Local Reflectional Symmetry Detection in Point Clouds Using a Simple PCA-Based Shape Descriptor

Lukáš Hruda, Ivana Kolingerová, David Podgorelec

2023

Abstract

Symmetry is a commonly occurring feature in real world objects and its knowledge can be useful in various applications. Different types of symmetries exist but we only consider the reflectional symmetry which is probably the most common one. Multiple methods exist that aim to find the global reflectional symmetry of a given 3D object and although this task on its own is not easy, finding symmetries of objects that are merely parts of larger scenes is much more difficult. Such symmetries are often called local symmetries and they commonly occur in real world 3D scans of whole scenes or larger areas. In this paper we propose a simple PCA-based local shape descriptor that can be easily used for potential symmetric point matching in 3D point clouds and, building on previous work, we present a new method for detecting local reflectional symmetries in 3D point clouds which combines the PCA descriptor point matching with the density peak location algorithm. We show the results of our method for several real 3D scanned scenes and demonstrate its computational efficiency and robustness to noise.

Paper Citation

in Harvard Style

Hruda L., Kolingerová I. and Podgorelec D. (2023). Local Reflectional Symmetry Detection in Point Clouds Using a Simple PCA-Based Shape Descriptor. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 1: GRAPP; ISBN 978-989-758-634-7, SciTePress, pages 52-63. DOI: 10.5220/0011622200003417

in Bibtex Style

@conference{grapp23,
author={Lukáš Hruda and Ivana Kolingerová and David Podgorelec},
title={Local Reflectional Symmetry Detection in Point Clouds Using a Simple PCA-Based Shape Descriptor},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 1: GRAPP},
year={2023},
pages={52-63},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011622200003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 1: GRAPP
TI - Local Reflectional Symmetry Detection in Point Clouds Using a Simple PCA-Based Shape Descriptor
SN - 978-989-758-634-7
AU - Hruda L.
AU - Kolingerová I.
AU - Podgorelec D.
PY - 2023
SP - 52
EP - 63
DO - 10.5220/0011622200003417
PB - SciTePress