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Aligning the Dissimilar: A Probabilistic Feature-Based Point Set Registration Approach
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0002-6096-3648
2016 (engelsk)Inngår i: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR) 2016, IEEE, 2016, s. 247-252Konferansepaper, Publicerat paper (Fagfellevurdert)
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 high-dimensional 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.

sted, utgiver, år, opplag, sider
IEEE, 2016. s. 247-252
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-137895DOI: 10.1109/ICPR.2016.7899641ISI: 000406771300044Scopus ID: 2-s2.0-85019098777ISBN: 9781509048472 (digital)ISBN: 9781509048489 (tryckt)OAI: oai:DiVA.org:liu-137895DiVA, id: diva2:1104306
Konferanse
23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico, 4-8 Dec. 2016
Merknad

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

Tilgjengelig fra: 2017-05-31 Laget: 2017-05-31 Sist oppdatert: 2019-10-31bibliografisk kontrollert

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