This paper presents a near-infrared (NIR) monocular 3D computational polarization imaging method to directly reconstruct the shape of surfaces exhibiting nonuniform reflectance. A reference gradient field is introduced to the weight constraints for globally correcting the ambiguity of the surface normal for a target with nonuniform reflectance. We experimentally demonstrated that our method can reconstruct the shape of surfaces exhibiting nonuniform reflectance in not only the near field but also the far field. Moreover, with the proposed method, the axial resolution can be kept constant even under different object distances as long as the ratio of the focal length to the object distance is fixed. The simplicity and robustness of the proposed method make it an attractive tool for the fast modeling of 3D scenes.

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.

Polarization 3D imaging has been a research hotspot in the field of 3D facial reconstruction because of its biosafety, high efficiency, and simplicity. However, the application of this technology is limited by the multi-valued problem of the azimuth angle of the normal vector. Currently, the most common method to overcome this limitation is to introduce additional depth techniques at the cost of reducing its applicability. This study presents a passive 3D polarization facial imaging method that does not require additional depth-capturing devices. It addresses the issue of azimuth ambiguity based on prior information about the target image's features. Specifically, by statistically analyzing the probability distribution of real azimuth angles, it is found that their quadrant distribution is closely related to the positions of facial feature points. Therefore, through facial feature detection, the polarized normal azimuth angle of each pixel can be accurately assigned to the corresponding quadrant, thus determining a precise unique normal vector and achieving accurate 3D facial reconstruction. Finally, our azimuth angle correction method was validated by simulated polarization imaging results, and it achieved accurate correction for over 75% of the global pixels without using additional depth techniques. Experimental results further indicate that this method can achieve polarization 3D facial imaging under natural conditions without extra depth devices, and the 3D results preserve edge details and texture information.

Multi-view stereo (MVS) estimates depth by matching features across calibrated views. Though highly accurate on textured surfaces, it is prone to holes and coarse geometry in the case of weak textures or limited viewpoints. Shape-from-polarization (SfP) recovers dense normals from polarized reflections regardless of texture but causes ambiguities in angle inversion, demanding additional disambiguation. Based on these complementary strengths, this paper proposes a passive 3D reconstruction framework that fuses coarse but globally consistent depth priors from a self-attention-enhanced PatchMatch network with fine-grained normal gradients recovered from calibrated polarization measurements. These cues are incorporated into a joint optimization that improves linear consistency between depth and polarization-derived normals while applying a robust, graph-based spatial smoothness constraint to address azimuthal ambiguities and suppress outliers. The final surface is acquired by integrating the optimized normal gradient field in the Fourier domain. Experimental results on different weakly textured objects indicate that our method obtains finer details and fewer artifacts than advanced multi-view stereo and deep learning-based methods, with significantly fewer viewpoints and less computational overhead.

Non-contact and non-destructive polarization 3D imaging uses a passive, single-frame array image to calculate 3D information, making it possible to obtain high-precision 3D information about tunnel cracks, and offering outstanding technical advantages. Based on the introduction of the principle of crack detection with polarization 3D imaging, a tunnel lining crack detection plan was developed and a detection equipment was designed. The method and process of polarization 3D imaging for lining crack detection are described in detail. A model of the impact of the tunnel environment on 3D detection and a method for obtaining absolute information have been established to obtain high-precision 3D information about cracks. In a real tunnel environment, tests were conducted to detect wide cracks, narrow cracks, and artificial cracks. The crack detection accuracy with respect to the crack width was 0.2–0.3 mm, and with respect to crack length was 0.2–0.3 mm. At the same time, crack depth information could be obtained. The present research results can provide technical support for the application of polarization 3D imaging in tunnel crack detection.

The shape from polarization is a noncontact 3D imaging method that shows great potential, but its application is limited by the monocular camera system and surface integration algorithm. This Letter proposes a novel, to the best of our knowledge, method that employs deep neural networks to enhance multi-target 3D reconstruction, making a significant advancement in the field. By constructing the relationship between targets’ blur, distance, and clarity, the proposed method provides accurate spatial information while mitigating inaccuracies arising from the continuous model. Experiments show that the constructed neural network can help improve the multi-target 3D reconstruction quality compared with conventional methods.