Ilia Shipachev presents SuperGlue: Learning Feature Matching with Graph Neural Networks

On 2020-12-15 11:00:00 at
Online reading group on the work "SuperGlue: Learning Feature Matching with
Graph Neural Networks", Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz,
Andrew Rabinovich, CVPR 2020.

Paper abstract: 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.

Paper URL:

Instructions for participants: The reading group studies the literature in the
field of pattern recognition and computer vision. At each meeting one or more
papers are prepared for presentation by a single person, the presenter. The
meetings are open to anyone, disregarding their background. It is assumed that
everyone attending the reading group has, at least briefly, read the paper –
not necessarily understanding everything. Attendants should preferably send
questions about the unclear parts to the speaker at least one day in advance.
During the presentation we aim to have a fruitful discussion, a critical
analysis of the paper, as well as brainstorming for creative extensions.

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Responsible person: Petr Pošík