A RGB-D based instant body-scanning solution for compact box installation

Body scanning presents unique value in delivering the first digital asset of a human body thus resulting a fundamental device for a range of applications dealing with health, fashion and fitness. Despite several body scanners are in the market, recently depth cameras such as Microsoft Kinect® have attracted the 3D community; compared with conventional 3D scanning systems, these sensors are able to capture depth and RGB data at video rate and even if quality and depth resolution are not optimal for this kind of applications, the major benefit comes from the overall acquisition speed and from the IR pattern that allows poor lighting conditions optimal acquisition. When dealing with non-rigid bodies, unfortunately, the use of a single depth camera may lead to inconsistent results mainly caused by wrong surfaces registration. With the aim of improving existing systems based on low-resolution depth cameras, the present paper describes a novel scanning system for capturing 3D full human body models by using multiple Kinect® devices in a compact setup. The system consists of an instantaneous scanning system using eight depth cameras, appropriately arranged in a compact wireframe. To validate the effectiveness of the proposed architecture, a comparison of the obtained 3D body model with the one obtained using a professional Konica Minolta Range Seven 3D scanner is also presented and possible drawbacks are hinted at.

[1]  Reinhard Koch,et al.  Time-of-Flight Sensors in Computer Graphics , 2009, Eurographics.

[2]  Francesco Leali,et al.  Integration of CAM off-line programming in robot high-accuracy machining , 2013, Proceedings of the 2013 IEEE/SICE International Symposium on System Integration.

[3]  Xu Wu-jun.  Study on 3D body scanning,reconstruction and measurement techniques based on Kinect , 2012 .

[4]  Ligang Liu,et al.  Scanning 3D Full Human Bodies Using Kinects , 2012, IEEE Transactions on Visualization and Computer Graphics.

[5]  Michael J. Black,et al.  Home 3D body scans from noisy image and range data , 2011, 2011 International Conference on Computer Vision.

[6]  Lapo Governi,et al.  3D geometry reconstruction from orthographic views: A method based on 3D image processing and data fitting , 2013, Comput. Ind..

[7]  Lapo Governi,et al.  Improving surface reconstruction in shape from shading using easy-to-set boundary conditions , 2013, Int. J. Comput. Vis. Robotics.

[8]  Hong Wei,et al.  A survey of human motion analysis using depth imagery , 2013, Pattern Recognit. Lett..

[9]  Monica Carfagni,et al.  Design and Assessment of a Machine Vision System for Automatic Vehicle Wheel Alignment , 2013 .

[10]  Sandro Barone,et al.  Multiple alignments of range maps by active stereo imaging and global marker framing , 2013 .

[11]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Marcello Pellicciari,et al.  Real-time 3D features reconstruction through monocular vision , 2010 .

[13]  Alessandro Naddeo,et al.  Postural comfort evaluation: experimental identification of Range of Rest Posture for human articular joints , 2012 .

[14]  Lapo Governi,et al.  Making Blind People Autonomous in the Exploration of Tactile Models: A Feasibility Study , 2015, HCI.

[15]  Heinz Hügli,et al.  A multi-resolution ICP with heuristic closest point search for fast and robust 3D registration of range images , 2003, Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings..

[16]  S. Barone,et al.  Computer-aided modelling of three-dimensional maxillofacial tissues through multi-modal imaging , 2013, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.