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Authors: Faisal Azhar ; Stephen Pollard and Guy Adams

Affiliation: HP Labs, Bristol and U.K.

ISBN: 978-989-758-354-4

Keyword(s): Gaussian Curvature, 3D Registration, Matching, Point Cloud, Hash Table.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Features Extraction ; Image and Video Analysis ; Pattern Recognition ; Robotics ; Shape Representation and Matching ; Software Engineering

Abstract: We propose a novel Gaussian Curvature (GC) based criterion to discard false point correspondences within the RANdom SAmple Matching (RANSAM) framework to improve the 3D registration. The RANSAM method is used to find a point pair correspondence match between two surfaces and GC is used to verify whether this point pair is a correct (similar curvatures) or false (dissimilar curvatures) match. The point pairs which pass the curvature test are used to compute a transformation which aligns the two overlapping surfaces. The results on shape alignment benchmarks show improved accuracy of the GRANSAM versus RANSAM and six other registration methods while maintaining efficiency.

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Paper citation in several formats:
Azhar, F.; Pollard, S. and Adams, G. (2019). Gaussian Curvature Criterion based Random Sample Matching for Improved 3D Registration.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP, ISBN 978-989-758-354-4, pages 319-325. DOI: 10.5220/0007343403190325

@conference{visapp19,
author={Faisal Azhar. and Stephen Pollard. and Guy Adams.},
title={Gaussian Curvature Criterion based Random Sample Matching for Improved 3D Registration},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP,},
year={2019},
pages={319-325},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007343403190325},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP,
TI - Gaussian Curvature Criterion based Random Sample Matching for Improved 3D Registration
SN - 978-989-758-354-4
AU - Azhar, F.
AU - Pollard, S.
AU - Adams, G.
PY - 2019
SP - 319
EP - 325
DO - 10.5220/0007343403190325

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