DocumentCode :
3306137
Title :
Attention-based active 3D point cloud segmentation
Author :
Johnson-Roberson, Matthew ; Bohg, Jeannette ; Björkman, Mårten ; Kragic, Danica
Author_Institution :
Centre for Autonomous Syst. & Comput. Vision & active Perception Lab., CSC-KTH, Stockholm, Sweden
fYear :
2010
fDate :
18-22 Oct. 2010
Firstpage :
1165
Lastpage :
1170
Abstract :
In this paper we present a framework for the segmentation of multiple objects from a 3D point cloud. We extend traditional image segmentation techniques into a full 3D representation. The proposed technique relies on a state-of-the-art min-cut framework to perform a fully 3D global multi-class labeling in a principled manner. Thereby, we extend our previous work in which a single object was actively segmented from the background. We also examine several seeding methods to bootstrap the graphical model-based energy minimization and these methods are compared over challenging scenes. All results are generated on real-world data gathered with an active vision robotic head. We present quantitive results over aggregate sets as well as visual results on specific examples.
Keywords :
image representation; image segmentation; robot vision; statistical analysis; 3D representation; 3d point cloud; active vision robotic; bootstrap; graphical model; object segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on
Conference_Location :
Taipei
ISSN :
2153-0858
Print_ISBN :
978-1-4244-6674-0
Type :
conf
DOI :
10.1109/IROS.2010.5649872
Filename :
5649872
Link To Document :
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