Polarization-guided three-dimensional recovery through scattering media

Three-dimensional (3D) recovery through scattering media remains a central challenge in optical imaging. The medium randomly scatters the optical field, mixing and obscuring information and rendering conventional decoding strategies ineffective. To address this, we propose polarization to impose physical constraints on intensity recovery, as build a key bridge between descattering imaging and 3D recovery. A polarization-guided network architecture is developed that directly incorporates the polarization characteristics of the scattered field into the network as differentiable physical constraints. By combining learned feature extraction with explicit physical computation, this enhances the accuracy of physical information recovery, thereby laying a reliable foundation for subsequent 3D recovery. Experimental results show that the proposed method yields an improvement in the joint recovery of intensity and polarization information and successfully reconstructs 3D recovery under random scattering conditions.