High-quality 3D shape recovery from scattering scenario via deep polarization neural networks

Abstract

High-quality three-dimension(3D) shape recovery from scattering scenario is of great importance with various potential applications. However, in turbid water scenarios, the absorption and scattering by water and suspending particles have a great disturbance on the generation of image clarity, texture details, and pixel-wise polarized properties such as degree of polarization (DoP) and angle of polarization (AoP), which significantly affect the 3D reconstructed performance. In this study, we present a data-driven polarimetric method for 3D reconstruction of underwater objects in turbid scenes, leveraging deep learning image priors. An underwater descattering network is designed to fit the underwater transport function of target signal light and generate clear underwater images, which are utilized to enhance the texture and remove the backscattering light. Subsequently, accurate pixel-wise DoP and AoP of the object could be calculated using the clear image prior and underwater imaging model. Finally, utilizing the obtained clear image and polarimetric information, polarimetric 3D reconstruction of object in turbid underwater scenarios is achieved. Experimental results demonstrate that accurate 3D shape of objects in turbid water can be well-reconstructed. The proposed technique has great potential for advancing marine exploration research.