Authors:
Dmytro Bobkov
1
;
Sili Chen
2
;
Martin Kiechle
1
;
Sebastian Hilsenbeck
3
and
Eckehard Steinbach
1
Affiliations:
1
Technical University of Munich, Germany
;
2
Baidu Inc., China
;
3
NavVis GmbH, Germany
Keyword(s):
Object Segmentation, Concavity Criterion, Laser Scanner, Point Cloud, Segmentation Dataset.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Pattern Recognition
;
Robotics
;
Segmentation and Grouping
;
Shape Representation and Matching
;
Software Engineering
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).