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Efficient Multi-Frequency Phase Unwrapping using Kernel Density Estimation

Felix Järemo Lawin, Per-Erik Forssén, Hannes Ovrén
ECCV16, Amsterdam
European Conference on Computer Vision (ECCV)
October 2016


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 18.75m, 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.

Full Paper

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Poster: PDF.
Supplemental material: PDF, video.
Dataset and code available on the dataset page.
Official proceedings at SpringerLink.

Bibtex entry

  author = 	 {Felix J\"aremo Lawin and Per-Erik Forss\'en and Hannes Ovr\'en},
  title = 	 {Efficient Multi-Frequency Phase Unwrapping using Kernel Density Estimation},
  booktitle =    {European Conference on Computer Vision ({ECCV})},
  year = 	 {2016},
  address = 	 {Amsterdam},
  month = 	 {October},
  publisher =    {Springer International Publishing AG},
  note =         {VR Projects: Learnable Camera Motion Models, 2014-5928, Energy Models for Computational Cameras, 2014-6227}

Per-Erik Forssén

Per-Erik Forssén


Computer Vision Laboratory
Department of Electrical Engineering
Building B
Room 2D:521
SE-581 83 Linköping, Sweden
+46(0)13 285654

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