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Authors: Polycarpo Souza Neto ; Nicolas S. Pereira and George A. P. Thé

Affiliation: Department of Teleinformatic Engineering, Federal University of Ceara, Fortaleza, Pici campus, Bl 725, Zip code 60455-970 and Brazil

ISBN: 978-989-758-321-6

Keyword(s): Computer Vision, Iterative Closest Point, Point Cloud Registration, Point Cloud Sampling.

Related Ontology Subjects/Areas/Topics: Image Processing ; Informatics in Control, Automation and Robotics ; Robotics and Automation ; Vision, Recognition and Reconstruction

Abstract: In 3D reconstruction applications, an important issue is the matching of point clouds corresponding to different perspectives of a given object in a scene. Traditionally, this problem is solved by the use of the Iterative Closest point (ICP) algorithm. In view of improving the efficiency of this technique, authors recently proposed a preprocessing step which works prior to the ICP algorithm and leads to faster matching. In this work, we provide some improvements in our technique and compare it with other 4 variations of sampling methods using a RMSE metric, an Euler angles analysis and a modification structural similarity (SSIM) based metric. Our experiments have been carried out on four different models from two different databases, and revealed that our cloud partitioning approach achieved more accurate cloud matching, in shorter time than the other techniques. Finally we tested the robustness of the technique adding noise and occlusion, obtaining, as in the other tests, superior pe rformance. (More)

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Paper citation in several formats:
Souza Neto, P.; S. Pereira, N. and A. P. Thé, G. (2018). Improved Cloud Partitioning Sampling for Iterative Closest Point: Qualitative and Quantitative Comparison Study.In Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-321-6, pages 49-60. DOI: 10.5220/0006828500590070

@conference{icinco18,
author={Polycarpo Souza Neto. and Nicolas S. Pereira. and George A. P. Thé.},
title={Improved Cloud Partitioning Sampling for Iterative Closest Point: Qualitative and Quantitative Comparison Study},
booktitle={Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2018},
pages={49-60},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006828500590070},
isbn={978-989-758-321-6},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Improved Cloud Partitioning Sampling for Iterative Closest Point: Qualitative and Quantitative Comparison Study
SN - 978-989-758-321-6
AU - Souza Neto, P.
AU - S. Pereira, N.
AU - A. P. Thé, G.
PY - 2018
SP - 49
EP - 60
DO - 10.5220/0006828500590070

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