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Visualizing Photo Coverage

Visualizing Photo Coverage. Phillip Isola 12/3/10. Big Idea . What would be possible if we could see sight ?. Big Idea . What would be possible if we could see sight ?. Big Idea . How can we effectively visualize the photographic coverage of a space. Background. Photo tourism

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Visualizing Photo Coverage

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  1. Visualizing Photo Coverage Phillip Isola 12/3/10

  2. Big Idea What would be possible if we could see sight?

  3. Big Idea What would be possible if we could see sight?

  4. Big Idea How can we effectively visualize thephotographic coverage of a space

  5. Background • Photo tourism • e.g. Snavely et al. 2007 • Camera + projector augmented reality • e.g. Raskar et al. 2003

  6. Background • Multi-camera tracking • e.g. Khan and Shah 2003 • Structure from motion • e.g. Mooser et al. 2009

  7. Possible approaches • Physical projection • Virtual environments • GPS, compass, etc • Image feature matching

  8. My current approach • Use blind camera calibration techniques to solve correspondence problem • Interpolate and render augmented reality coverage / modulation

  9. Algorithm Extract features Image processing KNN to generate candidate matches RANSAC on fundamental matrix Correspondence Triangulate Interpolate based on matches Smooth Interpolation and rendering

  10. Features Extract features SURF (Bay et al. 2008)

  11. KNN to generate candidate matches Correspondence Find highest probability set of unique matches Reject weak matches

  12. KNN to generate candidate matches Correspondence

  13. KNN to generate candidate matches Correspondence

  14. KNN to generate candidate matches Correspondence Matches in both directions agree, so probably fairly unambiguous

  15. RANSAC on fundamental matrix Correspondence

  16. RANSAC on fundamental matrix Epipolar geometry (From http://en.wikipedia.org/wiki/Epipolar_geometry)

  17. RANSAC on fundamental matrix RANSAC matches Reject matches that don’t well fit the epipolar constraints (fundamental matrix) Red box shows estimate of a slice of camera 1’s view frustum (using planar best fit)

  18. Triangulation Triangulate

  19. Interpolation Interpolate based on matches

  20. Smooth Rendering

  21. Multiple captures

  22. Non-planar geometry View 1 View 2

  23. Two cameras View 1 View 2

  24. Document scanning +

  25. Panorama completion

  26. Virtual laser pointer View 1 View 2

  27. Virtual paintbrush + + +

  28. Next steps • Virtual lights and projectors • Passive markers • Matches with internet • Guide photographers to unique shots or point out what was interesting in the past • Sikuli in the real world • ‘Program’ augmented reality systems by assigning functions to views (from:http://sikuli.org/)

  29. Summary • Photographs mark views of the world • They tell us that view is perhaps interesting, is definitely already captured • Cameras can be used as semantic projectors

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