Self-calibration of a moving camera from point correspondences and fundamental matrices

被引:289
作者
Luong, QT [1 ]
Faugeras, OD [1 ]
机构
[1] INRIA, F-06902 SOPHIA ANTIPOLIS, FRANCE
关键词
camera calibration; projective geometry; Euclidean geometry; Kruppa equations;
D O I
10.1023/A:1007982716991
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We address the problem of estimating three-dimensional motion, and structure from motion with an uncalibrated moving camera. We show that point correspondences between three images, and the fundamental matrices computed from these point correspondences, are sufficient to recover the internal orientation of the camera (its calibration), the motion parameters, and to compute coherent perspective projection matrices which enable us to reconstruct 3-D structure up to a similarity. In contrast with other methods, no calibration object with a known 3-D shape is needed, and no limitations are put upon the unknown motions to be performed or the parameters to be recovered, as long as they define a projective camera. The theory of the method, which is based on the constraint that the observed points are part of a static scene, thus allowing us to link the intrinsic parameters and the fundamental matrix via the absolute conic, is first detailed. Several algorithms are then presented, and their performances compared by means of extensive simulations and illustrated by several experiments with real images.
引用
收藏
页码:261 / 289
页数:29
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