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

• URL: http://arxiv.org/abs/2609.09359v1
• Date: Tue, 08 Sep 2026 18:48:33 GMT
• Title: DensePol: Dense-Angle Polarization Dataset for Learning-Based Polarimetric Vision
• Abstract summary: We introduce DensePol, a high-redundancy RGB--polarization dataset based on Division-of-Time (DoT) acquisition.<n>Dense angular sampling substantially improves polarization stability, reducing AoLP deviation from $13.36circ$ to $2.21circ$.<n>Experiments demonstrate improved polarization prediction and downstream surface-normal estimation.
• Score: 8.673131921192995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Stokes-Informed Diffusion for Robust Linear Polarization Estimation [49.459667092743445]
  GenPolar is a Stokes-informed diffusion framework grounded in the Mueller formalism from an intensity observation.<n>It achieves state-of-the-art performance in both DoLP fidelity and AoP stability.<n>These improvements translate to significant and consistent gains in downstream applications, including material detection and de-reflection.
  arXiv  Detail & Related papers  (2026-07-23T11:56:15Z)
• PolarVSR: A Unified Framework and Benchmark for Continuous Space-Time Polarization Video Reconstruction [42.44037120162351]
  Polarimetric imaging captures surface polarization characteristics, such as the Degree of Linear Polarization (DoLP) and the Angle of Polarization (AoP)<n>Existing Division of-Focal-Plane (DoFP) cameras face hardware bottlenecks and often cannot support high-frame-rate acquisition.<n>We propose the first space-time polarization video reconstruction architecture.
  arXiv  Detail & Related papers  (2026-05-11T09:33:50Z)
• Poppy: Polarization-based Plug-and-Play Guidance for Enhancing Monocular Normal Estimation [69.74622862203036]
  Poppy is a training-free framework that refines normals from any frozen RGB backbone using single-shot polarization measurements at test time.<n>A differentiable rendering layer converts the refined normals into polarization predictions and penalizes mismatches with the observed signal.<n>These results show that guiding learned RGB-based normal estimators with polarization cues at test time refines normals on challenging surfaces without retraining.
  arXiv  Detail & Related papers  (2026-03-29T22:05:09Z)
• PolarAnything: Diffusion-based Polarimetric Image Synthesis [59.14294818211059]
  We propose PolarAnything, capable of synthesizing polarization images from a single RGB input with both photorealism and physical accuracy.<n>Experiments show that our model generates high-quality polarization images and supports downstream tasks like shape from polarization.
  arXiv  Detail & Related papers  (2025-07-23T07:09:10Z)
• PIDSR: Complementary Polarized Image Demosaicing and Super-Resolution [63.87900679891159]
  The resolution of a polarization camera is often much lower than that of a conventional RGB camera.<n>Existing polarized image demosaicing (PID) methods are limited in that they cannot enhance resolution.<n>We propose PIDSR, a joint framework that performs complementary Polarized Image Demosaicing and Super-Resolution.
  arXiv  Detail & Related papers  (2025-04-10T13:56:33Z)
• Glossy Object Reconstruction with Cost-effective Polarized Acquisition [41.96986483856648]
  This work introduces a scalable polarization-aided approach that employs cost-effective acquisition tools.<n>The proposed approach represents polarimetric BRDF, Stokes vectors, and polarization states of object surfaces as neural implicit fields.<n>By leveraging fundamental physical principles for the implicit representation of polarization rendering, our method demonstrates superiority over existing techniques.
  arXiv  Detail & Related papers  (2025-04-09T16:38:51Z)
• Benchmarking Burst Super-Resolution for Polarization Images: Noise Dataset and Analysis [14.056265185258534]
  A polarization camera employs a double Bayer-Burst sensor to capture both color and polarization.<n>It demonstrates low light efficiency and low spatial resolution, resulting in increased noise and compromised polarization measurements.<n>Applying burst super-resolution to polarization imaging poses challenges due to the lack of tailored datasets and reliable ground truth noise statistics.<n>This work establishes a benchmark for polarization burst super-resolution and offers critical insights into noise propagation, thereby enhancing polarization image reconstruction.
  arXiv  Detail & Related papers  (2025-03-24T14:17:18Z)
• Learning to Deblur Polarized Images [59.33932533761383]
  A polarization camera can capture four linear polarized images with different polarizer angles in a single shot.<n>Since the on-chip micro-polarizers block part of the light so that the sensor often requires a longer exposure time, the captured polarized images are prone to motion blur.<n>We propose a polarized image deblurring pipeline to solve the problem in a polarization-aware manner.
  arXiv  Detail & Related papers  (2024-02-28T07:56:28Z)
• PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection [81.16859686137435]
  We present PARTNER, a novel 3D object detector in the polar coordinate. Our method outperforms the previous polar-based works with remarkable margins of 3.68% and 9.15% on and ONCE validation set.
  arXiv  Detail & Related papers  (2023-08-08T01:59:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.