Abstract
Abstract. Since the advent of the first Kinect as motion controller device for the Microsoft XBOX platform (November 2010), several similar active and low-cost range sensing devices have been introduced on the mass-market for several purposes, including gesture based interfaces, 3D multimedia interaction, robot navigation, finger tracking, 3D body scanning for garment design and proximity sensors for automotive. However, given their capability to generate a real time stream of range images, these has been used in some projects also as general purpose range devices, with performances that for some applications might be satisfying. This paper shows the working principle of the various devices, analyzing them in terms of systematic errors and random errors for exploring the applicability of them in standard 3D capturing problems. Five actual devices have been tested featuring three different technologies: i) Kinect V1 by Microsoft, Structure Sensor by Occipital, and Xtion PRO by ASUS, all based on different implementations of the Primesense sensor; ii) F200 by Intel/Creative, implementing the Realsense pattern projection technology; Kinect V2 by Microsoft, equipped with the Canesta TOF Camera. A critical analysis of the results tries first of all to compare them, and secondarily to focus the range of applications for which such devices could actually work as a viable solution.
Highlights
1.1 Gesture trackingThe low-cost 3D sensors mentioned in this paper are simple 3D devices for managing the so called “gesture based” interfaces
Five actual devices have been tested featuring three different technologies: i) Kinect V1 by Microsoft, Structure Sensor by Occipital, and Xtion PRO by ASUS, all based on different implementations of the Primesense sensor; ii) F200 by Intel/Creative, implementing the Realsense pattern projection technology; Kinect V2 by Microsoft, equipped with the Canesta TOF Camera
The performances of five low-cost 3D sensors conceived for gesture tracking have been analyzed in terms of systematic errors and random errors for exploring the applicability of them in standard 3D capturing projects
Summary
1.1 Gesture trackingThe low-cost 3D sensors mentioned in this paper are simple 3D devices for managing the so called “gesture based” interfaces. The extremely good results of Wii generated a worried reaction of the competitors that started to think to alternative ways for measuring position and orientation of the player harms, hands and legs, without any device hold by the end-user This led Microsoft, at the time involved in the lunch of the XBOX360 platform, to start the Project Natal whose purpose was to develop a device looking to the user (like Sony’s Eyetoy), but with a full 3D vision of the scene, on the basis of which generate gesturebased 3D input for a gaming console. The high variability in the latter results is due to the small size of the calibrated object with the respect of the limited spatial resolution of the analyzed devices, and in general to the relatively limited number of samples involved in this type of analysis, that make them more prone to high variability of the related characterization results
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
