liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Efficient Multi-frequency Phase Unwrapping Using Kernel Density Estimation
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
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
2016 (English)In: Computer Vision - ECCV 2016, Pt IV, SPRINGER INT PUBLISHING AG , 2016, Vol. 9908, 170-185 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we introduce an efficient method to unwrap multi-frequency phase estimates for time-of-flight ranging. The algorithm generates multiple depth hypotheses and uses a spatial kernel density estimate (KDE) to rank them. The confidence produced by the KDE is also an effective means to detect outliers. We also introduce a new closed-form expression for phase noise prediction, that better fits real data. The method is applied to depth decoding for the Kinect v2 sensor, and compared to the Microsoft Kinect SDK and to the open source driver libfreenect2. The intended Kinect v2 use case is scenes with less than 8m range, and for such cases we observe consistent improvements, while maintaining real-time performance. When extending the depth range to the maximal value of 18.75 m, we get about 52% more valid measurements than libfreenect2. The effect is that the sensor can now be used in large depth scenes, where it was previously not a good choice.

Place, publisher, year, edition, pages
SPRINGER INT PUBLISHING AG , 2016. Vol. 9908, 170-185 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743
Keyword [en]
Time-of-flight; Kinect v2; Kernel-density-estimation
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-133549DOI: 10.1007/978-3-319-46493-0_11ISI: 000389385100011ISBN: 978-3-319-46493-0; 978-3-319-46492-3 (print)OAI: oai:DiVA.org:liu-133549DiVA: diva2:1060849
Conference
14th European Conference on Computer Vision (ECCV)
Available from: 2016-12-30 Created: 2016-12-29 Last updated: 2016-12-30

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Järemo-Lawin, FelixForssén, Per-ErikOvrén, Hannes
By organisation
Computer VisionFaculty of Science & Engineering
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 279 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf