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Attention-based active 3D point cloud segmentation

2010

Conference Paper

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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.

Author(s): Johnson-Roberson, M. and Bohg, J. and Björkman, M. and Kragic, D.
Book Title: Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on
Pages: 1165-1170
Year: 2010
Month: October

Department(s): Autonomous Motion
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

DOI: 10.1109/IROS.2010.5649872
Attachments: pdf

BibTex

@inproceedings{5649872,
  title = {Attention-based active 3D point cloud segmentation},
  author = {Johnson-Roberson, M. and Bohg, J. and Bj{\"o}rkman, M. and Kragic, D.},
  booktitle = {Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on},
  pages = {1165-1170},
  month = oct,
  year = {2010},
  month_numeric = {10}
}