Learning to Filter Outlier Edges in Global SfM

This paper introduces a novel approach to improve camera position estimation in global Structure-from-Motion (SfM) frameworks by filtering inaccurate pose graph edges, representing relative translation estimates, before applying translation averaging. In SfM, pose graph vertices represent cameras and edges relative poses (rotation and translation) between cameras. We formulate the edge filtering problem as a vertex filtering in the dual graph - a line graph where the vertices stem from edges in the original graph, and the edges from cameras. Exploiting such a representation, we frame the problem as a binary classification over nodes in the dual graph. To learn such a classification and find outlier edges, we employ a Transformer architecture-based technique. To address the challenge of memory overflow often caused by converting to a line graph, we introduce a clustering-based graph processing approach, enabling the application of our method to arbitrarily large pose graphs. The proposed method outperforms existing relative translation filtering techniques in terms of final camera position accuracy and can be seamlessly integrated with any other filter. The code will be made public.