3d Capturing Performances of Low-Cost Range Sensors for Mass-Market Applications

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.

Low-Cost 3d Devices and Laser Scanners Comparison for the Application in Orthopedic Centres

“…Microsoft Kinect 1 and 2) and compare the results with a professional triangulation based laser scanner, the Minolta Vivid 9i. The starting point of the research was to evaluate the systematic error of each device with the use of a calibrated plane in order to highlight which was the best to be used as a reference, as demonstrated by (Guidi et al, 2016). The second step consisted in surveying three different test objects, representative of the biomedical application, and comparing the results with the reference.…”

“…For medical purposes, the Structure Sensor can be coupled with Captevia, an application for iPad, iPhone and iPod touch, available for free in the Apple Store that can be used for generating and saving 3D scan files on the Apple devices. The system is made by an IR projector, a 640x480 CMOS IR camera dedicated to range sensing and a second 640x480 CMOS colour camera for capturing the colour image associated to the depth map (Guidi et al, 2016). The O&P Scan by Rodin is an handheld 3D digitizer to be used in the medical field for acquiring the parts of the body of the patient to be analysed.…”

“…The International Vocabulary of Metrology (Joint Committee For Guides In Metrology (JCGM), 2008) stated that the measurement uncertainty of each equipment or device is affected by the systematic errors -associated to the concept of accuracy -and unavoidable random errors -associated with the concept of precision -, dependant by unpredictable causes like the electronic noise, that can be only statistically characterized for making the end-user aware of the measurement system intrinsic limitations (Guidi et al, 2016). The global error can be evaluated by acquiring a certified test object and measuring the deviation of the model from the ideal one.…”

“…In this work, the attention is focussed on the spatial error, neglecting the temporal error. This choice derives from (Guidi et al, 2016), that already stated that, in the practical application, a spatial characterization allows to obtain more statistically significant results. Moreover, we noticed that the temporal error should be mitigated by the time averaging that software like Skanect perform.…”

“…Four test objects were used to evaluate the different devices: a calibrated flat plane, used to calculate the systematic error (Guidi et al, 2016), and three anatomical parts of a mannequin to simulate the parts of a human body, chosen to investigate different dimensions and detail levels: a hand, a thigh and a chest. The characteristics of the different test objects are summarized in Table 2.…”