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UD-SfPNet: An Underwater Descattering Shape-from-Polarization Network for 3D Normal Reconstruction

Puyun Wang, Kaimin Yu, Huayang He, Feng Huang, Xianyu Wu, Yating Chen

TL;DR

UD-SfPNet, an underwater descattering shape-from-polarization network that leverages polarization cues for improved 3D surface normal prediction, and incorporates a novel color embedding module to enhance geometric consistency by exploiting the relationship between color encodings and surface orientation.

Abstract

Underwater optical imaging is severely hindered by scattering, but polarization imaging offers the unique dual advantages of descattering and shape-from-polarization (SfP) 3D reconstruction. To exploit these advantages, this paper proposes UD-SfPNet, an underwater descattering shape-from-polarization network that leverages polarization cues for improved 3D surface normal prediction. The framework jointly models polarization-based image descattering and SfP normal estimation in a unified pipeline, avoiding error accumulation from sequential processing and enabling global optimization across both tasks. UD-SfPNet further incorporates a novel color embedding module to enhance geometric consistency by exploiting the relationship between color encodings and surface orientation. A detail enhancement convolution module is also included to better preserve high-frequency geometric details that are lost under scattering. Experiments on the MuS-Polar3D dataset show that the proposed method significantly improves reconstruction accuracy, achieving a mean surface normal angular error of 15.12$^\circ$ (the lowest among compared methods). These results confirm the efficacy of combining descattering with polarization-based shape inference, and highlight the practical significance and potential applications of UD-SfPNet for optical 3D imaging in challenging underwater environments. The code is available at https://github.com/WangPuyun/UD-SfPNet.

UD-SfPNet: An Underwater Descattering Shape-from-Polarization Network for 3D Normal Reconstruction

TL;DR

UD-SfPNet, an underwater descattering shape-from-polarization network that leverages polarization cues for improved 3D surface normal prediction, and incorporates a novel color embedding module to enhance geometric consistency by exploiting the relationship between color encodings and surface orientation.

Abstract

Underwater optical imaging is severely hindered by scattering, but polarization imaging offers the unique dual advantages of descattering and shape-from-polarization (SfP) 3D reconstruction. To exploit these advantages, this paper proposes UD-SfPNet, an underwater descattering shape-from-polarization network that leverages polarization cues for improved 3D surface normal prediction. The framework jointly models polarization-based image descattering and SfP normal estimation in a unified pipeline, avoiding error accumulation from sequential processing and enabling global optimization across both tasks. UD-SfPNet further incorporates a novel color embedding module to enhance geometric consistency by exploiting the relationship between color encodings and surface orientation. A detail enhancement convolution module is also included to better preserve high-frequency geometric details that are lost under scattering. Experiments on the MuS-Polar3D dataset show that the proposed method significantly improves reconstruction accuracy, achieving a mean surface normal angular error of 15.12 (the lowest among compared methods). These results confirm the efficacy of combining descattering with polarization-based shape inference, and highlight the practical significance and potential applications of UD-SfPNet for optical 3D imaging in challenging underwater environments. The code is available at https://github.com/WangPuyun/UD-SfPNet.
Paper Structure (20 sections, 20 equations, 15 figures, 3 tables)

This paper contains 20 sections, 20 equations, 15 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Polarization-based descattering
  4. Polarization-based 3D imaging
  5. Methodology
  6. Principles of polarization imaging
  7. Solving the zenith angle $\theta$
  8. Solving the azimuth angle $\alpha$
  9. Network architecture
  10. Polarization Parameter Network (PPN)
  11. Descattering Network (DN)
  12. Normal Estimation Network (NEN)
  13. Color embedding module
  14. Detail-enhanced convolution module
  15. Further discussion
  16. ...and 5 more sections

Figures (15)

  • Figure 1: Schematic of the proposed underwater-robot polarization 3D visual perception pipeline.
  • Figure 2: Underwater polarization imaging model.
  • Figure 3: Schematic of the minimal element in the surface microfacet model.
  • Figure 4: Physical model of polarization-based 3D reconstruction: (a) global view; (b) camera view.
  • Figure 5: Relation between the degree of polarization $\rho$ and the zenith angle $\theta$ for a given refractive index $\eta$: (a) specular reflection; (b) diffuse reflection.
  • ...and 10 more figures