基于基本矩阵的变焦立体相机焦距变化估计

Kurnia Prima Putra, E. M. Yuniarno, M. Purnomo
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引用次数: 0

摘要

基本矩阵是不同相机位置和方向拍摄的两幅图像之间关系的代数表示。它通常用于从未经校准的相机拍摄的图像中重建3D物体。在未校准的相机上,我们没有焦距的信息,因此重建的物体与现实世界的单位不匹配。如果对图像进行缩放,则会对图像中对应的点进行缩放,影响重构对象的结果。为了避免它,需要每个图像的焦距值。在变焦过程中,对每一帧进行手动标定是不现实的。可以使用自校准,但这需要对场景有一定的了解,比如场景上的3D平行线。如果所记录的场景是完全未知的,这使得很难知道像平面的焦距。本文利用立体图像基本矩阵内的信息进行了焦距变化估计。利用该方法可以减少三维物体重建所需的点匹配过程。为了解决这一问题,我们尝试利用立体像对基本矩阵的元素,以最小变焦的固有参数为参考,求得焦距的估计值。在本文中,我们设法使用基本矩阵来估计相对于已知焦距的焦距变化。估计结果表明,与使用点距估计相比,估计结果略有差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Focal Length Changes Estimation on Zooming Stereo Camera using Fundamental Matrix
Fundamental matrix is algebraic representation of relation between two images taken from different camera position and orientation. It is often used to reconstruct 3D objects from images taken by uncalibrated cameras. On uncalibrated cameras, we have no information of focal length thus the reconstructed object is not matched to real world units. If the images are zoomed, the corresponding points in images are scaled and affect the result of reconstructed object. To avoid it, the focal length values of each image is required. During zooming process, it's impractical to do manual calibration of each frame. Self calibration could be used but it requires certain knowledge of the scene such as 3D parallel lines on the scene. This make difficult to know the focal length of image plane if the recorded scene is completely unknown. In this paper, we did focal length change estimation using information within fundamental matrix of stereo images. With this method we can reduce the process of point matching needed to reconstruct three dimensional objects. In order to solve the problem we tried to exploit the elements of fundamental matrix of stereo image pairs to obtain the estimate the value of focal length by using intrinsic parameters of minimal zoom as reference. In this paper, we managed to use fundamental matrix to estimate the changes of focal length relative to known focal length. The estimation result shows there is slight difference compared to estimation using points distance.
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