Depth position highly affects lens distortion, especially in close-range photography, which limits the measurement accuracy of existing stereo vision systems. Moreover, traditional depth-dependent distortion models and their calibration methods have remained complicated. In this work, we propose a minimal set of parameters based depth-dependent distortion model (MDM), which considers the radial and decentering distortions of the lens to improve the accuracy of stereo vision systems and simplify their calibration process. In addition, we present an easy and flexible calibration method for the MDM of stereo vision systems with a commonly used planar pattern, which requires cameras to observe the planar pattern in different orientations. The proposed technique is easy to use and flexible compared with classical calibration techniques for depth-dependent distortion models in which the lens must be perpendicular to the planar pattern. The experimental validation of the MDM and its calibration method showed that the MDM improved the calibration accuracy by 56.55% and 74.15% compared with the Li's distortion model and traditional Brown's distortion model. Besides, an iteration-based reconstruction method is proposed to iteratively estimate the depth information in the MDM during three-dimensional reconstruction. The results showed that the accuracy of the iteration-based reconstruction method was improved by 9.08% compared with that of the non-iteration reconstruction method.

翻译：深度位置对镜头畸变影响显著，尤其在近距摄影中，这限制了现有立体视觉系统的测量精度。此外，传统的深度相关畸变模型及其标定方法仍较为复杂。本文提出一种基于最小参数集的深度相关畸变模型（MDM），该模型同时考虑镜头的径向畸变和离心畸变，旨在提升立体视觉系统精度并简化标定流程。同时，我们针对立体视觉系统提出一种简易灵活的MDM标定方法，该方法仅需使用常见平面靶标，通过摄像机在不同方位观察该靶标即可实现。与传统深度相关畸变模型的经典标定技术（需保证镜头垂直于平面靶标）相比，所提方法更易操作且灵活性更高。实验验证表明，与Li畸变模型及传统Brown畸变模型相比，MDM使标定精度分别提升56.55%和74.15%。此外，本文提出一种基于迭代的三维重建方法，通过迭代估计MDM中的深度信息。结果表明，相较于非迭代重建方法，该迭代重建方法的精度提升了9.08%。