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Content-Adaptive Parallax Barriers for Automultiscopic 3D Display Douglas Lanman   Matthew Hirsch Yunhee Kim   Ramesh Raskar MIT Media Lab Imaging Hardware Session Friday, 17 December | 4:15 PM – 6:00 PM | Room E1-E4
A Renewed Interest in 3D Displays Theatrical and Home Movies Consumer Electronics A recent history of 3D displays ,[object Object]
 RealD Cinema, Dolby 3D, XpanD 3D, MasterImage 3D, etc.
 NVIDIA 3D Vision
 Nintendo 3DSExpansion of 3D to our living rooms, mobile phones, advertising, etc.
What is a 3D Display? Monocular Depth Cues Supported by Conventional Photography ,[object Object]
 perspective and occlusion
 texture gradient, shading and lighting, and atmospheric effects,[object Object]
 convergence,[object Object]
 accommodation,[object Object]
 Pixel intensity/color does not vary as a function of viewing angleLight field displays are view-dependent (automultiscopic) ,[object Object]
Trades decreased spatial resolution for increased angular resolutionFrederic Ives. Parallax Stereogram and Process of Making the Same. 1903. Gabriel Lippmann. Épreuves Réversibles Donnant la Sensation du Relief. 1908.
Previous Approaches barrier lenslet sensor/display sensor/display Achieving Light Field (Automultiscopic) Display ,[object Object]
Attenuating masks using parallax barriers [Ives, 1903]
 Refracting lenslet arrays [Lippmann, 1908]
 Barriers cause severe attenuation  dim displays
 Lenslets impose fixed trade-off between spatial and angular resolution
Masks can be optimized to increase optical efficiency
Enables flexible 3D displays with increased brightness and refresh rate,[object Object]
Target 4D Light Field viewer moves right viewer moves up
Parallax Barrier Front Mask (1 of 9)
Parallax Barrier Rear Mask (1 of 9)
Outline ,[object Object]
 Content-Adaptive Parallax Barriers
 Implementation and Results
 Discussion and Future Work
 Conclusion,[object Object]
Time-Multiplexing using Shifted Pinholes L[i,k] ` k g[k] i f[i] light box Ken Perlinet al. An Autosteroscopic Display. 2000. Yunhee Kim et al. Electrically Movable Pinhole Arrays. 2007.
Content-Adaptive Parallax Barriers L[i,k] ` k g[k] i f[i] light box
Content-Adaptive Parallax Barriers ` =
Content-Adaptive Parallax Barriers ` =
Content-Adaptive Parallax Barriers ` =
Content-Adaptive Parallax Barriers ` =
Target 4D Light Field viewer moves right viewer moves up
Optimization: Iteration 1 rear mask: f1[i,j] front mask: g1[k,l] reconstruction (central view) Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999. Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.
Optimization: Iteration 10 rear mask: f1[i,j] front mask: g1[k,l] reconstruction (central view) Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999. Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.
Optimization: Iteration 20 rear mask: f1[i,j] front mask: g1[k,l] reconstruction (central view) Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999. Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.
Optimization: Iteration 30 rear mask: f1[i,j] front mask: g1[k,l] reconstruction (central view) Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999. Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.
Optimization: Iteration 40 rear mask: f1[i,j] front mask: g1[k,l] reconstruction (central view) Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999. Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.
Optimization: Iteration 50 rear mask: f1[i,j] front mask: g1[k,l] reconstruction (central view) Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999. Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.
Optimization: Iteration 60 rear mask: f1[i,j] front mask: g1[k,l] reconstruction (central view) Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999. Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.
Optimization: Iteration 70 rear mask: f1[i,j] front mask: g1[k,l] reconstruction (central view) Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999. Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.
Optimization: Iteration 80 rear mask: f1[i,j] front mask: g1[k,l] reconstruction (central view) Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999. Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.
Optimization: Iteration 90 rear mask: f1[i,j] front mask: g1[k,l] reconstruction (central view) Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999. Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.
Content-Adaptive Front Mask (1 of 9)
Content-Adaptive Rear Mask (1 of 9)
Emitted 4D Light Field
Benefits of Content-Adaptation 1) Increasing brightness: 2) Increasing refresh rate:
Outline ,[object Object]

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HR3D: Content Adaptive Parallax Barriers

Editor's Notes

  1. http://www.pc.rhul.ac.uk/staff/J.Zanker/PS1061/L4/PS1061_4.htmhttp://psychinaction.files.wordpress.com/2010/02/picture2.jpg
  2. http://coolpics.911mb.com/galleries-coolpics/OpticalIllusions/WiggleVision/WiggleVision1.phphttp://www.brianharte.co.uk/blog/47-stereograms/http://prometheus.med.utah.edu/~bwjones/2009/03/focus-stacking/