  We develop new statistics for robustly filtering corrupted keypoint matches
in the structure from motion pipeline. The statistics are based on consistency
constraints that arise within the clustered structure of the graph of keypoint
matches. The statistics are designed to give smaller values to corrupted
matches and than uncorrupted matches. These new statistics are combined with an
iterative reweighting scheme to filter keypoints, which can then be fed into
any standard structure from motion pipeline. This filtering method can be
efficiently implemented and scaled to massive datasets as it only requires
sparse matrix multiplication. We demonstrate the efficacy of this method on
synthetic and real structure from motion datasets and show that it achieves
state-of-the-art accuracy and speed in these tasks.
