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  • 1. Mult i-perspect ive Panoramas Slides f r om a t alk by Lihi Zelnik- Manor at I CCV’07 3DRR wor kshop
  • 2. Pict ur es capt ure memories
  • 3. Panoramas Regist rat ion: Brown & Lowe, I CCV’05 Blending: Burt & Adelson, Trans. Gr aphics,1983 Visualizat ion: Kopf et al., SI GGRAPH, 2007
  • 4. Bad panor ama? Out put of Brown & Lowe sof t ware
  • 5. No geomet rically consist ent solut ion
  • 6. Scient ist s solut ion t o panoramas: Single cent er of proj ect ion Regist rat ion: Brown & Lowe, I CCV’05 Blending: Burt & Adelson, Trans. Graphics,1983 Visualizat ion: Kopf et al., SI GGRAPH, 2007 No 3D!!!
  • 7. From sphere t o plane Dist ort ions are unavoidable
  • 8. Dist or t ed panoramas Out put of Brown & Lowe sof t ware Act ual appearance
  • 9. Obj ect ives 1. Bet t er looking panoramas 2. Let t he camera move: • Any view • Nat ural phot ographing
  • 10. St and on t he shoulder s of giant s Cartographers Artists
  • 11. Cart ographic proj ect ions
  • 12. Common panorama pr oj ect ions θ φ Cylindircal Perspective Stereographic
  • 13. Global Proj ect ions Cylindircal Perspective Stereographic
  • 14. Learn f rom t he art ist s Mult iple view point s De Chirico “Myst ery and Melancholy of a St reet ”, 1914 perspect iveperspect ive Sharp discont inuit y
  • 15. Renaissance paint ers solut ion “School of Athens”, Raffaello Sanzio ~1510 Give a separat e t reat ment t o dif f erent part s of t he scene!!
  • 16. Personalized pr oj ect ions “School of Athens”, Raffaello Sanzio ~1510 Give a separat e t reat ment t o dif f erent part s of t he scene!!
  • 17. Mult iple planes of proj ect ion Sharp discont inuit ies can of t en be well hidden
  • 18. Our multi-view result Single view
  • 19. Our multi-view result Single view
  • 20. Our multi-view result Single view
  • 21. Applying personalized proj ect ions Foreground Input images Background panorama
  • 22. Single view Our multi-view result
  • 23. Single view Our multi-view result
  • 24. Obj ect ives - r evisit ed 1. Bet t er looking panoramas 2. Let t he camera move: • Any view • Nat ural phot ographing Mult iple views can live t oget her
  • 25. Mult i-view composit ions David Hockney, Place Furst enberg, (1985) 3D!!
  • 26. Melissa Slemin, Place Furst enberg, 2003 Why mult i-view? Mult iple viewpoint s Single viewpoint David Hockney, Place Furst enberg, 1985
  • 27. Mult i-view panoramas Single view Mult iview Requires video input Zomet et al. (PAMI ’03)
  • 28. Long I maging Agarwala et al. (SI GGRAPH 2006)
  • 29. Smoot h Mult i-View Google maps
  • 30. What ’s wrong in t he pict ure? Google maps
  • 31. Non-smoot h Google maps
  • 32. The Chair David Hockney (1985)
  • 33. J oiners ar e popular 4,985 phot os mat ching j oiners. 4,007 phot os mat ching Hockney. 41 groups about Hockney Thousands of members Flickr st at ist ics (Aug’07):
  • 34. Main goals: Aut omat e j oiners Generalize panoramas t o general image collect ions
  • 35. Obj ect ives • For Ar t ist s: Reduce manual labor Manual: ~40min. Fully aut omat ic
  • 36. Obj ect ives • For Ar t ist s: Reduce manual labor • For non-ar t ist s: Gener at e pleasing-t o-t he-eye j oiner s
  • 37. Obj ect ives • For Ar t ist s: Reduce manual labor • For non-ar t ist s: Gener at e pleasing-t o-t he-eye j oiner s • For dat a explorat ion: Or ganize images spat ially
  • 38. What ’s going on her e?
  • 39. A cact i garden
  • 40. Principles
  • 41. Principles • Convey t opology Correct I ncorrect
  • 42. Principles • Convey t opology • A 2D layering of images Blending: blurry Graph-cut : cut s hood Desired j oiner
  • 43. Principles • Convey t opology • A 2D layering of images • Don’t dist ort images rot at e scalet ranslat e
  • 44. Principles • Convey t opology • A 2D layering of images • Don’t dist ort images • Minimize inconsist encies GoodBad
  • 45. Algorit hm
  • 46. St ep 1: Feat ure mat ching Brown & Lowe, I CCV’03
  • 47. St ep 2: Align Large inconsist encies Brown & Lowe, I CCV’03
  • 48. St ep 3: Or der Reduced inconsist encies
  • 49. Or dering images Try all orders: only f or small dat aset s
  • 50. Or dering images Try all orders: only f or small dat aset s complexit y: (m+n)α m = # images n = # overlaps α = # acyclic orders
  • 51. Or dering images Obser vat ions: – Typically each image overlaps wit h only a f ew ot her s – Many decisions can be t aken locally
  • 52. Or dering images Appr oximat e solut ion: – Solve f or each image independent ly – I t er at e over all images
  • 53. Can we do bet t er?
  • 54. St ep 4: I mprove alignment
  • 55. I t erat e Align-Order- I mpor t ance
  • 56. I t erat ive ref inement I nit ial Final
  • 57. I t erat ive ref inement I nit ial Final
  • 58. I t erat ive ref inement I nit ial Final
  • 59. What is t his?
  • 60. That ’s me reading
  • 61. Anza-Borrego
  • 62. Tract or
  • 63. Paolo Uccello, 1436 Art r eproduct ion
  • 64. Paolo Uccello, 1436 Zelnik & Perona, 2006 Art r eproduct ion
  • 65. Single view-point Zelnik & Perona, 2006 Art r eproduct ion
  • 66. Manual by Phot ographer
  • 67. Our aut omat ic result
  • 68. Failur e?
  • 69. GUI
  • 70. The I mpossible Bridge
  • 71. Homage t o David Hockney
  • 72. • I ncorrect geomet ries are possible and f un! • Geomet ry is not enough, we need scene analysis • A highly r elat ed wor k: "Scene Collages and Flexible Camera Arrays,” Y. Nomura, L. Zhang and S.K. Nayar, Eurographics Symposium on Rendering, J un, 2007. Take home
  • 73. Thank You
  • 74. 15-463 Class Proj ect f r om 2007 http://www.cs.cmu.edu/afs/andrew/scs/cs/1 5-463/f07/proj_final/www/echuangs/