PolarVis: A Dataset and Diffusion Framework for RGB-to-Polarization Perception in Adverse Driving Scenes

Key points

• This paper presents RGB-P Wild, a real-world dataset containing 10,000 paired RGB and four-angle linear-polarization image sets, together with PolarVis, a diffusion-based framework for recovering polarization representations from a single RGB image.1
• PolarVis combines hierarchical RGB feature extraction, diffusion-based feature refinement, four angle-specific decoders, and multi-scale attention recalibration to reconstruct polarization intensity images at $0^\circ$, $45^\circ$, $90^\circ$, and $135^\circ$.1
• Compared with the released PolarAnything model under the adopted evaluation protocol, PolarVis obtains an AoLP error of $25.66^\circ$ and a DoLP MAE of 0.0404, compared with $46.03^\circ$ and 0.1084, respectively.1
• In downstream evaluation, a frozen PCDNet achieves AP50/AP50:95 of 86.3%/59.1% on 990 RGBP-Car test images using PolarVis-generated polarization, compared with 87.0%/59.8% using measured polarization.1

Sources (1)

• [1]PolarVis: A Dataset and Diffusion Framework for RGB-to-Polarization Perception in Adverse Driving Scenes
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 04:50 PM

  Reflections, glare, transparent structures, and material-dependent appearance variations can introduce ambiguity into RGB-based visual perception in vehicle-relevant environments.

  This paper presents RGB-P Wild, a real-world dataset containing 10,000 paired RGB and four-angle linear-polarization image sets, together with PolarVis, a diffusion-based framework for recovering polarization representations from a single RGB image.

Extractive summary: sentences quoted from the sources.