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Multi-Perspective Panoramas

Dive into the world of multi-perspective panoramas and computational photography with Alexei Efros from UC Berkeley. Learn the art of creating better-looking panoramas that allow the camera to capture any view naturally. Explore cartographic projections, personalized projections, and multiple planes of projection, drawing inspiration from renowned artists like De Chirico and Raffaello Sanzio. Discover the power of multi-view compositions and the significance of utilizing multiple viewpoints in image manipulation. Automate joiners and generalize panoramas to streamline the process for artists and non-artists alike. Uncover the principles of conveying topology, layering images, and minimizing distortions to create visually pleasing joiners. Delve into algorithmic steps for feature matching, alignment, ordering images, and iterative refinement to achieve impeccable results in your panoramic imagery endeavors.

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Multi-Perspective Panoramas

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  1. Multi-Perspective Panoramas CS194: Image Manipulation & Computational Photography Alexei Efros, UC Berkeley, Fall 2017 Slides from Lihi Zelnik

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

  3. Stand on the shoulders of giants Cartographers Artists

  4. Cartographic projections

  5. φ θ Common panorama projections Perspective Stereographic Cylindircal

  6. Global Projections Perspective Stereographic Cylindircal

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

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

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

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

  11. Single view multi-view result

  12. Single view multi-view result

  13. Single view multi-view result

  14. Single view multi-view result

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

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

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

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

  19. Smooth Multi-View Google maps

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

  21. Non-smooth Google maps

  22. The Chair David Hockney (1985)

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

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

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

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

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

  28. What’s going on here?

  29. A cacti garden

  30. Principles

  31. Principles • Convey topology Correct Incorrect

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

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

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

  35. Algorithm

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

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

  38. Step 3: Order Reduced inconsistencies

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

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

  41. Ordering images Observations: • Typically each image overlaps with only a few others • Many decisions can be taken locally

  42. Ordering images Approximate solution: • Solve for each image independently • Iterate over all images

  43. Can we do better?

  44. Step 4: Improve alignment

  45. Iterate Align-Order-Importance

  46. Iterative refinement Initial Final

  47. Iterative refinement Initial Final

  48. Iterative refinement Initial Final

  49. What is this?

  50. That’s me reading

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