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Multi-perspective Panoramas

Multi-perspective Panoramas. Slides from a talk by Lihi Zelnik-Manor at ICCV’07 3DRR workshop. Pictures capture memories. Panoramas. Registration: Brown & Lowe, ICCV’05 Blending: Burt & Adelson, Trans. Graphics,1983 Visualization: Kopf et al., SIGGRAPH, 2007. Bad panorama?.

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Multi-perspective Panoramas

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  1. Multi-perspective Panoramas Slides from a talk by Lihi Zelnik-Manor at ICCV’07 3DRR workshop

  2. Pictures capture memories

  3. Panoramas Registration: Brown & Lowe, ICCV’05 Blending: Burt & Adelson, Trans. Graphics,1983 Visualization: Kopf et al., SIGGRAPH, 2007

  4. Bad panorama? Output of Brown & Lowe software

  5. No geometrically consistent solution

  6. Scientists solution to panoramas:Single center of projection No 3D!!! Registration: Brown & Lowe, ICCV’05 Blending: Burt & Adelson, Trans. Graphics,1983 Visualization: Kopf et al., SIGGRAPH, 2007

  7. From sphere to plane Distortions are unavoidable

  8. Distorted panoramas Actual appearance Output of Brown & Lowe software

  9. Objectives • Better looking panoramas • Let the camera move: • Any view • Natural photographing

  10. Stand on the shoulders of giants Cartographers Artists

  11. Cartographic projections

  12. φ θ Common panorama projections Perspective Stereographic Cylindircal

  13. Global Projections Perspective Stereographic Cylindircal

  14. Sharp discontinuity perspective perspective Learn from the artists Multiple view points De Chirico “Mystery and Melancholy of a Street”, 1914

  15. Renaissance painters solution “School of Athens”, Raffaello Sanzio ~1510 Give a separate treatment to different parts of the scene!!

  16. Personalized projections “School of Athens”, Raffaello Sanzio ~1510 Give a separate treatment to different parts of the scene!!

  17. Multiple planes of projection Sharp discontinuities can often be well hidden

  18. Single view Our multi-view result

  19. Single view Our multi-view result

  20. Single view Our multi-view result

  21. Applying personalized projections Input images Foreground Background panorama

  22. Single view Our multi-view result

  23. Single view Our multi-view result

  24. Objectives - revisited • Better looking panoramas • Let the camera move: • Any view • Natural photographing Multiple views can live together

  25. Multi-view compositions 3D!! David Hockney, Place Furstenberg, (1985)

  26. Why multi-view? Multiple viewpoints Single viewpoint David Hockney, Place Furstenberg, 1985 Melissa Slemin, Place Furstenberg, 2003

  27. Multi-view panoramas Single view Multiview Zomet et al. (PAMI’03) Requires video input

  28. Long Imaging Agarwala et al. (SIGGRAPH 2006)

  29. Smooth Multi-View Google maps

  30. What’s wrong in the picture? Google maps

  31. Non-smooth Google maps

  32. The Chair David Hockney (1985)

  33. Joiners are popular Flickr statistics (Aug’07): 4,985 photos matching joiners. 4,007 photos matching Hockney. 41 groups about Hockney Thousands of members

  34. Main goals: Automate joinersGeneralize panoramas to general image collections

  35. Objectives • For Artists:Reduce manual labor Fully automatic Manual: ~40min.

  36. Objectives • For Artists:Reduce manual labor • For non-artists:Generate pleasing-to-the-eye joiners

  37. Objectives • For Artists:Reduce manual labor • For non-artists:Generate pleasing-to-the-eye joiners • For data exploration:Organize images spatially

  38. What’s going on here?

  39. A cacti garden

  40. Principles

  41. Principles • Convey topology Correct Incorrect

  42. Principles • Convey topology • A 2D layering of images Blending: blurry Graph-cut: cuts hood Desired joiner

  43. Principles • Convey topology • A 2D layering of images • Don’t distort images translate rotate scale

  44. Principles • Convey topology • A 2D layering of images • Don’t distort images • Minimize inconsistencies Bad Good

  45. Algorithm

  46. Step 1: Feature matching Brown & Lowe, ICCV’03

  47. Step 2: Align Large inconsistencies Brown & Lowe, ICCV’03

  48. Step 3: Order Reduced inconsistencies

  49. Ordering images Try all orders: only for small datasets

  50. Ordering images Try all orders: only for small datasets complexity: (m+n)m = # imagesn = # overlaps = # acyclic orders

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