SuperGlue The SuperGlue model was proposed in SuperGlue: Learning Feature Matching with Graph Neural Networks by Paul Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich. This model consists of matching two sets of interest points detected in an image. Paired with the SuperPoint model, it can be used to match two images and estimate the pose between them. This model is useful for tasks such as image matching, homography estimation, etc. The abstract from the paper is the following: 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 approac…
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