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

2026-02-28

AI Summary

Underwater optical imaging suffers from scattering effects that severely degrade the accuracy of 3D surface normal reconstruction. To address this challenge, this work proposes UD-SfPNet, the first end-to-end network framework that jointly integrates image de-scattering and shape-from-polarization (SfP) for surface normal estimation. The method introduces a color embedding module to enhance geometric consistency and incorporates a detail-enhancing convolutional block to preserve high-frequency geometric information. Evaluated on the MuS-Polar3D dataset, UD-SfPNet achieves a mean angular error of 15.12°, significantly outperforming existing approaches and establishing a new state-of-the-art in underwater 3D reconstruction accuracy.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• underwater scattering
• shape-from-polarization
• 3D normal reconstruction
• polarization imaging
• optical imaging

Innovation

• underwater polarization imaging
• shape-from-polarization
• joint descattering and normal estimation
• color embedding module
• detail enhancement convolution