Dual-context Identification based on Geometric Descriptors for 3D Registration Algorithm Selection

Polycarpo Neto, José Soares, Michela Mulas, George Thé

2021

Abstract

In 3D reconstruction applications, matching between corresponding point clouds is commonly resolved using variants of the Iterative Closest Point (ICP). However, ICP and its variants suffer from some limitations, functioning properly only for some contexts with well-behaved data distribution; outdoor scene, for example, poses many challenges. Indeed, the literature has suggested that the ability of some of these algorithms to find a match was reduced by the presence of geometric disorder in the scene, for example. This article presents a method based on the characterization of the eigentropy and omnivariance properties of clouds to indicate which variant of the ICP is best suited for each context considered here, namely, object or outdoor scene alignment. In addition to the context selector, we suggest a partitioning step prior to alignment, which in most cases allows for reduced computational cost. In summary, the proposal as a whole worked satisfactorily to the alignment as a multipurpose registration technique, serving to pose correction of data from different contexts and thus being useful for computer vision and robotics applications.

Paper Citation

in Harvard Style

Neto P., Soares J., Mulas M. and Thé G. (2021). Dual-context Identification based on Geometric Descriptors for 3D Registration Algorithm Selection. In Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - Volume 1: ROBOVIS, ISBN 978-989-758-537-1, pages 150-157. DOI: 10.5220/0010712400003061

in Bibtex Style

@conference{robovis21,
author={Polycarpo Neto and José Soares and Michela Mulas and George Thé},
title={Dual-context Identification based on Geometric Descriptors for 3D Registration Algorithm Selection},
booktitle={Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - Volume 1: ROBOVIS,},
year={2021},
pages={150-157},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010712400003061},
isbn={978-989-758-537-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - Volume 1: ROBOVIS,
TI - Dual-context Identification based on Geometric Descriptors for 3D Registration Algorithm Selection
SN - 978-989-758-537-1
AU - Neto P.
AU - Soares J.
AU - Mulas M.
AU - Thé G.
PY - 2021
SP - 150
EP - 157
DO - 10.5220/0010712400003061