MuS-Polar3D: A Benchmark Dataset for Computational Polarimetric 3D Imaging under Multi-Scattering Conditions
Puyun Wang, Kaimin Yu, Huayang He, Xianyu Wu

TL;DR
MuS-Polar3D is the first comprehensive benchmark dataset for polarization-based underwater 3D imaging under various scattering conditions, facilitating fair evaluation and development of reconstruction algorithms.
Contribution
The paper introduces MuS-Polar3D, a diverse, publicly available dataset with controlled scattering conditions, supporting multiple vision tasks for underwater 3D imaging research.
Findings
Effective decoupling of descattering and 3D reconstruction stages.
Achieved a mean angular error of 15.49 degrees in evaluations.
Demonstrated the dataset's utility for fair comparison of algorithms.
Abstract
Polarization-based underwater 3D imaging exploits polarization cues to suppress background scattering, exhibiting distinct advantages in turbid water. Although data-driven polarization-based underwater 3D reconstruction methods show great potential, existing public datasets lack sufficient diversity in scattering and observation conditions, hindering fair comparisons among different approaches, including single-view and multi-view polarization imaging methods. To address this limitation, we construct MuS-Polar3D, a benchmark dataset comprising polarization images of 42 objects captured under seven quantitatively controlled scattering conditions and five viewpoints, together with high-precision 3D models (+/- 0.05 mm accuracy), normal maps, and foreground masks. The dataset supports multiple vision tasks, including normal estimation, object segmentation, descattering, and 3D…
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Taxonomy
TopicsOptical Polarization and Ellipsometry · Image Enhancement Techniques · Synthetic Aperture Radar (SAR) Applications and Techniques
