Gaussian Curvature Criterion based Random Sample Matching for Improved 3D Registration

Faisal Azhar, Stephen Pollard, Guy Adams

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 Harvard Style

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, ISBN 978-989-758-354-4, pages 319-325. DOI: 10.5220/0007343403190325


in Bibtex Style

@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,},
year={2019},
pages={319-325},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007343403190325},
isbn={978-989-758-354-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: 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