• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
HR3D: Content Adaptive Parallax Barriers
 

HR3D: Content Adaptive Parallax Barriers

on

  • 27,732 views

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 ...

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).

Statistics

Views

Total Views
27,732
Views on SlideShare
4,271
Embed Views
23,461

Actions

Likes
2
Downloads
0
Comments
0

18 Embeds 23,461

http://web.media.mit.edu 23013
http://translate.googleusercontent.com 163
http://augmentedlifeinmotion.com 130
http://ideca.bellasartes.uclm.es 72
http://forum.sirius.dn.ua 33
http://diyiat.blogspot.com 12
http://webcache.googleusercontent.com 10
http://diyiat.blogspot.tw 5
http://www.slideshare.net 5
http://sirius.dn.ua 4
http://fanyi.youdao.com 4
http://honyaku.yahoofs.jp 3
http://translate.yandex.net 2
http://cameraculture.media.mit.edu 1
http://66.163.168.225 1
http://perevod.yandex.net 1
http://131.253.14.66 1
http://131.253.14.125 1
More...

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

CC Attribution-NonCommercial-ShareAlike LicenseCC Attribution-NonCommercial-ShareAlike LicenseCC Attribution-NonCommercial-ShareAlike License

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • http://www.pc.rhul.ac.uk/staff/J.Zanker/PS1061/L4/PS1061_4.htmhttp://psychinaction.files.wordpress.com/2010/02/picture2.jpg
  • 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/

HR3D: Content Adaptive Parallax Barriers HR3D: Content Adaptive Parallax Barriers Presentation Transcript

  • 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
    • Avatar [released December 2009, has grossed >$2.7 billion so far]
    • RealD Cinema, Dolby 3D, XpanD 3D, MasterImage 3D, etc.
    • NVIDIA 3D Vision
    • Nintendo 3DS
    Expansion of 3D to our living rooms, mobile phones, advertising, etc.
  • What is a 3D Display?
    Monocular Depth Cues Supported by Conventional Photography
    • relative and familiar size
    • perspective and occlusion
    • texture gradient, shading and lighting, and atmospheric effects
  • Binocular Depth Cues
    “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?”
    Binocular Depth Cues
    • retinal disparity [Charles Wheatstone, 1838]
    • convergence
  • What is missing?
    Additional Monocular Depth Cues
    • motion parallax [Hermann von Helmholtz, 1866]
    • accommodation
  • Conventional vs. Light Field Displays
    Lenslet Array
    Pinhole Array
    u
    s
    Virtual Scene
    Viewers
    Display
    Conventional displays are view-independent
    • Consider two-plane light field parameterization [Levoy and Hanrahan, 1996]
    • Pixel intensity/color does not vary as a function of viewing angle
    Light field displays are view-dependent (automultiscopic)
    • Achieved with pinhole/lenslet array placed close to the display
    • Trades decreased spatial resolution for increased angular resolution
    Frederic 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
    • Two alternatives for spatially-multiplexed light field display:
    • 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
  • Target 4D Light Field
  • 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
    • Introduction
    • Content-Adaptive Parallax Barriers
    • Implementation and Results
    • Discussion and Future Work
    • Conclusion
  • Light Field Analysis of Barriers
    k
    L[i,k]
    i
    `
    k
    g[k]
    i
    L[i,k]
    f[i]
    light box
  • 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
    • Introduction
    • Content-Adaptive Parallax Barriers
    • Implementation and Results
    • Discussion and Future Work
    • Conclusion
  • Implementation
    Components
    • 22 inch ViewSonic FuHzion VX2265wm LCD [1680×1050 @ 120 fps]
  • Motion Parallax
    viewer moves right
    viewer moves up
    Light Field
  • Increasing Brightness and Refresh Rate
    1) Increasing brightness:
    2) Increasing refresh rate:
  • Increasing Brightness and Refresh Rate
    1) Increasing brightness:
    2) Increasing refresh rate:
  • Increasing Brightness
    Time-Shifted Barriers (central view)
    Content-Adaptive Barriers (central view)
  • Increasing Refresh Rate
    Time-Shifted Barriers (central view)
    Content-Adaptive Barriers (central view)
  • Outline
    • Introduction
    • Content-Adaptive Parallax Barriers
    • Implementation and Results
    • Discussion and Future Work
    • Conclusion
  • Future Work: Analytic Adaptation?
    rear mask: f1[i,j]
    front mask: g1[k,l]
    What patterns does content-adaptation produce?
    • Optimization appears to produce local parallax barriers
  • Future Work: Analytic Adaptation?
    viewer moves right (b)
    viewer moves up (a)
    Light Field
    (step edge)
    Rear mask
    Front mask
    What patterns does content-adaptation produce?
    • Optimization appears to produce local parallax barriers
    • Related to aperture problem(i.e., motion of windowed grating)
    • NMF converges to local stationary point, not global minimum
    • Emergent structure predicted by angular gradient:
    David Marr and Shimon Ullman. Directional Selectivity and Its Use in Early Visual Processing. 1981.
  • Future Work: Local Parallax Barriers
    viewer moves right (b)
    viewer moves up (a)
    Light Field
    Streamlines of the Angular Gradient*
    Rear Mask
    Front Mask
    *Brian Cabral and Leith Casey Leedom. Imaging Vector Fields using Line Integral Convolution. 1993.
  • Outline
    • Introduction
    • Content-Adaptive Parallax Barriers
    • Implementation and Results
    • Discussion and Future Work
    • Conclusion
  • Conclusion
    `
    Content-Adaptive Parallax Barriers
    =
    • Described a rank constraint for all dual-layer displays
    • With a fixed pair of masks, emitted light field is rank-1
    • Achieved higher-rank approximation using temporal multiplexing
    • With T time-multiplexed masks, emitted light field is rank-T
    • Constructed a prototype using off-the-shelf panels
    • Demonstrated light field display is a matrix approximation problem
    • Introduced content-adaptive parallax barriers
    • Applied weighted NMF to optimize weighted Euclidean distance to target
    Adaptation increases brightness and refresh rate of dual-stacked LCDs
  • Questions and Answers
    Content-Adaptive Parallax Barriers
    “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.”
    Gabriel Lippmann, 1908