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Efficient Multi-frequency Phase Unwrapping Using Kernel Density Estimation
[chapter]
2016
Lecture Notes in Computer Science
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
doi:10.1007/978-3-319-46493-0_11
fatcat:azywisud6fgbbejwq7bk7l76vu