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基于运动深度约束的无姿势结构运动恢复。

Pose-free structure from motion using depth from motion constraints.

机构信息

Department of Mathematics, Purdue University, West Lafayette, IN 47906, USA.

出版信息

IEEE Trans Image Process. 2011 Oct;20(10):2937-53. doi: 10.1109/TIP.2011.2147322. Epub 2011 Apr 25.

Abstract

Structure from motion (SFM) is the problem of recovering the geometry of a scene from a stream of images taken from unknown viewpoints. One popular approach to estimate the geometry of a scene is to track scene features on several images and reconstruct their position in 3-D. During this process, the unknown camera pose must also be recovered. Unfortunately, recovering the pose can be an ill-conditioned problem which, in turn, can make the SFM problem difficult to solve accurately. We propose an alternative formulation of the SFM problem with fixed internal camera parameters known a priori. In this formulation, obtained by algebraic variable elimination, the external camera pose parameters do not appear. As a result, the problem is better conditioned in addition to involving much fewer variables. Variable elimination is done in three steps. First, we take the standard SFM equations in projective coordinates and eliminate the camera orientations from the equations. We then further eliminate the camera center positions. Finally, we also eliminate all 3-D point positions coordinates, except for their depths with respect to the camera center, thus obtaining a set of simple polynomial equations of degree two and three. We show that, when there are merely a few points and pictures, these "depth-only equations" can be solved in a global fashion using homotopy methods. We also show that, in general, these same equations can be used to formulate a pose-free cost function to refine SFM solutions in a way that is more accurate than by minimizing the total reprojection error, as done when using the bundle adjustment method. The generalization of our approach to the case of varying internal camera parameters is briefly discussed.

摘要

运动结构恢复(SFM)是从从未知视角拍摄的图像流中恢复场景几何结构的问题。一种流行的估计场景几何结构的方法是在几张图像上跟踪场景特征,并重建它们在 3D 中的位置。在这个过程中,还必须恢复未知的相机姿势。不幸的是,恢复姿势可能是一个病态问题,这反过来又使得 SFM 问题难以准确解决。我们提出了一种具有固定内部相机参数的 SFM 问题的替代公式,这些参数是先验已知的。在这种公式中,通过代数变量消除,外部相机姿势参数不会出现。因此,除了涉及的变量更少之外,问题的条件也更好。变量消除分三个步骤进行。首先,我们采用射影坐标下的标准 SFM 方程,并从方程中消除相机方向。然后,我们进一步消除相机中心位置。最后,我们还消除了所有 3D 点位置坐标,除了它们相对于相机中心的深度,从而得到一组简单的二次和三次多项式方程。我们表明,当仅有几个点和图片时,这些“仅深度方程”可以使用同伦方法以全局方式求解。我们还表明,通常,这些相同的方程可以用于制定无姿态成本函数,以比使用束调整方法最小化总重投影误差更准确的方式细化 SFM 解决方案。简要讨论了我们的方法对内部相机参数变化情况的推广。

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