Computer Vision – ECCV 2016
European Conference on Computer Vision
ECCV 2016: Computer Vision – ECCV 2016 pp 170-185 | Cite as
Efficient Multi-frequency Phase Unwrapping Using Kernel Density Estimation
Felix Järemo Lawin
Per-Erik Forssén
Hannes Ovrén
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 8 m 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.
Time-of-flight Kinect v2 Kernel-density-estimation
The online version of this chapter (doi: 10.1007/978-3-319-46493-0_11) contains supplementary material, which is available to authorized users.
This work has been supported by the Swedish Research Council in projects 2014-6227 (EMC2) and 2014-5928 (LCMM) and the EU's Horizon 2020 Programme grant No 644839 (CENTAURO).
Supplementary material 1 (mp4 12608 KB)
419976_1_En_11_MOESM2_ESM.pdf (15 mb)
Supplementary material 2 (pdf 15332 KB)
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© Springer International Publishing AG 2016
1.Computer Vision LaboratoryLinköping UniversityLinköpingSweden
Järemo Lawin F., Forssén PE., Ovrén H. (2016) Efficient Multi-frequency Phase Unwrapping Using Kernel Density Estimation. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9908. Springer, Cham
DOI https://doi.org/10.1007/978-3-319-46493-0_11
Publisher Name Springer, Cham