LGCNet: Feature Enhancement and Consistency Learning Based on Local and Global Coherence Network for Correspondence Selection

Tzu Han Wu, Kuan Wen Chen*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Correspondence selection, a crucial step in many computer vision tasks, aims to distinguish between inliers and outliers from putative correspondences. The coherence of correspondences is often used for predicting inlier probability, but it is difficult for neural networks to extract coherence contexts based only on quadruple coordinates. To overcome this difficulty, we propose enhancing the preliminary features using local and global handcrafted coherent characteristics before model learning, which strengthens the discrimination of each correspondence and guides the model to prune obvious outliers. Furthermore, to fully utilize local information, neighbors are searched in coordinate space as well as feature space. These two kinds of neighbors provide complementary and plentiful contexts for inlier probability prediction. Finally, a novel neighbor representation and a fusion architecture are proposed to retain detailed features. Experiments demonstrate that our method achieves state-of-the-art performance on relative camera pose estimation and correspondence selection metrics on the outdoor YFCC100M [1] and the indoor SUN3D [2] datasets.

Original languageEnglish
Title of host publicationProceedings - ICRA 2023
Subtitle of host publicationIEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6182-6188
Number of pages7
ISBN (Electronic)9798350323658
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Robotics and Automation, ICRA 2023 - London, United Kingdom
Duration: 29 May 20232 Jun 2023

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2023-May
ISSN (Print)1050-4729

Conference

Conference2023 IEEE International Conference on Robotics and Automation, ICRA 2023
Country/TerritoryUnited Kingdom
CityLondon
Period29/05/232/06/23

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