CVPR 2023 - IMP: Iterative Matching and Pose Estimation with Adaptive Pooling

In this episode we discuss IMP: Iterative Matching and Pose Estimation with Adaptive Pooling by Fei Xue, Ignas Budvytis, Roberto Cipolla. The paper proposes an iterative matching and pose estimation framework (IMP) that leverages the geometric connections between the two tasks. They introduce a geometry-aware recurrent attention-based module which jointly outputs sparse matches and camera poses. They also introduce an efficient version of IMP called EIMP, that dynamically discards keypoints without potential matches, reducing the quadratic time complexity of attention computation. The proposed method outperforms previous approaches in terms of accuracy and efficiency on YFCC100m, Scannet, and Aachen Day-Night datasets.