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

  9. 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

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

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

  12. 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

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

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

  15. Venice Sparse Model

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