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

arXiv: https://arxiv.org/abs/2609.09359 · HTML: https://mlsanity.com/paper/2609.09359/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 8 Sep 2026.

## Builds on (most cited first, 15 of 15)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [PolarFree: Polarization-based Reflection-free Imaging](https://mlsanity.com/paper/2503.18055/) (2025)
- [Monochrome and Color Polarization Demosaicking Using Edge-Aware Residual Interpolation](https://mlsanity.com/paper/2007.14292/) (2020)
- [PIDSR: Complementary Polarized Image Demosaicing and Super-Resolution](https://mlsanity.com/paper/2504.07758/) (2025)
- [Simultaneous Acquisition of High Quality RGB Image and Polarization Information using a Sparse Polarization Sensor](https://mlsanity.com/paper/2209.13106/) (2022)
- [Spectral and Polarization Vision: Spectro-polarimetric Real-world Dataset](https://mlsanity.com/paper/2311.17396/) (2023)
- [RGB-to-Polarization Estimation: A New Task and Benchmark Study](https://mlsanity.com/paper/2505.13050/) (2025)
- [PolarAnything: Diffusion-based Polarimetric Image Synthesis](https://mlsanity.com/paper/2507.17268/) (2025)
- [Polarization Denoising and Demosaicking: Dataset and Baseline Method](https://mlsanity.com/paper/2509.10098/) (2025)
- [Benchmarking Burst Super-Resolution for Polarization Images: Noise Dataset and Analysis](https://mlsanity.com/paper/2503.18705/) (2025)
- [Stokes-Informed Diffusion for Robust Linear Polarization Estimation](https://mlsanity.com/paper/2607.21239/) (2026)

## Abstract (arXiv)

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.