Notice
This record has been edited as far as possible, missing data will be added when the version of record is issued.
Metadata only
Date
2023Type
- Conference Paper
ETH Bibliography
yes
Altmetrics
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 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 github.com/cvg/LightGlue. Show more
Publication status
publishedExternal links
Book title
2023 IEEE/CVF International Conference on Computer Vision (ICCV)Pages / Article No.
Event
Organisational unit
03766 - Pollefeys, Marc / Pollefeys, Marc
More
Show all metadata
ETH Bibliography
yes
Altmetrics