论文标题
单视镜头触发相机自动校准的最小求解器
Minimal Solvers for Single-View Lens-Distorted Camera Auto-Calibration
论文作者
论文摘要
本文提出了最小的求解器，这些求解器使用成像的翻译对称性和平行场景线的组合来共同估计晶状体不合适的，即仿射整流，焦距长度和绝对方向。我们使用正交场景平面提供的约束来恢复焦距。我们表明，使用功能组合的求解器比仅使用一种具有线条和纹理平衡的场景上的特征类型的求解器可以恢复更准确的校准。我们还表明，所提出的求解器是互补的，可以在基于RANSAC的估计器中一起使用以提高自动校准精度。在镜头延伸的城市图像的标准数据集中证明了最先进的性能。该代码可在https://github.com/ylochman/single-view-autocalib上找到。
This paper proposes minimal solvers that use combinations of imaged translational symmetries and parallel scene lines to jointly estimate lens undistortion with either affine rectification or focal length and absolute orientation. We use constraints provided by orthogonal scene planes to recover the focal length. We show that solvers using feature combinations can recover more accurate calibrations than solvers using only one feature type on scenes that have a balance of lines and texture. We also show that the proposed solvers are complementary and can be used together in a RANSAC-based estimator to improve auto-calibration accuracy. State-of-the-art performance is demonstrated on a standard dataset of lens-distorted urban images. The code is available at https://github.com/ylochman/single-view-autocalib.
