Stratified Self-Calibration with the Modulus Constraint
Stratified Self-Calibration with the Modulus Constraint
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DOI：
10.1109/34.784285
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发表时间：
1999-08
期刊：
影响因子：
--
通讯作者：
M. Pollefeys;L. Gool
中科院分区：
文献类型：
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作者：
M. Pollefeys;L. Gool
在计算机视觉中，特别是在三维重建中，相机标定参数的检索是关键问题之一。这些都需要从相机获得关于场景的度量信息。这些参数通常是通过繁琐的校准程序获得的。有一种方法可以避免对相机进行显式校准。自校准是基于寻找满足某些约束的校准参数集（例如，恒定的校准参数）。已经提出了几种技术，但往往证明很难立即达到公制校准。因此，本文提出了一种分层方法，从射影到仿射再到度量。实现这一点的关键概念是模约束。它允许对恒定内在参数的仿射校准进行检索。它也适合与场景知识结合使用。此外，如果仿射校准是已知的，它也可以用来应付焦距的变化。
In computer vision and especially for 3D reconstruction, one of the key issues is the retrieval of the calibration parameters of the camera. These are needed to obtain metric information about the scene from the camera. Often these parameters are obtained through cumbersome calibration procedures. There is a way to avoid explicit calibration of the camera. Self-calibration is based on finding the set of calibration parameters which satisfy some constraints (e.g., constant calibration parameters). Several techniques have been proposed but it often proved difficult to reach a metric calibration at once. Therefore, in the paper, a stratified approach is proposed, which goes from projective through affine to metric. The key concept to achieve this is the modulus constraint. It allows retrieval of the affine calibration for constant intrinsic parameters. It is also suited for use in conjunction with scene knowledge. In addition, if the affine calibration is known, it can also be used to cope with a changing focal length.