Polarization imaging can retrieve inaccurate objects’ 3D shapes with fine textures, whereas coarse but accurate depths can be provided by binocular stereo vision. To take full advantage of these two complementary techniques, we investigate a novel 3D reconstruction method based on the fusion of polarization imaging and binocular stereo vision for high quality 3D reconstruction. We first generate the polarization surface by correcting the azimuth angle errors on the basis of registered binocular depth, to solve the azimuthal ambiguity in the polarization imaging. Then we propose a joint 3D reconstruction model for depth fusion, including a data fitting term and a robust low-rank matrix factorization constraint. The former is to transfer textures from the polarization surface to the fused depth by assuming their relationship linear, whereas the latter is to utilize the low-frequency part of binocular depth to improve the accuracy of the fused depth considering the influences of missing-entries and outliers. To solve the optimization problem in the proposed model, we adopt an efficient solution based on the alternating direction method of multipliers. Extensive experiments have been conducted to demonstrate the efficiency of the proposed method in comparison with state-of-the-art methods and to exhibit its wide application prospects in 3D reconstruction.

偏振成像可以检索具有精细纹理的不准确物体的 3D 形状，而双目立体视觉可以提供粗糙但准确的深度。为了充分利用这两种互补技术，我们研究了一种基于偏振成像和双目立体视觉融合的新型 3D 重建方法，用于高质量 3D 重建。我们首先通过在注册双目深度的基础上校正方位角误差来生成偏振面，以解决偏振成像中的方位模糊。然后我们提出了用于深度融合的联合 3D 重建模型，包括数据拟合项和稳健的低秩矩阵分解约束。前者是通过假设它们的线性关系将纹理从极化表面转移到融合深度，而后者是利用双目深度的低频部分来提高融合深度的准确性，同时考虑到缺失条目和异常值的影响。为了解决所提出模型中的优化问题，我们采用了一种基于乘法器交替方向方法的有效解决方案。已经进行了广泛的实验，以证明所提出的方法与最先进的方法相比的效率，并展示其在 3D 重建中的广泛应用前景。