匹配点分布密度约束下的基础矩阵估计

Fundamental Matrix Estimation Based on Inlier Distributions Constraint

  • 摘要: 提出一种匹配点分布密度约束下的基础矩阵估计方法。该方法以传统RANSAC方法为基本框架,结合匹配点分布密度约束来选择内点集,并采用M-Estimators方法重新计算基础矩阵。通过模拟数据和真实图像实验表明,本文方法可有效提高基础矩阵的计算精度。

     

    Abstract: This paper presents a novel approach to estimate the fundamental matrix. The proposed method takes the traditional RANSAC method as the basic framework and selects the optimum inlier set combined with the inliers distribution and the average distance of the points to the epipolar lines. Then the M-Estimators method is used to compute the fundamental matrix. Experiment on a large number of simulated and real image data show that our algorithm can achieve a more precise estimation of the fundamental matrix.

     

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