基于多视图像的摄像机自标定方法

Camera self-calibration based on multiple view images

摄像机标定是三维重建时的必要步骤。传统的标定方法对设备要求高、操作繁琐,而自标定方法虽然简便,但精度不高,会严重影响三维重建的效果。因此,越来越需要一种操作简便并且精度高的自标定方法。采用SIFT特征点匹配算法,根据多视序列图像中对应点间的相互关系,利用光束法平差,提出了一种基于局部-全局混合优化的迭代优化方法。针对图像匹配量大的问题,提出了一种邻域内图像互匹配方法来降低时间代价。实验表明,本文提出的多摄像机自标定方法是一种有效的高精度方法,采用的邻域内图像互匹配技术能很好地降低图像匹配的时间消耗。根据多视图像的对应点间相互关系,充分利用局部-全局优化的思想,通过混合优化的方法得到相机参数,对比现有自标定算法,本文给出的方法有较高的精度和鲁棒性。

Camera calibration is an essential step of 3D reconstruction. Conventional calibration methods require high precision equipment and sophisticated operations. Compared with them, camera self-calibration is simple but has low precision, which leads to significant performance degradation of 3D reconstruction. Therefore, there is a growing need for a simple and accurate high precision self-calibration method. By using the bundle adjustment algorithm and SIFT points matching relationship, we propose a local-global hybrid iterative optimization method. As for the large number of matching features, we propose a neighborhood image matching method, which can significantly reduce the matching time under the premise of maintaining accuracy. Experimental results show that the proposed method is effective and accurate, and it can reduce the time consumption of image matching. Based on the relationship between corresponding matching points in multi view images, our method makes full use of the local-global hybrid idea to compute the parameters of the camera. Compared with other existing methods, it is more robust with higher precision.