\sigma and reconstructs the full Stokes components Sn=σ sn\mathrm{S}_{n}=\sigma\,\mathrm{s}_{n} , making the scale an explicit evaluation target. The input is the normalized total-intensity image s0=S0/σ\mathrm{s}_{0}=\mathrm{S}_{0}/\sigma (Section 3.2 ), not a consumer sRGB photograph.
Figure 2: Overview of the three prediction strategies evaluated in our benchmark. (a) Direct Stokes Estimation predicts σ\sigma and normalized Stokes components, then derives AoLP, DoLP, and DoCP. (b) Joint Estimation predicts all polarimetric parameters with a unified decoder. (c) Decoupled Multi-decoder separately estimates scale-dependent components and scale-independent descriptors. The input in all three strategies is the normalized total-intensity image s0\mathrm{s}_{0} .
Figure 3: Reconstructed full Stokes components for test scene 1043 (input: normalized s0\mathrm{s}_{0} ). The sky carries strong S2\mathrm{S}_{2} and S3\mathrm{S}_{3} signals: MAE [ 33 ] recovers their extent and sign, Uformer [ 32 ] only faint edges, and DiT [ 35 ] and I2ITurbo [ 37 ] predict nearly flat maps.
Appendix figures & tables15 assets

Supplementary material from the paper’s appendix.

Appendix

SymbolMeaning
S0,S1,S2,S3\mathrm{S}_{0},\mathrm{S}_{1},\mathrm{S}_{2},\mathrm{S}_{3}Full (absolute) Stokes components, trichromatic ( H×W×3H\times W\times 3 each)
σ\sigmaPer-scene radiometric scale, σ=max⁡S0\sigma=\max\mathrm{S}_{0}
σ^\hat{\sigma}Predicted per-scene scale
sk=Sk/σ\mathrm{s}_{k}=\mathrm{S}_{k}/\sigmaNormalized Stokes components, k∈{0,1,2,3}k\in\{0,1,2,3\}
s0\mathrm{s}_{0}Network input: normalized total-intensity image
s^n\hat{\mathrm{s}}_{n}Predicted normalized Stokes components, n∈{1,2,3}n\in\{1,2,3\}
Appendix
Table 6: Notation used in this paper.
Figure 4: Reconstructed full Stokes components for test scene 1050, an urban plaza with high-rise buildings in which the sky region carries a strong negative S2\mathrm{S}_{2} signal. MAE recovers S2\mathrm{S}_{2} and S3\mathrm{S}_{3} in this region but predicts the wrong sign for S1\mathrm{S}_{1} , while the other models predict nearly flat maps.
Figure 5: Reconstructed full Stokes components for test scene 1003.
Figure 6: Reconstructed full Stokes components for test scene 1035.
Figure 7: Reconstructed full Stokes components for test scene 1038.
Figure 8: Reconstructed full Stokes components for test scene 1062.
Figure 9: Reconstructed full Stokes components for test scene 1083.
Figure 10: Reconstructed full Stokes components for test scene 1126.
Figure 11: Predicted AoLP, DoLP, and DoCP for test scene 1158, a street intersection with crosswalk stripes, vehicles, and building facades. The models capture the global layout but miss abrupt polarization transitions; DiT produces noisy AoLP and I2ITurbo saturates DoCP, while MAE is closest to the ground truth.
Figure 12: Predicted AoLP, DoLP, and DoCP for test scene 1196, a high-rise facade whose repetitive window pattern produces fine, periodic polarization structure.
Figure 13: Predicted AoLP, DoLP, and DoCP for test scene 1036.
Figure 14: Predicted AoLP, DoLP, and DoCP for test scene 1040.
Figure 15: Predicted AoLP, DoLP, and DoCP for test scene 1047.
Figure 16: Predicted AoLP, DoLP, and DoCP for test scene 1052.
Figure 17: Predicted AoLP, DoLP, and DoCP for test scene 1194.

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