Deep learning-based polarization 3D imaging method for underwater targets.

Abstract

The significant absorption and scattering of light during its propagation in water severely degrade the quality of underwater imaging, presenting challenges for developing high-precision 3D imaging techniques based on optical methods. Polarization imaging has demonstrated effectiveness in mitigating the effects of scattering, making it a valuable approach for underwater imaging. Additionally, the polarization state of reflected light can be utilized for surface normal estimation and 3D shape reconstruction. This paper presents a learning-based method for 3D shape reconstruction of underwater targets using shape from polarization techniques. To address the lack of publicly available datasets for underwater polarization 3D imaging, we have developed a data acquisition system that simulates Jerlov Type I water conditions, creating a dataset of underwater polarized images along with corresponding ground truth surface normal images. Furthermore, we propose a network framework based on Attention U²Net for the 3D reconstruction of underwater polarized images. This framework is designed to capture detailed texture information of underwater targets and incorporates an effective polarization representation to resolve azimuthal ambiguity, thus enhancing the accuracy of underwater 3D imaging. Experimental results demonstrate that our method effectively addresses azimuthal ambiguity, reduces texture loss during reconstruction, and improves the accuracy of surface normal estimation, achieving superior performance compared to existing methods.