  This work addresses the outlier removal problem in large-scale global
structure-from-motion. In such applications, global outlier removal is very
useful to mitigate the deterioration caused by mismatches in the feature point
matching step. Unlike existing outlier removal methods, we exploit the
structure in multiview geometry problems to propose a dimension reduced
formulation, based on which two methods have been developed. The first method
considers a convex relaxed $\ell_1$ minimization and is solved by a single
linear programming (LP), whilst the second one approximately solves the ideal
$\ell_0$ minimization by an iteratively reweighted method. The dimension
reduction results in a significant speedup of the new algorithms. Further, the
iteratively reweighted method can significantly reduce the possibility of
removing true inliers. Realistic multiview reconstruction experiments
demonstrated that, compared with state-of-the-art algorithms, the new
algorithms 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}.
