Attention-based Active 3D Point Cloud Segmentation
2010 (English)In: IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), 2010, 1165-1170 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
2010. 1165-1170 p.
, IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
GRAPH CUTS, ENERGY MINIMIZATION, VISION
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-32009DOI: 10.1109/IROS.2010.5649872ISI: 000287672003158ScopusID: 2-s2.0-78651512765ISBN: 978-1-4244-6675-7OAI: oai:DiVA.org:kth-32009DiVA: diva2:409085
IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, TAIWAN, OCT 18-22, 2010
QC 201104072011-04-072011-04-042012-01-28Bibliographically approved