论文标题
来自筛分功能的相对姿势
Relative Pose from SIFT Features
论文作者
论文摘要
本文提出了异性几何形状和方向和比例融合的几何关系，例如筛分，特征。我们得出了一个新的线性约束，该约束将基本矩阵的未知元素以及方向和规模。该方程可与众所周知的表皮约束一起使用，例如，从四个筛分对应关系（三个筛子矩阵）估算了基本矩阵，并从三个对应关系中求解了半校准的情况。与众所周知的基于点的方法（例如5pt，6pt和7pt求解器）所需的对应关系更少，因此，与lansac样的随机稳健估计值明显更快。在合成环境中的许多问题以及在80000多个图像对上的公开现实世界数据集中测试了所提出的约束。就处理时间而言，它比最先进的，同时通常会导致更准确的结果。
This paper proposes the geometric relationship of epipolar geometry and orientation- and scale-covariant, e.g., SIFT, features. We derive a new linear constraint relating the unknown elements of the fundamental matrix and the orientation and scale. This equation can be used together with the well-known epipolar constraint to, e.g., estimate the fundamental matrix from four SIFT correspondences, essential matrix from three, and to solve the semi-calibrated case from three correspondences. Requiring fewer correspondences than the well-known point-based approaches (e.g., 5PT, 6PT and 7PT solvers) for epipolar geometry estimation makes RANSAC-like randomized robust estimation significantly faster. The proposed constraint is tested on a number of problems in a synthetic environment and on publicly available real-world datasets on more than 80000 image pairs. It is superior to the state-of-the-art in terms of processing time while often leading to more accurate results.
