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Evaluating and Improving the Depth Accuracy of Kinect for Windows v2

IEEE Sensors Journal · 2015 · Vol. 15(8) · pp. 4275–4285
Yang LinLongyu ZhangHaiwei DongAbdulhameed AlelaiwiAbdulmotaleb El Saddik

Abstract

Microsoft Kinect sensor has been widely used in many applications since the launch of its first version. Recently, Microsoft released a new version of Kinect sensor with improved hardware. However, the accuracy assessment of the sensor remains to be answered. In this paper, we measure the depth accuracy of the newly released Kinect v2 depth sensor, and obtain a cone model to illustrate its accuracy distribution. We then evaluate the variance of the captured depth values by depth entropy. In addition, we propose a trilateration method to improve the depth accuracy with multiple Kinects simultaneously. The experimental results are provided to ascertain the proposed model and method.

Advanced Optical Sensing TechnologiesOptical measurement and interference techniquesAdvanced Vision and ImagingTrilaterationComputer scienceMeasured depthComputer visionArtificial intelligenceEntropy (arrow of time)Variance (accounting)Measure (data warehouse)Computer graphics (images)Engineering
Citations
289
FWCI
101.04
field-weighted impact
References
30
Percentile
100%
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Citations per year
References
Real-time human pose recognition in parts from single depth images
Communications of the ACM · 2013 · 2,049 citations
Lock-in Time-of-Flight (ToF) Cameras: A Survey
IEEE Sensors Journal · 2011 · 620 citations
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