## 1 Introduction  
Maritime target recognition faces notable challenges due to the complexity of marine environments, such as varying lighting conditions, wave interference, and weather factors. These conditions severely reduce imaging accuracy, particularly for long-distance maritime target recognition and detection. To address these challenges, researchers have developed various three-dimensional imaging technologies, such as structured light 3D imaging, stereo vision, holographic 3D imaging, light detection and ranging (LiDAR), and time-of-flight (ToF) technology.  
Structured light 3D imaging involves projecting a known structured light pattern onto a target surface and using cameras to capture the deformed light pattern to reconstruct the target's 3D information. However, this method is highly sensitive to ambient light, especially in complex outdoor settings. Furthermore, the high complexity of structured light 3D imaging systems limits their practicality in real-world 3D applications [1]. Although stereo vision can reconstruct the 3D information of targets by capturing two or more images from different angles, the disparity is often inaccurate in low-texture and repetitive texture regions; moreover, image matching increases computational costs [2]. Holographic 3D imaging relies on high-precision optical components and stable lasers, making it impractical and costly for real-world applications because of its complex system architecture [3].  
Although LiDAR provides excellent accuracy, it performs poorly in marine environments, particularly under adverse weather conditions [4]. In addition, laser signal degradation over long distances hampers its suitability for long-range target identification [5]. ToF technology relies on measuring the target distance based on light speed differences but suffers from notably reduced accuracy with increasing scene depth, requiring high-frequency signals and complex electronic components to mitigate signal attenuation [6].  
More recently, polarized 3D imaging has emerged as a prominent research area. This technology is based on using the polarization properties of light to capture the geometric morphology of a target's surface under complex lighting conditions by analyzing the polarization changes in the reflected light. Polarized 3D imaging overcomes the limitations of traditional 3D imaging methods for long-range imaging and adapts to varying lighting conditions [7], making it highly suitable for target recognition and imaging in marine environments. By using polarized 3D imaging, essential geometric information, such as the zenith and azimuth angles of light reflected from an object's surface, can be obtained for 3D reconstruction. Unlike traditional geometric 3D imaging, which relies on multiview images or structured light, polarized 3D imaging derives normal surface information from the polarization properties of light using a single image [8].  
In practical applications, the zenith and azimuth angles are critical for precise 3D reconstruction. Numerous studies have examined methods for extracting these angles from polarized images for 3D target reconstruction. [PERSON] et al. proposed an improved normal estimation method by analyzing the combined polarization properties of specular and diffuse reflections, which enhanced 3D reconstruction accuracy [9]. [PERSON] et al. introduced a surface normal estimation strategy that simultaneously employs polarization and geometric information [10]. [PERSON] et al. explored ways to improve the reconstruction accuracy of complex surface shapes by incorporating near-infrared polarization data [11].