Accurate passive 3D face reconstruction is of great importance with various potential applications. Three-dimensional polarization face reconstruction is a promising approach, but one bothered by serious deformations caused by an ambiguous surface normal. In this study, we propose a learning-based method for passive 3D polarization face reconstruction. It first calculates the surface normal of each microfacet at a pixel level based on the polarization of diffusely reflected light on the face, where no auxiliary equipment, including artificial illumination, is required. Then, the CNN-based 3DMM (convolutional neural network; 3D morphable model) generates a rough depth map of the face with the directly captured polarization image. The map works as an extra constraint to correct the ambiguous surface normal obtained from polarization. An accurate surface normal finally allows for an accurate 3D face reconstruction. Experiments in both indoor and outdoor conditions demonstrate that accurate 3D faces can be well-reconstructed. Moreover, with no auxiliary equipment required, the method ensures a total passive 3D face reconstruction.

This study introduces a novel imaging technology designed to address the critical challenges of high integration, wide field of view (FOV), and high-resolution detection in optoelectronic systems. The proposed approach leverages ultra-small pixels and a unique “single-eye + compound-eye” architecture, combining a concentric spherical lens for wide-FOV light collection with a distributed camera array for segmented imaging and stitching of the Petzval image plane. This design enables high-resolution imaging across a large area while maintaining compact system dimensions. The ultra-small-pixel large-format detector excels in capturing fine details, and by applying linear system theory, the technology achieves significant reductions in system size and complexity without compromising performance. Experimental validation shows that the imaging system achieves a modulation transfer function near the diffraction limit at 500 lp/mm, with a root-mean-square spot size consistently below 1 $\mathrm{\mu }$m. Additionally, the system delivers an angular resolution of 25 $\mathrm{\mu }$rad and distortion-free imaging over a ${61.5}^{\circ }\times {55}^{\circ }$ FOV.

Multi-target imaging beyond the optical memory effect (OME) is fundamentally challenged by the incoherent superposition of speckles originating from different OME regions, which renders object separation highly ill-posed. To address this challenge, we introduce a polarization-guided blind unmixing framework that exploits the distinct polarization responses of different targets as an additional discrimination cue for speckle separation. Polarized speckles are first used to construct a dense polarization-response representation through Stokes-based synthesis. The resulting polarization diversity is leveraged to estimate the number of targets without prior knowledge and to identify structurally representative autocorrelation endmembers associated with different objects. An energy-constrained non-negative matrix factorization is then developed to separate individual target autocorrelations, followed by phase retrieval for object reconstruction. Experimental results demonstrate the reconstruction of up to five targets located beyond the OME range, achieving target separations of up to 2.6× the measured OME limit and peak signal-to-noise ratio (PSNR) values above 25 dB, without requiring prior knowledge of target locations, structures, polarization states, or target number.