magicleap/SuperGluePretrainedNetwork

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

4072 stars · 758 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

SuperGlue is a PyTorch implementation of a graph neural network with an optimal matching layer that matches sparse image features between two images, released with pretrained indoor and outdoor weights. It runs on top of SuperPoint keypoints and descriptors and includes demo and evaluation scripts for image pairs and live video streams.

Use cases

• match features between two images
• estimate relative pose from image pairs
• run feature matching on webcam or video streams
• build visual localization or SfM pipelines
• match SuperPoint keypoints with a learned matcher

When to choose

• you need state-of-the-art sparse feature matching for localization, SfM, or pose estimation
• you want pretrained indoor and outdoor matching models ready to run in PyTorch

When to avoid

• you need dense pixel-level matching rather than sparse keypoint matching
• you cannot use a GPU or PyTorch in your environment
• you need a permissively licensed library for commercial products (license is non-standard)

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning artificial-intelligence python cross-platform feature-matching graph-neural-networks superpoint pose-estimation pytorch pretrained-weights visual-localization slam gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="magicleap/SuperGluePretrainedNetwork")