[Submitted on 8 Sep 2026]
Title:DensePol: Dense-Angle Polarization Dataset for Learning-Based Polarimetric Vision
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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.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.09359 [cs.CV]
(or arXiv:2609.09359v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.09359
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Param Sangani [view email] [v1] Tue, 8 Sep 2026 18:48:33 UTC (3,046 KB)
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