聚类辅助的基本矩阵估计
Clustering Assisted Fundamental Matrix Estimation
摘要：
在计算机视觉中，基本矩阵的估计是一个被广泛研究的基本问题。估计的准确性对后续的任务，如相机轨迹确定和3D重建施加了显着的影响。在本文中，我们提出了一种新的方法，利用聚类的一组4D向量的基本矩阵估计。关键的见解是观察到，在从SIFT算法获得的点的匹配对构造的4D向量中，定义良好的聚类点往往是适合于基本矩阵估计的可靠内点。在此基础上，我们利用最近提出的一种有效的聚类方法，通过密度峰值寻求，提出了一种新的聚类辅助方法。实验结果表明，该算法比目前常用的方法更快，更准确。
英文摘要：
In computer vision, the estimation of the fundamental matrix is a basic problem that has been extensively studied. The accuracy of the estimation imposes a significant influence on subsequent tasks such as the camera trajectory determination and 3D reconstruction. In this paper we propose a new method for fundamental matrix estimation that makes use of clustering a group of 4D vectors. The key insight is the observation that among the 4D vectors constructed from matching pairs of points obtained from the SIFT algorithm, well-defined cluster points tend to be reliable inliers suitable for fundamental matrix estimation. Based on this, we utilizes a recently proposed efficient clustering method through density peaks seeking and propose a new clustering assisted method. Experimental results show that the proposed algorithm is faster and more accurate than currently commonly used methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
