Improved Cloud Partitioning Sampling for Iterative Closest Point: Qualitative and Quantitative Comparison Study

Polycarpo Souza Neto, Nicolas S. Pereira, George A. P. Thé

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 performance.

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Paper Citation


in Harvard Style

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 2: ICINCO, ISBN 978-989-758-321-6, pages 49-60. DOI: 10.5220/0006828500490060


in Bibtex Style

@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 2: ICINCO,},
year={2018},
pages={49-60},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006828500490060},
isbn={978-989-758-321-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 2: 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 - 10.5220/0006828500490060