发表机构
National University of Singapore; University of Michigan, Ann Arbor（新加坡国立大学; 密歇根大学安娜堡分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PolarScale提出物理基准，将RGB到Stokes估计中的辐射尺度作为显式预测目标，通过多模型评估表明恢复模型优于生成模型，并提升下游视觉任务性能。
AI 中文摘要
偏振成像提供了超越强度成像的物理线索，但通常需要专用硬件。近期方法从RGB类输入推断偏振信息，却仅预测归一化的Stokes分量或相对描述符，而完整Stokes重建所需的辐射度量已被从中消去。我们提出PolarScale基准，将该尺度作为明确的预测与评估目标。基于现有的三色全Stokes测量，PolarScale采用每场景归一化总强度图像$s_0$（场景参考线性图像，而非消费级sRGB照片），要求模型预测归一化Stokes分量、AoLP/DoLP/DoCP以及每场景尺度。由于尺度已从输入中消去，其在物理上不可辨识；因此PolarScale将数据集条件下的语义尺度估计与恒定尺度对照进行比较，并辅以角度、自一致性和物理边界指标。在七个基于恢复和生成的骨干网络及三种预测策略中，最强的恢复模型以3.6-4.3%的平均相对误差估计尺度（恒定对照为5.7%），且违反物理边界的像素少于0.25%，而两个生成基线则坍缩至接近零的尺度；显式描述符监督提升了描述符精度（MAE的PSNR从18.88 dB提升至23.66 dB）。预测的全Stokes表示改善了漫反射/镜面反射分离、材质分割和眩光分类，尽管在漫反射/镜面反射分离中，学习到的尺度仅与恒定对照表现相当。
英文摘要
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.
Comments22 pages, 17 figures, 8 tables. Accepted to NeurIPS 2026
