PolarVSR: A Unified Framework and Benchmark for Continuous Space-Time Polarization Video Reconstruction

• URL: http://arxiv.org/abs/2605.10275v1
• Date: Mon, 11 May 2026 09:33:50 GMT
• Title: PolarVSR: A Unified Framework and Benchmark for Continuous Space-Time Polarization Video Reconstruction
• Abstract summary: 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.
• Score: 42.44037120162351
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Polarimetric imaging captures surface polarization characteristics, such as the Degree of Linear Polarization (DoLP) and the Angle of Polarization (AoP). In mainstream Division of-Focal-Plane (DoFP) color polarization imaging, recovering polarization parameters from captured mosaic arrays remains a challenging inverse problem. Existing DoFP cameras also face hardware bottlenecks and often cannot support high-frame-rate acquisition, limiting polarimetric imaging in dynamic video tasks. These limitations motivate joint spatial and temporal enhancement. To this end, we propose the first space-time polarization video reconstruction architecture. The method jointly models polarization directions in space and time and uses a polarization-aware implicit neural representation for continuous, high-fidelity upsampling. By analyzing temporal variations in polarization parameters, we further introduce a flow-guided polarization variation loss to supervise polarization dynamics. We also establish the first large-scale color DoFP polarization video benchmark to support this research direction. Extensive experiments on this benchmark demonstrate the effectiveness of the method.

Related papers

• DensePol: Dense-Angle Polarization Dataset for Learning-Based Polarimetric Vision [8.673131921192995]
  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.
  arXiv  Detail & Related papers  (2026-09-08T18:48:33Z)
• 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)
• High-Resolution Single-Shot Polarimetric Imaging Made Easy [76.04914313843774]
  EasyPolar is a multi-view polarimetric imaging framework.<n>Three synchronized RGB cameras capture one unpolarized view and two polarized views with distinct orientations.<n>A confidence-guided polarization reconstruction network is proposed to address the potential misalignment in multi-view fusion.
  arXiv  Detail & Related papers  (2026-04-07T08:20:58Z)
• 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)
• 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)
• Deep Snapshot HDR Reconstruction Based on the Polarization Camera [3.534261120716916]
  On-chip micro-polarizer technology has made it possible to acquire four spatially aligned and temporally synchronized polarization images. We study the relationship among polarizer orientation, degree and angle of polarization of light to the exposure time of a pixel in the polarization image. We propose a deep snapshot HDR reconstruction framework to recover an HDR image using the polarization images.
  arXiv  Detail & Related papers  (2021-05-12T17:35:10Z)

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.