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

TL;DR

UD-SfPNet is a novel deep learning framework that jointly performs underwater image descattering and shape-from-polarization 3D surface normal reconstruction, significantly improving accuracy in scattering-affected environments.

Contribution

It introduces a unified pipeline for simultaneous descattering and shape-from-polarization estimation, incorporating a color embedding and detail enhancement modules for better geometric accuracy.

Findings

Achieves a mean surface normal error of 15.12° on MuS-Polar3D dataset.

Outperforms existing methods in underwater 3D reconstruction accuracy.

Demonstrates the effectiveness of joint descattering and polarization-based shape inference.

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.