单视点镜头畸变摄像机自动标定的最小解算器
Minimal Solvers for Single-View Lens-Distorted Camera Auto-Calibration
摘要：
本文提出了最小的求解器，使用图像的平移对称性和平行的场景线的组合，共同估计镜头不失真与仿射整流或焦距和绝对方向。我们使用正交场景平面提供的约束来恢复焦距。我们表明，求解器使用功能组合可以恢复更准确的校准比求解器只使用一个功能类型的场景，有一个平衡的线条和纹理。我们还表明，所提出的求解器是互补的，可以一起使用在一个基于RANSAC的估计，以提高自动校准精度。国家的最先进的性能表现在镜头失真的城市图像的标准数据集。该代码可在https://github.com/ylochman/single-view-autocalib上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
