LightGlue: Local Feature Matching at Light Speed

By: Philipp Lindenberger, Paul-Edouard Sarlin, Marc Pollefeys, ETH Zurich, Microsoft Mixed Reality & AI Lab

https://github.com/cvg/LightGlue

Abstract:

We introduce LightGlue, a deep neural network that learns to match local features across images. We revisit multiple design decisions of SuperGlue, the state of the art in sparse matching, and derive simple but effective improve- ments. Cumulatively, they make LightGlue more efficient – in terms of both memory and computation, more accurate, and much easier to train. One key property is that …

abstract art computervision decisions deep neural network design eth zurich feature features images lab light microsoft mixed mixed reality multiple network neural network reality speed state state of the art zurich