Efficient Multi-frequency Phase Unwrapping Using Kernel Density Estimation
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 b
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act. 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.
Keywords: Time-of-flight
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· Kinect v2 · Kernel-density-estimation
Introduction
Multi-frequency time-of-flight is a way to accurately estimate distance, that was originally invented for Doppler RADAR [1]. More recently it has also found an application in RGB-D sensors1 that use time-of-flight ranging, such as the Microsoft Kinect v2 [2]. Depth from time-of-flight requires very accurate time-of-arrival estimation. Amplitude modulation improves accuracy, by measuring phase shifts between the received and emitted signals, instead of time-of-arrival. However, a disadvantage with amplitude modulation is that it introduces a periodic depth ambiguity. By using multiple modulation frequencies in parallel, the ambiguity can be resolved in most cases, and the useful range can thus be extended. 1
RGB-D sensors output both colour (RGB) and depth (D) images. Electronic supplementary material The online version of this chapter (doi:10. 1007/978-3-319-46493-0 11) contains supplementary material, which is available to authorized users.
c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part IV, LNCS 9908, pp. 170–185, 2016. DOI: 10.1007/978-3-319-46493-0 11
Efficient Multi-Frequency Phase Unwrapping using KDE
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Fig. 1. Single frame output on scene with greater than 18.75 m depth range. Left: libfreenect2, Center: proposed method. Right: corresponding RGB image. Pixels suppressed by outlier rejection are shown in green. The proposed method has more valid depth points than libfreenect2 resulting in a denser and more well defined depth scene. While the suppressed areas are clean from outliers for the proposed method, the libfreenect2 image is covered in salt and pepper noise. (Color figure online)
We introduce an efficient method to unwrap multi-frequency phase estimates for time-of-flight ranging. The algorithm uses kernel density estimation (KDE) in a spatial neighbourhood to rank different depth hypotheses. The KDE also doubles as a confidence measure which can be used to detect and suppress bad pixels. We apply our method to depth decoding for the Kinect
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