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

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

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).

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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