Attention-based active 3D point cloud segmentation
2010
Conference Paper
am
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): | Autonome Motorik |
Bibtex Type: | Conference Paper (inproceedings) |
Paper Type: | Conference |
DOI: | 10.1109/IROS.2010.5649872 |
Attachments: |
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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}, doi = {10.1109/IROS.2010.5649872}, month_numeric = {10} } |