LightGlue: Local Feature Matching at Light Speed
Philipp Lindenberger Paul-Edouard Sarlin Marc Pollefeys
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
improvements. 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 LightGlue is adaptive to the difficulty of the problem: the
inference is much faster on image pairs that are intuitively easy to match, for
example because of a larger visual overlap or limited appearance change. This
opens up exciting prospects for deploying deep matchers in latency-sensitive
applications like 3D reconstruction. The code and trained models are publicly
available at https://github.com/cvg/LightGlue.