SuperGlue: Learning Feature Matching with Graph Neural Networks

Understand

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.