Detection and Classification of Holes in Point Clouds

Nader H. Aldeeb, Olaf Hellwich

2017

Abstract

Structure from Motion (SfM) is the most popular technique behind 3D image reconstruction. It is mainly based on matching features between multiple views of the target object. Therefore, it gives good results only if the target object has enough texture on its surface. If not, virtual holes are caused in the estimated models. But, not all holes that appear in the estimated model are virtual, i.e. correspond to a failure of the reconstruction. There could be a real physical hole in the structure of the target object being reconstructed. This presents ambiguity when applying a hole-filling algorithm. That is, which hole should be filled and which must be left as it is. In this paper, we first propose a simple approach for the detection of holes in point sets. Then we investigate two different measures for automatic classification of these detected holes in point sets. According to our knowledge, hole-classification has not been addressed beforehand. Experiments showed that all holes in 3D models are accurately identified and classified.

Paper Citation

in Harvard Style

Aldeeb N. and Hellwich O. (2017). Detection and Classification of Holes in Point Clouds . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-227-1, pages 321-330. DOI: 10.5220/0006296503210330

in Bibtex Style

@conference{visapp17,
author={Nader H. Aldeeb and Olaf Hellwich},
title={Detection and Classification of Holes in Point Clouds},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={321-330},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006296503210330},
isbn={978-989-758-227-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)
TI - Detection and Classification of Holes in Point Clouds
SN - 978-989-758-227-1
AU - Aldeeb N.
AU - Hellwich O.
PY - 2017
SP - 321
EP - 330
DO - 10.5220/0006296503210330