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Density Adaptive 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: 0000-0002-5698-5983
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2018 (English)In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, p. 3829-3837Conference paper, Published paper (Refereed)
Abstract [en]

Probabilistic methods for point set registration have demonstrated competitive results in recent years. These techniques estimate a probability distribution model of the point clouds. While such a representation has shown promise, it is highly sensitive to variations in the density of 3D points. This fundamental problem is primarily caused by changes in the sensor location across point sets.    We revisit the foundations of the probabilistic registration paradigm. Contrary to previous works, we model the underlying structure of the scene as a latent probability distribution, and thereby induce invariance to point set density changes. Both the probabilistic model of the scene and the registration parameters are inferred by minimizing the Kullback-Leibler divergence in an Expectation Maximization based framework. Our density-adaptive registration successfully handles severe density variations commonly encountered in terrestrial Lidar applications. We perform extensive experiments on several challenging real-world Lidar datasets. The results demonstrate that our approach outperforms state-of-the-art probabilistic methods for multi-view registration, without the need of re-sampling.

Place, publisher, year, edition, pages
IEEE, 2018. p. 3829-3837
Series
IEEE Conference on Computer Vision and Pattern Recognition
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-149774DOI: 10.1109/CVPR.2018.00403ISI: 000457843603101ISBN: 978-1-5386-6420-9 (electronic)OAI: oai:DiVA.org:liu-149774DiVA, id: diva2:1233671
Conference
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, United States, 18-22 June, 2018
Note

Funding Agencies|EUs Horizon 2020 Programme [644839]; CENIIT grant [18.14]; VR grant: EMC2 [2014-6227]; VR grant [2016-05543]; VR grant: LCMM [2014-5928]

Available from: 2018-07-18 Created: 2018-07-18 Last updated: 2020-02-03Bibliographically approved

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Density Adaptive Point Set Registration(1340 kB)109 downloads
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Järemo Lawin, FelixDanelljan, MartinKhan, Fahad ShahbazForssén, Per-ErikFelsberg, Michael

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Järemo Lawin, FelixDanelljan, MartinKhan, Fahad ShahbazForssén, Per-ErikFelsberg, Michael
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