DensePol: Dense-Angle Polarization Dataset for Learning-Based Polarimetric Vision

2026-09-08

AI Summary

为解决RGB图像难以恢复场景形状、材质和反射的问题，通过构建高冗余角度的DensePol数据集并使用确定性扩散框架，提高从RGB到偏振预测的准确性。

Abstract

Polarimetric vision is gaining increasing attention because it provides physical cues about scene shape, material, and reflection that are difficult to recover from RGB alone. Recent work has therefore explored predicting polarization directly from conventional RGB images; however, the fidelity of these methods strongly depends on the polarization supervision used for training. Most existing datasets rely on Division-of-Focal-Plane (DoFP) cameras with four spatially interleaved analyzer orientations, which provide limited angular redundancy and introduce interpolation and instantaneous-field-of-view errors. We introduce DensePol, a high-redundancy RGB--polarization dataset based on Division-of-Time (DoT) acquisition, capturing 180 full-resolution analyzer orientations at $1^\circ$ intervals. DensePol contains 2,018 paired RGB--polarization images with the angular measurements and fitting residuals retained. Dense angular sampling substantially improves polarization stability, reducing AoLP deviation from $13.36^\circ$ to $2.21^\circ$. We further introduce a deterministic diffusion-based RGB-to-polarization framework with cyclic AoLP representation and a local DoLP refiner. Experiments demonstrate improved polarization prediction and downstream surface-normal estimation. The dataset and code will be publicly available.

Problem

• Polarimetric Vision
• RGB-to-Polarization
• Dataset
• Angular Redundancy
• Division-of-Focal-Plane (DoFP)

Innovation

• Dense-Angle Polarization
• Division-of-Time (DoT)
• Polarimetric Vision
• Angular Redundancy
• Cyclic AoLP Representation