MPIG: Multi-angle Polarization Image Generation from a Single Polarization Observation

Key points

• Polarization imaging provides informative cues for material analysis, scene understanding, and visual perception.1
• In this work, we address the problem of predicting the missing $45^\circ$, $90^\circ$, and $135^\circ$ polarization views from a single $0^\circ$ observation by leveraging a learned scene prior.1
• We also construct HighPol, a large-scale real-world polarization dataset comprising 5,474 groups of high-resolution four-angle polarization images collected from diverse indoor and outdoor scenes.1
• Furthermore, downstream validation on glass and semantic-segmentation tasks suggests that the generated polarization images can preserve task-relevant information, demonstrating the effectiveness of MPIG and the practical utility of predicted polarization observations for downstream vision applications.1

Sources (1)

• [1]MPIG: Multi-angle Polarization Image Generation from a Single Polarization Observation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 03:13 PM

  To this end, we propose MPIG (Multi-angle Polarization Image Generation), a Swin Transformer-based framework that couples cross-angle feature interaction with residual decoding to predict angle-dependent intensity variations while preserving shared scene structure.

  Polarization imaging provides informative cues for material analysis, scene understanding, and visual perception.

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