[CVPR 2020] SuperGlue: Learning Feature Matching with Graph Neural Networks (5min oral)

This is the 5 minute oral video for our CVPR 2020 paper:
"SuperGlue: Learning Feature Matching with Graph Neural Networks"
Project Page: https://psarlin.com/superglue
Paper: https://arxiv.org/abs/1911.11763
Code: https://github.com/magicleap/SuperGlu...
Authors: Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich

Abstract:
This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points. Assignments are estimated by solving a differentiable optimal transport problem, whose costs are predicted by a graph neural network. We introduce a flexible context aggregation mechanism based on attention, enabling SuperGlue to reason about the underlying 3D scene and feature assignments jointly. Compared to traditional, hand-designed heuristics, our technique learns priors over geometric transformations and regularities of the 3D world through end-to-end training from image pairs. SuperGlue outperforms other learned approaches and achieves state-of-the-art results on the task of pose estimation in challenging real-world indoor and outdoor environments. The proposed method performs matching in real-time on a modern GPU and can be readily integrated into modern SfM or SLAM systems.