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HR3D: Content Adaptive Parallax Barriers Slide 1 HR3D: Content Adaptive Parallax Barriers Slide 2 HR3D: Content Adaptive Parallax Barriers Slide 3 HR3D: Content Adaptive Parallax Barriers Slide 4 HR3D: Content Adaptive Parallax Barriers Slide 5 HR3D: Content Adaptive Parallax Barriers Slide 6 HR3D: Content Adaptive Parallax Barriers Slide 7 HR3D: Content Adaptive Parallax Barriers Slide 8 HR3D: Content Adaptive Parallax Barriers Slide 9 HR3D: Content Adaptive Parallax Barriers Slide 10 HR3D: Content Adaptive Parallax Barriers Slide 11 HR3D: Content Adaptive Parallax Barriers Slide 12 HR3D: Content Adaptive Parallax Barriers Slide 13 HR3D: Content Adaptive Parallax Barriers Slide 14 HR3D: Content Adaptive Parallax Barriers Slide 15 HR3D: Content Adaptive Parallax Barriers Slide 16 HR3D: Content Adaptive Parallax Barriers Slide 17 HR3D: Content Adaptive Parallax Barriers Slide 18 HR3D: Content Adaptive Parallax Barriers Slide 19 HR3D: Content Adaptive Parallax Barriers Slide 20 HR3D: Content Adaptive Parallax Barriers Slide 21 HR3D: Content Adaptive Parallax Barriers Slide 22 HR3D: Content Adaptive Parallax Barriers Slide 23 HR3D: Content Adaptive Parallax Barriers Slide 24 HR3D: Content Adaptive Parallax Barriers Slide 25 HR3D: Content Adaptive Parallax Barriers Slide 26 HR3D: Content Adaptive Parallax Barriers Slide 27 HR3D: Content Adaptive Parallax Barriers Slide 28 HR3D: Content Adaptive Parallax Barriers Slide 29 HR3D: Content Adaptive Parallax Barriers Slide 30 HR3D: Content Adaptive Parallax Barriers Slide 31 HR3D: Content Adaptive Parallax Barriers Slide 32 HR3D: Content Adaptive Parallax Barriers Slide 33 HR3D: Content Adaptive Parallax Barriers Slide 34 HR3D: Content Adaptive Parallax Barriers Slide 35 HR3D: Content Adaptive Parallax Barriers Slide 36 HR3D: Content Adaptive Parallax Barriers Slide 37 HR3D: Content Adaptive Parallax Barriers Slide 38 HR3D: Content Adaptive Parallax Barriers Slide 39 HR3D: Content Adaptive Parallax Barriers Slide 40 HR3D: Content Adaptive Parallax Barriers Slide 41 HR3D: Content Adaptive Parallax Barriers Slide 42 HR3D: Content Adaptive Parallax Barriers Slide 43 HR3D: Content Adaptive Parallax Barriers Slide 44 HR3D: Content Adaptive Parallax Barriers Slide 45 HR3D: Content Adaptive Parallax Barriers Slide 46 HR3D: Content Adaptive Parallax Barriers Slide 47 HR3D: Content Adaptive Parallax Barriers Slide 48 HR3D: Content Adaptive Parallax Barriers Slide 49
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HR3D: Content Adaptive Parallax Barriers

HR3D: Content Adaptive Parallax Barriers, SIGGRAPH Asia 2010 Technical Paper presentation, presented by Douglas Lanman (http://web.media.mit.edu/~dlanman). Please see the project page for more details: http://web.media.mit.edu/~mhirsch/hr3d

This is a project in the Camera Culture group (http://cameraculture.media.mit.edu) at the MIT Media Lab, led by Professor Ramesh Raskar (http://web.media.mit.edu/~raskar).

