swMATH ID: 42666
Software Authors: Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich
Description: SuperGlue: Learning Feature Matching with Graph Neural Networks. This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points. Assignments are estimated by solving a differentiable optimal transport problem, whose costs are predicted by a graph neural network. We introduce a flexible context aggregation mechanism based on attention, enabling SuperGlue to reason about the underlying 3D scene and feature assignments jointly. Compared to traditional, hand-designed heuristics, our technique learns priors over geometric transformations and regularities of the 3D world through end-to-end training from image pairs. SuperGlue outperforms other learned approaches and achieves state-of-the-art results on the task of pose estimation in challenging real-world indoor and outdoor environments. The proposed method performs matching in real-time on a modern GPU and can be readily integrated into modern SfM or SLAM systems. The code and trained weights are publicly available at https://github.com/magicleap/SuperGluePretrainedNetwork
Homepage: https://arxiv.org/abs/1911.11763
Source Code: https://github.com/magicleap/SuperGluePretrainedNetwork
Dependencies: Python
Related Software: D2-Net; SuperPoint; GMS; HPatches; ContextDesc; ASLFeat; LF-Net; MegaDepth; GANDissect; COTR; MatchFormer; InLoc; ACNe; LoFTR; Grad-CAM; MS-COCO; ScanNet; BRIEF; Patch2Pix; TopicFM
Cited in: 1 Publication

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