3D human body modeling based on single Kinect

3D human body models have been widely used in all fields. For acquiring precise human body models, the research about their recognition and reconstruction has become a major topic. Here we present a new method for modeling 3D human body based on single Kinect from the viewpoint of cost and operability. In this method, human body 3D information is recognized and acquired by only one Kinect while 3D human body models are reconstructed by using the tools of Processing and Point Cloud Library. To achieve the reconstruction objective, the iterative closest point algorithm is adopted for registering the captured upper human body 3D point cloud data with the standard reference human body data. The experiment results demonstrate that this method is feasible.

[1]  Zhengyou Zhang,et al.  Microsoft Kinect Sensor and Its Effect , 2012, IEEE Multim..

[2]  Jae-Hean Kim,et al.  Calibration of multi-Kinect and multi-camera setup for full 3D reconstruction , 2013, IEEE ISR 2013.

[3]  Ichiro Hagiwara,et al.  Automatic small blend recognition from B-rep models for analysis , 2009, Engineering with Computers.

[4]  Yumin Li,et al.  AN ACCURATE REGISTRATION METHOD BASED ON POINT CLOUDS AND REDUNDANCY ELIMINATION OF LIDAR DATA , 2008 .

[5]  Elena De Momi,et al.  Multi kinect people detection for intuitive and safe human robot cooperation in the operating room , 2013, 2013 16th International Conference on Advanced Robotics (ICAR).

[6]  Cheng-Tiao Hsieh.  An efficient development of 3D surface registration by Point Cloud Library (PCL) , 2012, 2012 International Symposium on Intelligent Signal Processing and Communications Systems.

[7]  Daijin Kim,et al.  A Fast ICP Algorithm for 3-D Human Body Motion Tracking , 2010, IEEE Signal Processing Letters.

[8]  Wu Song,et al.  Motion capture of maintenance personnel based on multi-Kinect , 2013, 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE).

[9]  Uwe D. Hanebeck,et al.  Intelligent sensor-scheduling for multi-kinect-tracking , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Xu Hua.  GPU Based Fast 3D-Object Modeling with Kinect , 2012 .

[11]  Georgy L. Gimel'farb,et al.  Multi-Kinect scene reconstruction: Calibration and depth inconsistencies , 2013, 2013 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013).

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

[13]  Heinz Hügli,et al.  Fast ICP Algorithms for Shape Registration , 2002, DAGM-Symposium.

[14]  Antonis A. Argyros,et al.  Efficient model-based 3D tracking of hand articulations using Kinect , 2011, BMVC.

[15]  刘鑫,et al.  基于 GPU 和 Kinect 的快速物体重建 , 2012 .

[16]  Tilak Dutta,et al.  Evaluation of the Kinect™ sensor for 3-D kinematic measurement in the workplace. , 2012, Applied ergonomics.