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HR3D: Content Adaptive Parallax Barriers

  1. 1. Content-Adaptive Parallax Barriers for Automultiscopic 3D Display<br />Douglas Lanman Matthew Hirsch<br />Yunhee Kim Ramesh Raskar<br />MIT Media Lab<br />Imaging Hardware Session<br />Friday, 17 December | 4:15 PM – 6:00 PM | Room E1-E4<br />
  2. 2. A Renewed Interest in 3D Displays<br />Theatrical and Home Movies<br />Consumer Electronics<br />A recent history of 3D displays<br /><ul><li> Avatar [released December 2009, has grossed >$2.7 billion so far]
  3. 3. RealD Cinema, Dolby 3D, XpanD 3D, MasterImage 3D, etc.
  4. 4. NVIDIA 3D Vision
  5. 5. Nintendo 3DS</li></ul>Expansion of 3D to our living rooms, mobile phones, advertising, etc.<br />
  6. 6. What is a 3D Display?<br />Monocular Depth Cues Supported by Conventional Photography<br /><ul><li> relative and familiar size
  7. 7. perspective and occlusion
  8. 8. texture gradient, shading and lighting, and atmospheric effects</li></li></ul><li>Binocular Depth Cues<br />“It being thus established that the mind perceives an object of three dimensions by means of the two dissimilar pictures projected by it on the two retinae, the following question occurs: What would be the visual effect of simultaneously presenting to each eye, instead of the object itself, its projection on a plane surface as it appears to that eye?”<br />Binocular Depth Cues<br /><ul><li> retinal disparity [Charles Wheatstone, 1838]
  9. 9. convergence</li></li></ul><li>What is missing?<br />Additional Monocular Depth Cues<br /><ul><li> motion parallax [Hermann von Helmholtz, 1866]
  10. 10. accommodation</li></li></ul><li>Conventional vs. Light Field Displays<br />Lenslet Array<br />Pinhole Array<br />u<br />s<br />Virtual Scene<br />Viewers<br />Display<br />Conventional displays are view-independent<br /><ul><li> Consider two-plane light field parameterization [Levoy and Hanrahan, 1996]
  11. 11. Pixel intensity/color does not vary as a function of viewing angle</li></ul>Light field displays are view-dependent (automultiscopic)<br /><ul><li> Achieved with pinhole/lenslet array placed close to the display
  12. 12. Trades decreased spatial resolution for increased angular resolution</li></ul>Frederic Ives. Parallax Stereogram and Process of Making the Same. 1903.<br />Gabriel Lippmann. Épreuves Réversibles Donnant la Sensation du Relief. 1908.<br />
  13. 13. Previous Approaches<br />barrier<br />lenslet<br />sensor/display<br />sensor/display<br />Achieving Light Field (Automultiscopic) Display<br /><ul><li> Two alternatives for spatially-multiplexed light field display:
  14. 14. Attenuating masks using parallax barriers [Ives, 1903]
  15. 15. Refracting lenslet arrays [Lippmann, 1908]
  16. 16. Barriers cause severe attenuation  dim displays
  17. 17. Lenslets impose fixed trade-off between spatial and angular resolution
  18. 18. Masks can be optimized to increase optical efficiency
  19. 19. Enables flexible 3D displays with increased brightness and refresh rate</li></li></ul><li>Target 4D Light Field<br />
  20. 20. Target 4D Light Field<br />viewer moves right<br />viewer moves up<br />
  21. 21. Parallax Barrier Front Mask (1 of 9)<br />
  22. 22. Parallax Barrier Rear Mask (1 of 9)<br />
  23. 23. Outline<br /><ul><li>Introduction
  24. 24. Content-Adaptive Parallax Barriers
  25. 25. Implementation and Results
  26. 26. Discussion and Future Work
  27. 27. Conclusion</li></li></ul><li>Light Field Analysis of Barriers<br />k<br />L[i,k]<br />i<br />`<br />k<br />g[k]<br />i<br />L[i,k]<br />f[i]<br />light box<br />
  28. 28. Time-Multiplexing using Shifted Pinholes<br />L[i,k]<br />`<br />k<br />g[k]<br />i<br />f[i]<br />light box<br />Ken Perlinet al. An Autosteroscopic Display. 2000.<br />Yunhee Kim et al. Electrically Movable Pinhole Arrays. 2007.<br />
