Scalable Cluster-Consistency Statistics for Robust Multi-Object Matching
Yunpeng Shi, Shaohan Li, Tyler Maunu and Gilad Lerman
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.