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Aligning the Dissimilar: A Probabilistic Method for Feature-Based Point Set Registration
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6096-3648
2016 (English)In: 2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), IEEE COMPUTER SOC , 2016, p. 247-252Conference paper, Published paper (Refereed)
Abstract [en]

3D-point set registration is an active area of research in computer vision. In recent years, probabilistic registration approaches have demonstrated superior performance for many challenging applications. Generally, these probabilistic approaches rely on the spatial distribution of the 3D-points, and only recently color information has been integrated into such a framework, significantly improving registration accuracy. Other than local color information, high-dimensional 3D shape features have been successfully employed in many applications such as action recognition and 3D object recognition. In this paper, we propose a probabilistic framework to integrate highdimensional 3D shape features with color information for point set registration. The 3D shape features are distinctive and provide complementary information beneficial for robust registration. We validate our proposed framework by performing comprehensive experiments on the challenging Stanford Lounge dataset, acquired by a RGB-D sensor, and an outdoor dataset captured by a Lidar sensor. The results clearly demonstrate that our approach provides superior results both in terms of robustness and accuracy compared to state-of-the-art probabilistic methods.

Place, publisher, year, edition, pages
IEEE COMPUTER SOC , 2016. p. 247-252
Series
International Conference on Pattern Recognition, ISSN 1051-4651
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-141746DOI: 10.1109/ICPR.2016.7899641ISI: 000406771300044ISBN: 978-1-5090-4847-2 (print)OAI: oai:DiVA.org:liu-141746DiVA: diva2:1147265
Conference
23rd International Conference on Pattern Recognition (ICPR)
Note

Funding Agencies|SSF (VPS); VR (EMC2); Vinnova (iQMatic); EUs Horizon RI program grant [644839]; Wallenberg Autonomous Systems Program; NSC; Nvidia

Available from: 2017-10-05 Created: 2017-10-05 Last updated: 2018-01-13

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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  • Other style
More styles
Language
  • de-DE
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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