Noise-resistant Unsupervised Object Segmentation in Multi-view Indoor Point Clouds

Dmytro Bobkov, Sili Chen, Martin Kiechle, Sebastian Hilsenbeck, Eckehard Steinbach

2017

Abstract

3D object segmentation in indoor multi-view point clouds (MVPC) is challenged by a high noise level, varying point density and registration artifacts. This severely deteriorates the segmentation performance of state-of-the- art algorithms in concave and highly-curved point set neighborhoods, because concave regions normally serve as evidence for object boundaries. To address this issue, we derive a novel robust criterion to detect and remove such regions prior to segmentation so that noise modelling is not required anymore. Thus, a significant number of inter-object connections can be removed and the graph partitioning problem becomes simpler. After initial segmentation, such regions are labelled using a novel recovery procedure. Our approach has been experimentally validated within a typical segmentation pipeline on multi-view and single-view point cloud data. To foster further research, we make the labelled MVPC dataset public (Bobkov et al., 2017).

Paper Citation

in Harvard Style

Bobkov D., Chen S., Kiechle M., Hilsenbeck S. and Steinbach E. (2017). Noise-resistant Unsupervised Object Segmentation in Multi-view Indoor Point Clouds . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 149-156. DOI: 10.5220/0006100801490156

in Bibtex Style

@conference{visapp17,
author={Dmytro Bobkov and Sili Chen and Martin Kiechle and Sebastian Hilsenbeck and Eckehard Steinbach},
title={Noise-resistant Unsupervised Object Segmentation in Multi-view Indoor Point Clouds},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={149-156},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006100801490156},
isbn={978-989-758-226-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - Noise-resistant Unsupervised Object Segmentation in Multi-view Indoor Point Clouds
SN - 978-989-758-226-4
AU - Bobkov D.
AU - Chen S.
AU - Kiechle M.
AU - Hilsenbeck S.
AU - Steinbach E.
PY - 2017
SP - 149
EP - 156
DO - 10.5220/0006100801490156