- The document describes a new technique called content-adaptive parallax barriers for automultiscopic 3D displays.
- Content-adaptive barriers optimize the parallax barrier patterns for each image/video by applying non-negative matrix factorization to increase brightness and refresh rate compared to traditional time-multiplexed barriers.
- An initial prototype was constructed using off-the-shelf LCD panels to demonstrate the concept. The content-adaptive barriers allowed the system to emit higher rank light fields compared to conventional barriers.
Unblocking The Main Thread Solving ANRs and Frozen Frames
Content-Adaptive Parallax Barriers Increase 3D Display Brightness
1. 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
12. 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.
28. 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.
35. 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.
36. 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.
37. 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.
38. 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.
39. 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.
40. 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.
41. 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.
42. 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.
43. 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.
44. 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.
66. NMF converges to local stationary point, not global minimum
67. Emergent structure predicted by angular gradient:David Marr and Shimon Ullman. Directional Selectivity and Its Use in Early Visual Processing. 1981.
68. 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.
80. Applied weighted NMF to optimize weighted Euclidean distance to targetAdaptation increases brightness and refresh rate of dual-stacked LCDs
81. 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