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dc.contributor.authorGiancola, Silvio
dc.contributor.authorSchneider, Jens
dc.contributor.authorWonka, Peter
dc.contributor.authorGhanem, Bernard
dc.date.accessioned2019-02-18T06:26:19Z
dc.date.available2018-02-22T10:34:41Z
dc.date.available2019-02-18T06:26:19Z
dc.date.issued2018-12-18
dc.identifier.citationGiancola S, Schneider J, Wonka P, Ghanem BS (2018) Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction Pipeline. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Available: http://dx.doi.org/10.1109/CVPRW.2018.00198.
dc.identifier.doi10.1109/CVPRW.2018.00198
dc.identifier.urihttp://hdl.handle.net/10754/627172
dc.description.abstractIn this paper, we show how absolute orientation measurements provided by low-cost but high-fidelity IMU sensors can be integrated into the KinectFusion pipeline. We show that integration improves both runtime, robustness and quality of the 3D reconstruction. In particular, we use this orientation data to seed and regularize the ICP registration technique. We also present a technique to filter the pairs of 3D matched points based on the distribution of their distances. This filter is implemented efficiently on the GPU. Estimating the distribution of the distances helps control the number of iterations necessary for the convergence of the ICP algorithm. Finally, we show experimental results that highlight improvements in robustness, a speed-up of almost 12%, and a gain in tracking quality of 53% for the ATE metric on the Freiburg benchmark.
dc.description.sponsorshipThis work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research and the Visual Computing Center (VCC).
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8575358
dc.rightsArchived with thanks to 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
dc.titleIntegration of Absolute Orientation Measurements in the KinectFusion Reconstruction Pipeline
dc.typeConference Paper
dc.contributor.departmentCompetitive Research Funds
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.identifier.journal2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
dc.conference.date2018-06-18 to 2018-06-22
dc.conference.name31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
dc.conference.locationSalt Lake City, UT, USA
dc.eprint.versionPost-print
dc.identifier.arxividarXiv:1802.03980
kaust.personGiancola, Silvio
kaust.personSchneider, Jens
kaust.personWonka, Peter
kaust.personGhanem, Bernard
refterms.dateFOA2018-06-14T03:52:31Z
dc.date.published-online2018-12-18
dc.date.published-print2018-06
dc.date.posted2018-02-12


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