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      Calibration of Kinect for Xbox One and Comparison between the Two Generations of Microsoft Sensors

      research-article
      * ,
      Sensors (Basel, Switzerland)
      MDPI
      Kinect, calibration, depth maps, distortion removal, RGB-D, fusion libraries

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          Abstract

          In recent years, the videogame industry has been characterized by a great boost in gesture recognition and motion tracking, following the increasing request of creating immersive game experiences. The Microsoft Kinect sensor allows acquiring RGB, IR and depth images with a high frame rate. Because of the complementary nature of the information provided, it has proved an attractive resource for researchers with very different backgrounds. In summer 2014, Microsoft launched a new generation of Kinect on the market, based on time-of-flight technology. This paper proposes a calibration of Kinect for Xbox One imaging sensors, focusing on the depth camera. The mathematical model that describes the error committed by the sensor as a function of the distance between the sensor itself and the object has been estimated. All the analyses presented here have been conducted for both generations of Kinect, in order to quantify the improvements that characterize every single imaging sensor. Experimental results show that the quality of the delivered model improved applying the proposed calibration procedure, which is applicable to both point clouds and the mesh model created with the Microsoft Fusion Libraries.

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          Most cited references56

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          Enhanced computer vision with Microsoft Kinect sensor: a review.

          With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual (RGB) sensing has become available for widespread use. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in computer vision. This paper presents a comprehensive review of recent Kinect-based computer vision algorithms and applications. The reviewed approaches are classified according to the type of vision problems that can be addressed or enhanced by means of the Kinect sensor. The covered topics include preprocessing, object tracking and recognition, human activity analysis, hand gesture analysis, and indoor 3-D mapping. For each category of methods, we outline their main algorithmic contributions and summarize their advantages/differences compared to their RGB counterparts. Finally, we give an overview of the challenges in this field and future research trends. This paper is expected to serve as a tutorial and source of references for Kinect-based computer vision researchers.
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            Close-range camera calibration

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              Joint depth and color camera calibration with distortion correction.

              We present an algorithm that simultaneously calibrates two color cameras, a depth camera, and the relative pose between them. The method is designed to have three key features: accurate, practical, and applicable to a wide range of sensors. The method requires only a planar surface to be imaged from various poses. The calibration does not use depth discontinuities in the depth image, which makes it flexible and robust to noise. We apply this calibration to a Kinect device and present a new depth distortion model for the depth sensor. We perform experiments that show an improved accuracy with respect to the manufacturer's calibration.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                30 October 2015
                November 2015
                : 15
                : 11
                : 27569-27589
                Affiliations
                Politecnico di Milano, Department of Civil and Environmental Engineering (DICA)-Geomatic and Geodesy Section, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; E-Mail: livio.pinto@ 123456polimi.it
                Author notes
                [* ]Author to whom correspondence should be addressed; E-Mail: diana.pagliari@ 123456polimi.it ; Tel.: +39-2-2399-6543.
                Article
                sensors-15-27569
                10.3390/s151127569
                4701245
                26528979
                0628cfa4-d46e-4602-b084-bb6b26ab3831
                © 2015 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 03 August 2015
                : 27 October 2015
                Categories
                Article

                Biomedical engineering
                kinect,calibration,depth maps,distortion removal,rgb-d,fusion libraries
                Biomedical engineering
                kinect, calibration, depth maps, distortion removal, rgb-d, fusion libraries

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