Learning to Filter Outlier Edges in Global SfM

We present a novel approach to enhance camera pose 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 images, and edges represent relative poses (rotations and translations) between cameras. We reformulate the edge filtering problem as a vertex filtering in the dual graph, specifically, a line graph where vertices correspond to edges in the original graph and edges correspond to cameras. Utilizing this representation, we frame the problem as a binary classification over nodes in the dual graph. To identify outlier edges, we employ a Transformer-based architecture. To overcome the challenge of memory overflow caused by converting to a line graph, we introduce a clustering-based graph processing approach, enabling our method to be applied to arbitrarily large pose graphs. Our method outperforms existing relative translation filtering techniques in terms of camera position accuracy and can be seamlessly integrated with other filters. The code is available at https://github . com/DmblnNicole/LFOE-GlobalSfM .