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Announcements

Announcements. Readings Seitz et al., A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR 2006, pp. 519-526 http://vision.middlebury.edu/mview/seitz_mview_cvpr06.pdf. Multi-view Stereo. Apple Maps. CMU’s 3D Room. Point Grey ’s ProFusion 25. Multi-view Stereo.

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Announcements

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  1. Announcements • Readings • Seitz et al., A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR 2006, pp. 519-526 • http://vision.middlebury.edu/mview/seitz_mview_cvpr06.pdf

  2. Multi-view Stereo Apple Maps

  3. CMU’s 3D Room Point Grey’s ProFusion 25 Multi-view Stereo Point Grey’s Bumblebee XB3

  4. Multi-view Stereo

  5. Multi-view Stereo Input: calibrated images from several viewpoints Output: 3D object model Figure by Carlos Hernandez

  6. Fua Seitz, Dyer Narayanan, Rander, Kanade Faugeras, Keriven 1995 1997 1998 1998 Goesele et al. Hernandez, Schmitt Pons, Keriven, Faugeras Furukawa, Ponce 2004 2005 2006 2007

  7. error depth Stereo: basic idea

  8. all of these points project to the same pair of pixels width of a pixel Choosing the stereo baseline What’s the optimal baseline? • Too small: large depth error • Too large: difficult search problem Large Baseline Small Baseline

  9. The Effect of Baseline on Depth Estimation

  10. pixel matching score z z width of a pixel width of a pixel

  11. Multibaseline Stereo Basic Approach • Choose a reference view • Use your favorite stereo algorithm BUT • replace two-view SSD with SSSD over all baselines Limitations • Only gives a depth map (not an “object model”) • Won’t work for widely distributed views:

  12. Problem: visibility • Some Solutions • Match only nearby photos • Ignore SSD values > threshold • Alternative to SSD

  13. merged surface mesh set of depth maps (one per view) Merging Depth Maps vrip [Curless and Levoy 1996] • compute weighted average of depth maps

  14. Merging depth maps Naïve combination (union) produces artifacts Better solution: find “average” surface • Surface that minimizes sum (of squared) distances to the depth maps depth map 1 Union depth map 2

  15. E ( ∫ N x ( 2 , f f ) ) = dx d ∑ i i = 1 Least squares solution

  16. VRIP [Curless & Levoy 1996] depth map 1 combination depth map 2 isosurface extraction signed distance function

  17. Merging Depth Maps: Temple Model 16 images (ring) 317 images (hemisphere) input image 47 images (ring) ground truth model Goesele, Curless, Seitz, 2006 Michael Goesele from [Seitz et al., 2006] temple: 159.6 mm tall

  18. From Sparse to Dense Sparse output from the SfM system

  19. From Sparse to Dense Furukawa, Curless, Seitz, Szeliski, CVPR 2010

  20. Venice Sparse Model

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