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Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
Scanning 3 d full human bodies using kinects
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Scanning 3 d full human bodies using kinects

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  • 1. By, Fensa Merry Saj LBSITW
  • 2. OVERVIEW  Introduction  Related Work  System Setup  Reconstruction Approach  Results  Conclusion  References 10/5/2013 2
  • 3. INTRODUCTION  Many computer graphics applications require realistic 3D models of human bodies.  Depth cameras such as Microsoft Kinects are able to capture depth and image data at video rate.  Kinect is compact, low-price and as easy to use as a video camera. 10/5/2013 3
  • 4. 10/5/2013 4
  • 5. RELATED WORK  Scanning devices based on structured light or laser scan can capture human body with much high quality, but is very expensive (about $240,000).  Two main approaches employed in depth camera technology are:- based on the time-of-flight principle, measuring time delay between transmissions of a light pulse(about $8,000). based on light coding; projecting a known infrared pattern onto the scene and determining depth based on the pattern’s deformation. 10/5/2013 5
  • 6. RELATED WORK(contd…) A most popular one based on light coding is the Microsoft Kinect Sensor which is at a price of only $150.  3 Main Types:- Registration without a template.  This method requires high quality scan data & needs small changes in temporal coherence. Registration with a template.  This method needs a relative accurate template & then uses the template to fit each scan. 10/5/2013 6
  • 7. RELATED WORK(contd…) Registration with a semi-template. Rough template, such as the skeleton model of articulated object, can be utilized.  The first type requires high quality input data & is computationally expensive; the second one needs an accurate template which is hard to fulfil in many applications.  Here, this system uses the third type. 10/5/2013 7
  • 8. RELATED WORK( contd…)  The idea of creating a graph of pairwise alignments between scans.  First, pairwise rigid alignment is computed in the geometric level.  Global error distribution then operates on an upper level, where errors are measured in terms of the relative rotations and translations of pairwise alignments.  The graph methods can simultaneously minimize the errors of all views rapidly and do not need all scan in memory. 10/5/2013 8
  • 9. SYSTEM SETUP  Two kinects are used to capture the upper and the lower part of a human body respectively, without overlapping region, from one direction.  A third kinect is used to capture the middle part of the human body from the opposite direction.  The distance between two sets of Kinects is about 2 meters.  A turntable is put in between them. 10/5/2013 9
  • 10. The setup of our system 10/5/2013 10
  • 11. RECONSTRUCTION APPROACH  Denote Di={Mi,Ii},i=1,….n as the captured data, n is the number of captured frames, Mi is the merged mesh & Ii is the corresponding image of the i-th frame respectively.  First, a rough template is constructed.  The template is used to deform the geometry of successive frames pair wisely.  Global registration is performed to distribute errors in the deformation space.  Finally, reconstructed model is generated using Poisson reconstruction method. 10/5/2013 11
  • 12.  An accurate template is unavailable.  We construct an estimated body shape as the template mesh T1 from the first frame.  It is impossible to use this template to register each frame by geometry fitting but it can track the pairwise deformation of successive frames.  T1={v1^k,k=1…K; K is the number of nodes of T1 (typically 50-60). 10/5/2013 12
  • 13.  Suppose Mi, i=1…n forming a cycle. fi,j denotes the registration that can deform mesh Mi to register with mesh Mj.  To find the pairwise registration f1,2,f2,3,…fn-1,n,fn,1. Deformation Model:  Suppose we have two meshes Mi & Mj, and template mesh at frame i is Ti, then 10/5/2013 13
  • 14. Pairwise registration:  For successive frames Mi and Mi+1,corresponding feature points are obtained by optical flow in the corresponding images. Projection to the first frame:  n-1 pair wise deformation is required to recover all the relative position of all frames.(refer fig.6)  The desired pairwise deformation f̂1,2,f̂2,3,…f̂n-1,n,f̂n,1 should meet the following conditions:  1.It is cyclic consistent.  2.The original pairwise deformation is relatively correct, so minimize the weighted square error of the new and old deformation. 10/5/2013 14
  • 15. Overview of our reconstruction algorithm 10/5/2013 15
  • 16. 10/5/2013 16
  • 17. Different 3D full human models generated by the system 10/5/2013 17
  • 18. RESULTS  Global non-rigid registration gives better result than global rigid alignment.  Since the color image and depth information are captured simultaneously and calibrated, the color information of deformed mesh is generated automatically.  Virtual try on.  Personalized avatar-video games,online shopping , human computer interaction etc. 10/5/2013 18
  • 19. Realistic virtual try on experience based on the reconstructed model.(Left)the try on results;(right)the corresponding meshes. 10/5/2013 19
  • 20. Personalized avatar generated by our system. The motion of the human body is driven by a given skeleton motion sequence 10/5/2013 20
  • 21. CONCLUSION  The proposed method can deal with non-rigid alignment and complex occlusions.  The two stage registration algorithm is efficient and of memory efficiency.  The system can generate convincing 3D human bodies at a much low price.  It has good potential for home oriented VR applications. 10/5/2013 21
  • 22. REFERENCES  [1]Jing Tong; Jin Zhou; Ligang Liu; Zhigeng Pan; Hao Yan, ”Scanning 3D Full Human Bodies Using Kinects”,Visualization and Computer Graphics, IEEE Transactions on, vol.18, no.4, April 2012.  [2]Srivishnu Satyavolu,Gerd Bruder,Pete Willemsen,Frank Stenicke,”Analysis of IR-based virtual reality tracking using multiple Kinects”,2012 IEEE Virtual Reality,2012. 10/5/2013 22
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