A Variational Approach to Problems in Calibration of Multiple Cameras
A Variational Approach to Problems in Calibration of Multiple Cameras
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DOI：
10.1109/tpami.2007.1035
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发表时间：
2004-07
影响因子：
23.6
通讯作者：
Gözde B. Ünal;A. Yezzi;Stefano Soatto;G. Slabaugh
中科院分区：
文献类型：
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作者：
Gözde B. Ünal;A. Yezzi;Stefano Soatto;G. Slabaugh
本文讨论了用变分法标定摄像机参数的问题。解决的一个问题是低成本相机中严重的透镜失真。对于许多旨在重建可靠的3D场景表示的计算机视觉算法，如果不考虑相机失真效应，将导致不准确的3D重建和几何测量。第二个问题是由相机响应的变化引起的颜色校准问题，相机响应的变化导致不同的颜色测量并影响依赖于这些测量的算法。我们还解决了外部摄像机校准，估计系统中的多个摄像机的相对姿态和方向和内部摄像机校准，估计焦距和摄像机的倾斜参数。为了解决这些校准问题，我们提出了基于变分方法，利用偏微分方程和常微分方程的多视图立体技术。我们的方法也可以被认为是一个协调的相机校准参数的细化。为了降低这种算法的计算复杂性，我们利用先验知识的校准对象，使一个分段光滑的表面假设，并演变的姿态，方向和比例参数，这样的3D模型对象，而不需要从相机视图的2D特征提取。我们推导出演化方程的失真系数，颜色校准参数，外部和内部参数的相机，并提出实验结果。
This paper addresses the problem of calibrating camera parameters using variational methods. One problem addressed is the severe lens distortion in low-cost cameras. For many computer vision algorithms aiming at reconstructing reliable representations of 3D scenes, the camera distortion effects will lead to inaccurate 3D reconstructions and geometrical measurements if not accounted for. A second problem is the color calibration problem caused by variations in camera responses that result in different color measurements and affects the algorithms that depend on these measurements. We also address the extrinsic camera calibration that estimates relative poses and orientations of multiple cameras in the system and the intrinsic camera calibration that estimates focal lengths and the skew parameters of the cameras. To address these calibration problems, we present multiview stereo techniques based on variational methods that utilize partial and ordinary differential equations. Our approach can also be considered as a coordinated refinement of camera calibration parameters. To reduce computational complexity of such algorithms, we utilize prior knowledge on the calibration object, making a piecewise smooth surface assumption, and evolve the pose, orientation, and scale parameters of such a 3D model object without requiring a 2D feature extraction from camera views. We derive the evolution equations for the distortion coefficients, the color calibration parameters, the extrinsic and intrinsic parameters of the cameras, and present experimental results.