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Convergence of vision and graphics

Convergence of vision and graphics. Jitendra Malik University of California at Berkeley. Overview. 3D capture:. Modeling, simulation. Rendering. Display. Applications: Simulation Virtual Reality Remote collaboration. Graphics and Vision.

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Convergence of vision and graphics

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  1. Convergence of vision and graphics Jitendra Malik University of California at Berkeley

  2. Overview 3D capture: Modeling, simulation Rendering Display Applications: Simulation Virtual Reality Remote collaboration

  3. Graphics and Vision • Computer graphics is the forward problem: given scene geometry, reflectances and lighting, synthesize an image. • Computer vision must address the inverse problem: given an image/multiple images, reconstruct the scene geometry, reflectacnes and illumination.

  4. Image-based Modeling Recover Models of Real World Scenes and Make Possible Various Visual Interactions • Vary viewpoint • Vary lighting • Vary scene configuration

  5. Image-based Modeling • 1st Generation---- vary viewpoint but not lighting • Acquire photographs • Recover geometry (explicit or implicit) • Texture map

  6. Recovering geometry • Historical roots in photogrammetry and analysis of 3D cues in human vision • Single images adequate given knowledge of object class • Multiple images make the problem easier, but not trivial as corresponding points must be identified.

  7. Arc de Triomphe

  8. The Taj Mahal Taj Mahal modeled from one photograph by G. Borshukov

  9. Campus Model of UC Berkeley Campanile + 40 Buildings (Debevec et al)

  10. Image-based Modeling • 2nd Generation---- vary viewpoint and lighting • Recover geometry & reflectance properties • Render using light transport simulation or local shading Original Lighting & Viewpoint Novel Lighting & Viewpoint

  11. Inverse Global Illumination (Yu et al) Reflectance Properties Radiance Maps Light Sources Geometry

  12. Real vs. Synthetic

  13. Real vs. Synthetic

  14. Image-based Modeling • 3rd Generation--Vary spatial configurations in addition to viewpoint and lighting Novel Viewpoint Novel Viewpoint & Configuration

  15. Input Multiple range scans of a scene Multiple photographs of the same scene Output Geometric meshes of each object in the scene Registered texture maps for objects Our Framework

  16. Segmentation: From images to objects

  17. Segmentation Results[Yu, Ferencz and Malik ’00]

  18. Models of Individual Objects

  19. Texture-Mapping and Object Manipulation

  20. Image Based modeling for motion capture Body Suits, Markers Video Motion Capture

  21. Eadweard Muybridge [Bregler and Malik ’98]

  22. Continuing Challenges • Finding correspondences automatically • Optimal estimation of structure from n views under perspective projection • Models of reflectance and texture for natural materials and objects

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