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Camera self-calibration from unknown planar structures enforcing the multiview constraints between collineations

机译:来自未知平面结构的相机自校准,加强了归类之间的多视图约束

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In this paper, we describe an efficient method to impose the constraints existing between the collineations between images which can be computed from a sequence of views of a planar structure. These constraints are usually not taken into account by multiview techniques in order not to increase the computational complexity of the algorithms. However, imposing the constraints is very useful since it allows a reduction of geometric errors in the reprojected features and provides a consistent set of collineations which can be used for several applications such as mosaicing, reconstruction, and self-calibration. In order to show the validity of our approach, this paper focus on self-calibration from unknown planar structures proposing a method exploiting the consistent set of collineations. Our method can deal with an arbitrary number of views and an arbitrary number of planes and varying camera internal parameters. However, for simplicity, this papers will only discuss the case with one plane in several views. The results obtained with synthetic and real data are very accurate and stable even when using only few images.
机译:在本文中,我们描述了一种有效的方法,可以在图像之间的排序之间施加约束,可以从平面结构的视图序列中计算出这些约束。为了不增加算法的计算复杂度,多视图技术通常不考虑这些约束。但是,施加约束非常有用,因为它可以减少重新投影的特征中的几何误差,并提供一致的校准集,这些校准集可用于多种应用程序,例如镶嵌,重建和自校准。为了证明我们的方法的有效性,本文着重于对未知平面结构的自校准,提出了一种利用一致的校准集的方法。我们的方法可以处理任意数量的视图和任意数量的平面以及变化的相机内部参数。但是,为简单起见,本文仅以几种观点讨论一个平面的情况。即使仅使用少量图像,使用合成和真实数据获得的结果也非常准确和稳定。

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