SuperPoint: Self-Supervised Interest Point Detection and Description
Daniel DeTone,Tomasz Malisiewicz,Andrew Rabinovich +2 more
- pp 337-33712
TLDR
In this paper, a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision is presented, which operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass.Abstract:
This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches when compared to LIFT, SIFT and ORB.read more
Citations
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Proceedings ArticleDOI
SuperGlue: Learning Feature Matching With Graph Neural Networks
TL;DR: SuperGlue is introduced, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points and introduces a flexible context aggregation mechanism based on attention, enabling SuperGlue to reason about the underlying 3D scene and feature assignments jointly.
Posted Content
SuperGlue: Learning Feature Matching with Graph Neural Networks
TL;DR: SuperGlue as discussed by the authors matches two sets of local features by jointly finding correspondences and rejecting non-matchable points by solving a differentiable optimal transport problem, whose costs are predicted by a graph neural network.
Proceedings ArticleDOI
D2-Net: A Trainable CNN for Joint Description and Detection of Local Features
Mihai Dusmanu,Ignacio Rocco,Tomas Pajdla,Marc Pollefeys,Josef Sivic,Akihiko Torii,Torsten Sattler +6 more
TL;DR: This work proposes an approach where a single convolutional neural network plays a dual role: It is simultaneously a dense feature descriptor and a feature detector, and shows that this model can be trained using pixel correspondences extracted from readily available large-scale SfM reconstructions, without any further annotations.
Journal ArticleDOI
Image Matching from Handcrafted to Deep Features: A Survey
TL;DR: This survey introduces feature detection, description, and matching techniques from handcrafted methods to trainable ones and provides an analysis of the development of these methods in theory and practice, and briefly introduces several typical image matching-based applications.
Proceedings ArticleDOI
LoFTR: Detector-Free Local Feature Matching with Transformers
TL;DR: LoFTR as discussed by the authors uses self and cross attention layers in Transformer to obtain feature descriptors that are conditioned on both images, which enables the method to produce dense matches in low-texture areas.
References
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
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Multiple view geometry in computer vision
Richard Hartley,Andrew Zisserman +1 more
TL;DR: In this article, the authors provide comprehensive background material and explain how to apply the methods and implement the algorithms directly in a unified framework, including geometric principles and how to represent objects algebraically so they can be computed and applied.
Proceedings ArticleDOI
A Combined Corner and Edge Detector
Chris Harris,Mike Stephens +1 more
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