GMS: Grid-Based Motion Statistics for Fast, Ultra-Robust Feature Correspondence
Jia-Wang Bian,Wen-Yan Lin,Yasuyuki Matsushita,Sai-Kit Yeung,Tan-Dat Nguyen,Ming-Ming Cheng +5 more
- pp 2828-2837
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TLDR
GMS (Grid-based Motion Statistics), a simple means of encapsulating motion smoothness as the statistical likelihood of a certain number of matches in a region, enables translation of high match numbers into high match quality.Abstract:
Incorporating smoothness constraints into feature matching is known to enable ultra-robust matching. However, such formulations are both complex and slow, making them unsuitable for video applications. This paper proposes GMS (Grid-based Motion Statistics), a simple means of encapsulating motion smoothness as the statistical likelihood of a certain number of matches in a region. GMS enables translation of high match numbers into high match quality. This provides a real-time, ultra-robust correspondence system. Evaluation on videos, with low textures, blurs and wide-baselines show GMS consistently out-performs other real-time matchers and can achieve parity with more sophisticated, much slower techniques.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.
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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.
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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.
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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.
Proceedings ArticleDOI
Learning to Find Good Correspondences
TL;DR: In this paper, a multi-layer perceptron operating on pixel coordinates rather than directly on the image is proposed to learn to find good correspondences for wide-baseline stereo.
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