  29. 29. Content-Adaptive Parallax Barriers<br />L[i,k]<br />`<br />k<br />g[k]<br />i<br />f[i]<br />light box<br />
  30. 30. Content-Adaptive Parallax Barriers<br />`<br />=<br />
  31. 31. Content-Adaptive Parallax Barriers<br />`<br />=<br />
  32. 32. Content-Adaptive Parallax Barriers<br />`<br />=<br />
  33. 33. Content-Adaptive Parallax Barriers<br />`<br />=<br />
  34. 34. Target 4D Light Field<br />viewer moves right<br />viewer moves up<br />
  35. 35. Optimization: Iteration 1<br />rear mask: f1[i,j]<br />front mask: g1[k,l]<br />reconstruction (central view)<br />Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999.<br />Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.<br />
  36. 36. Optimization: Iteration 10<br />rear mask: f1[i,j]<br />front mask: g1[k,l]<br />reconstruction (central view)<br />Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999.<br />Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.<br />
  37. 37. Optimization: Iteration 20<br />rear mask: f1[i,j]<br />front mask: g1[k,l]<br />reconstruction (central view)<br />Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999.<br />Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.<br />
  38. 38. Optimization: Iteration 30<br />rear mask: f1[i,j]<br />front mask: g1[k,l]<br />reconstruction (central view)<br />Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999.<br />Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.<br />
  39. 39. Optimization: Iteration 40<br />rear mask: f1[i,j]<br />front mask: g1[k,l]<br />reconstruction (central view)<br />Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999.<br />Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.<br />
  40. 40. Optimization: Iteration 50<br />rear mask: f1[i,j]<br />front mask: g1[k,l]<br />reconstruction (central view)<br />Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999.<br />Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.<br />
  41. 41. Optimization: Iteration 60<br />rear mask: f1[i,j]<br />front mask: g1[k,l]<br />reconstruction (central view)<br />Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999.<br />Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.<br />
  42. 42. Optimization: Iteration 70<br />rear mask: f1[i,j]<br />front mask: g1[k,l]<br />reconstruction (central view)<br />Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999.<br />Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.<br />
  43. 43. Optimization: Iteration 80<br />rear mask: f1[i,j]<br />front mask: g1[k,l]<br />reconstruction (central view)<br />Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999.<br />Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.<br />
  44. 44. Optimization: Iteration 90<br />rear mask: f1[i,j]<br />front mask: g1[k,l]<br />reconstruction (central view)<br />Daniel Lee and Sebastian Seung. Non-negative Matrix Factorization. 1999.<br />Vincent Blondel et al. Weighted Non-negative Matrix Factorization. 2008.<br />
  45. 45. Content-Adaptive Front Mask (1 of 9)<br />
  46. 46. Content-Adaptive Rear Mask (1 of 9)<br />
  47. 47. Emitted 4D Light Field<br />
  48. 48. Benefits of Content-Adaptation<br />1) Increasing brightness:<br />2) Increasing refresh rate:<br />
  49. 49. Outline<br /><ul><li>Introduction
  50. 50. Content-Adaptive Parallax Barriers
  51. 51. Implementation and Results
  52. 52. Discussion and Future Work
  53. 53. Conclusion</li></li></ul><li>Implementation<br />Components<br /><ul><li>22 inch ViewSonic FuHzion VX2265wm LCD [1680×1050 @ 120 fps]</li></li></ul><li>Motion Parallax<br />viewer moves right<br />viewer moves up<br />Light Field<br />
  54. 54.
  55. 55.
