3D Form and Motion: Difference between revisions

From RedwoodCenter
Jump to navigationJump to search
No edit summary
No edit summary
Line 9: Line 9:
* Seitz google talk on history of 3D computer vision: [https://www.youtube.com/watch?v=kyIzMr917Rc URL]
* Seitz google talk on history of 3D computer vision: [https://www.youtube.com/watch?v=kyIzMr917Rc URL]
** Blanz and Vetter, 3D face models [http://redwood.berkeley.edu/bruno/3DFM/Blanz-siggraph-99.pdf pdf]
** Blanz and Vetter, 3D face models [http://redwood.berkeley.edu/bruno/3DFM/Blanz-siggraph-99.pdf pdf]
** Tomasi and Kanade, factorization of shape and motion, [[http://redwood.berkeley.edu/bruno/3DFM/TomasiKanade92.pdf
** Tomasi and Kanade, factorization of shape and motion, [http://redwood.berkeley.edu/bruno/3DFM/TomasiKanade92.pdf
pdf]
pdf]



Revision as of 00:37, 25 March 2013

Mondays at 1:00, Evans 560

3D models from images:

  • Hartley and Zisserman: Multiple View Geometry (Amazon)
  • Hoiem and Savarese: 3D object recognition and scene interpretation, book pdf
  • Hoiem Ph.D. thesis "SEEING THE WORLD BEHIND THE IMAGE" pdf
  • Cashman and Fitzgibbon: "What Shape are Dolphins? Building 3D Morphable Models from 2D Images" pdf
  • Snavely and Seitz (2006), "Photo tourism" (aka photosynth), pdf
  • Seitz google talk on history of 3D computer vision: URL

pdf]

SLAM:

  • wiki page with list of refs
  • Thrun, "Probabilistic algorithms in robotics" pdf
  • Daniel Cremers work on helicopters/SLAM (TU Munich) URL
  • Newcombe and Davison, "Live dense reconstruction with a single moving camera work on fast single camera", pdf

Psychophysics:

  • Nakayama et al. (1995) "Visual Surface Representation" pdf
  • Glennerster and Fitzgibbon, "View-Based Approaches to Spatial Representation in Human Vision" pdf
  • Wexler work on depth from self-motion/parallax, reference frames, pdf1, pdf2

Action-perception:

  • Philipona et al. Neural Computation (2003) pdf
    • Follow on papers URL
  • Philipona et al. NIPS (2003) pdf
    • Follow on papers URL
  • Robotics paper trying to perceive environment based on sensiromotor loop (from Pulkit): pdf

Manifolds:

  • Manifolds in computer vision tutorial: pdf
    • references: URL