Calibration Method for Sparse Multi-view Cameras by Bridging with a Mobile Camera
Calibration Method for Sparse Multi-view Cameras by Bridging with a Mobile Camera
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稀疏多视角相机桥接移动相机标定方法
DOI：
10.1109/ipta.2017.8310128
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Itaru Kitahara
中科院分区：
文献类型：
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作者：
Hidehiko Shishido;Itaru Kitahara
摄像机标定是三维重建、三维跟踪等三维图像处理的关键环节之一，它是对三维图像空间和二维图像空间之间的投影关系进行估计的过程。需要放置具有已知3D位置的地标的强校准方法是常用技术。然而，随着目标空间变大，标志放置变得更加复杂。虽然弱校准方法不需要已知地标来从多视点图像之间的对应信息估计投影变换矩阵，但是估计精度取决于对应的精度。当多个相机稀疏地布置时，难以检测足够的对应点。在这项研究中，我们提出了一种校准方法，桥梁稀疏的多个摄像机与移动的摄像机图像。移动的相机在稀疏多视图相机之间移动时捕获视频图像。所捕获的视频类似于密集的多视图图像并且包括稀疏的多视图图像，使得弱校准是有效的。通过改变桥接图像的数目，对摄像机标定精度进行对比实验，确定了合适的图像间距，并将该方法应用于大规模空间的多次捕获实验，验证了其鲁棒性。
Camera calibration that estimates the projective relationship between 3D and 2D image spaces is one of the most crucial processes for such 3D image processing as 3D reconstruction and 3D tracking. A strong calibration method, which needs to place landmarks with known 3D positions, is a common technique. However, as the target space becomes large, landmark placement becomes more complicated. Although a weak-calibration method does not need known landmarks to estimate a projective transformation matrix from the correspondence information among multi-view images, the estimation precision depends on the accuracy of the correspondence. When multiple cameras are arranged sparsely, detecting sufficient corresponding points is difficult. In this research, we propose a calibration method that bridges sparse multiple cameras with mobile camera images. The mobile camera captures video images while moving among sparse multi-view cameras. The captured video resembles dense multi-view images and includes sparse multi-view images so that weak-calibration is effective. We confirmed the appropriate spacing between the images through comparative experiments of camera calibration accuracy by changing the number of bridging images and applied our proposed method to multiple capturing experiments in a large-scale space and verified its robustness.