Investigating Keypoint Repeatability for 3D Correspondence Estimation in Cluttered Scenes

Chiem, QT; Wilkinson, RH; Lech, M; Cheng, E

Investigating Keypoint Repeatability for 3D Correspondence Estimation in Cluttered Scenes

Chiem, QT Wilkinson, RH Lech, M Cheng, E

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Publication Type:: Conference Proceeding
Citation:: DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications, 2017, 2017-December pp. 1 - 7
Issue Date:: 2017-12-19

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Field	Value	Language
dc.contributor.author	Chiem, QT	en_US
dc.contributor.author	Wilkinson, RH	en_US
dc.contributor.author	Lech, M	en_US
dc.contributor.author	Cheng, E https://orcid.org/0000-0003-1632-8062	en_US
dc.date.issued	2017-12-19	en_US
dc.identifier.citation	DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications, 2017, 2017-December pp. 1 - 7	en_US
dc.identifier.isbn	9781538628393	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126133
dc.description.abstract	© 2017 IEEE. In 3D object recognition, local feature-based recognition is known to be robust against occlusion and clutter. Local feature estimation requires feature correspondences, including feature extraction and matching. Feature extraction is normally a two-stage process that estimates keypoints and keypoint descriptors, and existing studies show repeatability to be a good indicator of keypoint feature detector robustness. However, the impact of keypoint repeatability on feature correspondence estimation and overall feature matching accuracy has not yet been studied. In this paper, local features are extracted at both regular and repeatable 3D keypoints using leading keypoint detectors combined with the SHOT descriptor to estimate a set of correspondences. When using a keypoint detector of high repeatability, experimental results show improved feature matching accuracy and reduced computational requirements for the feature description and matching, and overall correspondence estimation process.	en_US
dc.relation.ispartof	DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications	en_US
dc.relation.isbasedon	10.1109/DICTA.2017.8227449	en_US
dc.title	Investigating Keypoint Repeatability for 3D Correspondence Estimation in Cluttered Scenes	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2017-December	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Professional Practice and Leadership
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2017-December	en_US

Abstract:

© 2017 IEEE. In 3D object recognition, local feature-based recognition is known to be robust against occlusion and clutter. Local feature estimation requires feature correspondences, including feature extraction and matching. Feature extraction is normally a two-stage process that estimates keypoints and keypoint descriptors, and existing studies show repeatability to be a good indicator of keypoint feature detector robustness. However, the impact of keypoint repeatability on feature correspondence estimation and overall feature matching accuracy has not yet been studied. In this paper, local features are extracted at both regular and repeatable 3D keypoints using leading keypoint detectors combined with the SHOT descriptor to estimate a set of correspondences. When using a keypoint detector of high repeatability, experimental results show improved feature matching accuracy and reduced computational requirements for the feature description and matching, and overall correspondence estimation process.

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http://hdl.handle.net/10453/126133