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具有径向对称畸变的相机校准。

Calibration of cameras with radially symmetric distortion.

作者信息

Tardif Jean-Philippe, Sturm Peter, Trudeau Martin, Roy Sébastien

机构信息

Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104-6228, USA.

出版信息

IEEE Trans Pattern Anal Mach Intell. 2009 Sep;31(9):1552-66. doi: 10.1109/TPAMI.2008.202.

Abstract

We present algorithms for plane-based calibration of general radially distorted cameras. By this, we understand cameras that have a distortion center and an optical axis such that the projection rays of pixels lying on a circle centered on the distortion center form a right viewing cone centered on the optical axis. The camera is said to have a single viewpoint (SVP) if all such viewing cones have the same apex (the optical center); otherwise, we speak of NSVP cases. This model encompasses the classical radial distortion model [5], fisheyes, and most central or noncentral catadioptric cameras. Calibration consists in the estimation of the distortion center, the opening angles of all viewing cones, and their optical centers. We present two approaches of computing a full calibration from dense correspondences of a single or multiple planes with known euclidean structure. The first one is based on a geometric constraint linking viewing cones and their intersections with the calibration plane (conic sections). The second approach is a homography-based method. Experiments using simulated and a broad variety of real cameras show great stability. Furthermore, we provide a comparison with Hartley-Kang's algorithm [12], which, however, cannot handle such a broad variety of camera configurations, showing similar performance.

摘要

我们提出了用于对一般径向畸变相机进行基于平面校准的算法。在此,我们所说的相机是指具有一个畸变中心和一条光轴的相机,使得位于以畸变中心为圆心的圆上的像素的投影光线形成一个以光轴为中心的直视图锥。如果所有这样的视锥都具有相同的顶点(光心),则称该相机具有单视点(SVP);否则,我们称之为非单视点(NSVP)情况。该模型涵盖了经典的径向畸变模型[5]、鱼眼镜头以及大多数中心或非中心折反射相机。校准包括估计畸变中心、所有视锥的张角及其光心。我们提出了两种从具有已知欧几里得结构的单个或多个平面的密集对应关系计算完全校准的方法。第一种方法基于将视锥与其在校准平面上的交点(圆锥曲线)联系起来的几何约束。第二种方法是基于单应性的方法。使用模拟相机和各种真实相机进行的实验显示出很高的稳定性。此外,我们与Hartley-Kang算法[12]进行了比较,然而,该算法无法处理如此广泛的相机配置,但其性能与之相似。

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