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This work addresses the outlier removal problem in large-scale globalstructure-from-motion. In such applications, global outlier removal is veryuseful to mitigate the deterioration caused by mismatches in the feature pointmatching step. Unlike existing outlier removal methods, we exploit thestructure in multiview geometry problems to propose a dimension reducedformulation, based on which two methods have been developed. The first methodconsiders a convex relaxed $\ell_1$ minimization and is solved by a singlelinear programming (LP), whilst the second one approximately solves the ideal$\ell_0$ minimization by an iteratively reweighted method. The dimensionreduction results in a significant speedup of the new algorithms. Further, theiteratively reweighted method can significantly reduce the possibility ofremoving true inliers. Realistic multiview reconstruction experimentsdemonstrated that, compared with state-of-the-art algorithms, the newalgorithms are much more efficient and meanwhile can give improved solution.Matlab code for reproducing the results is available at\textit{https://github.com/FWen/OUTLR.git}.

Efficient Outlier Removal in Large Scale Global Structure-from-Motion