  56. 56. Increasing Brightness and Refresh Rate<br />1) Increasing brightness:<br />2) Increasing refresh rate:<br />
  57. 57. Increasing Brightness and Refresh Rate<br />1) Increasing brightness:<br />2) Increasing refresh rate:<br />
  58. 58. Increasing Brightness<br />Time-Shifted Barriers (central view)<br />Content-Adaptive Barriers (central view)<br />
  59. 59. Increasing Refresh Rate<br />Time-Shifted Barriers (central view)<br />Content-Adaptive Barriers (central view)<br />
  60. 60. Outline<br /><ul><li>Introduction
  61. 61. Content-Adaptive Parallax Barriers
  62. 62. Implementation and Results
  63. 63. Discussion and Future Work
  64. 64. Conclusion</li></li></ul><li>Future Work: Analytic Adaptation?<br />rear mask: f1[i,j]<br />front mask: g1[k,l]<br />What patterns does content-adaptation produce?<br /><ul><li> Optimization appears to produce local parallax barriers</li></li></ul><li>Future Work: Analytic Adaptation?<br />viewer moves right (b)<br />viewer moves up (a)<br />Light Field<br />(step edge)<br />Rear mask<br />Front mask<br />What patterns does content-adaptation produce?<br /><ul><li> Optimization appears to produce local parallax barriers
  65. 65. Related to aperture problem(i.e., motion of windowed grating)
  66. 66. NMF converges to local stationary point, not global minimum
  67. 67. Emergent structure predicted by angular gradient:</li></ul>David Marr and Shimon Ullman. Directional Selectivity and Its Use in Early Visual Processing. 1981.<br />
  68. 68. Future Work: Local Parallax Barriers<br />viewer moves right (b)<br />viewer moves up (a)<br />Light Field<br />Streamlines of the Angular Gradient*<br />Rear Mask<br />Front Mask<br />*Brian Cabral and Leith Casey Leedom. Imaging Vector Fields using Line Integral Convolution. 1993.<br />
  69. 69. Outline<br /><ul><li>Introduction
  70. 70. Content-Adaptive Parallax Barriers
  71. 71. Implementation and Results
  72. 72. Discussion and Future Work
  73. 73. Conclusion</li></li></ul><li>Conclusion<br />`<br />Content-Adaptive Parallax Barriers<br />=<br /><ul><li> Described a rank constraint for all dual-layer displays
  74. 74. With a fixed pair of masks, emitted light field is rank-1
  75. 75. Achieved higher-rank approximation using temporal multiplexing
  76. 76. With T time-multiplexed masks, emitted light field is rank-T
  77. 77. Constructed a prototype using off-the-shelf panels
  78. 78. Demonstrated light field display is a matrix approximation problem
  79. 79. Introduced content-adaptive parallax barriers
  80. 80. Applied weighted NMF to optimize weighted Euclidean distance to target</li></ul>Adaptation increases brightness and refresh rate of dual-stacked LCDs<br />
  81. 81. Questions and Answers<br />Content-Adaptive Parallax Barriers<br />“Can we request that Photography renders the full variety offered by the direct observation of objects? Is it possible to create a photographic print in such a manner that it represents the exterior world framed, in appearance, between the boundaries of the print, as if those boundaries were that of a window opened on reality? It appears that yes, we can request from Photography infinitely more than from the human hand.”<br />Gabriel Lippmann, 1908<br />
  • JianJia

    Apr. 22, 2014
  • w_keikou_w

    Aug. 26, 2013

HR3D: Content Adaptive Parallax Barriers, SIGGRAPH Asia 2010 Technical Paper presentation, presented by Douglas Lanman (http://web.media.mit.edu/~dlanman). Please see the project page for more details: http://web.media.mit.edu/~mhirsch/hr3d This is a project in the Camera Culture group (http://cameraculture.media.mit.edu) at the MIT Media Lab, led by Professor Ramesh Raskar (http://web.media.mit.edu/~raskar).

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