magicleap/SuperGluePretrainedNetwork
SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
Combines Graph Neural Networks with an Optimal Matching layer to perform context-aware feature matching across image pairs, operating as an end-to-end "middle-end" that aggregates spatial context before matching. Provides pretrained indoor (ScanNet) and outdoor (MegaDepth) models that work on top of SuperPoint keypoints, with PyTorch inference code and evaluation utilities including RANSAC-based pose estimation and matching confidence scoring. Integrates with the hloc visual localization toolbox and supports live webcam/video streams, image sequences, and batch pair processing for both qualitative visualization and quantitative evaluation against ground truth poses.
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