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Multiple View Geometry

Multiple View Geometry. Ilan Shimshoni Dept of Management Information Systems University of Haifa ishimshoni@mis.haifa.ac.il. Examples of Applications. Visual robot navigation 3D reconstruction from video Was there a goal in a game from two video cameras?. What do we know?.

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Multiple View Geometry

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  1. Multiple View Geometry Ilan Shimshoni Dept of Management Information Systems University of Haifa ishimshoni@mis.haifa.ac.il

  2. Examples of Applications • Visual robot navigation • 3D reconstruction from video • Was there a goal in a game from two video cameras?

  3. What do we know? • Camera parameters • Camera position with respect to the scene • Relative camera position (known or constrained) • Objects in the scene • Their positions • Characteristics of objects in the scene • Nothing!!!

  4. The more we know the easier it is. • The more we need to know the harder it is to perform the experiment. • Example stereo reconstruction • Camera parameters known • Relative camera positions known and constrained • Goal: reconstruct scene. Find for each point in the first image its corresponding point in the second image. • How can we make the task easier?

  5. Another example: scene reconstruction from a video sequence • Camera parameters not known • Camera positions not known • Much easier for the user • Something in between: Scene reconstruction from a set of images taken by a plane (geodesic dept) • Position of plane known quite accurately • High quality camera which has been accurately calibrated

  6. How do we solve problems? • Example: Pose estimation • The algorithm is given: • A model of a 3D object • The geometric imaging parameters of the camera • An image of the object • Goal: • Calculate accurately the position of the camera with respect to the object

  7. Applications • A robot would like to grab an object with its gripper using a camera which is positioned on the robot • A robot would like to know where it is. The object is the room. • A satellite would like to know its orientation in space from an image it took of the stars. The object is the universe

  8. The input:

  9. Steps in developing an algorithm • Define f(X,x,) • Find a method to compute  such that f(X,x,) = 0 • What should we do if there is measurement noise in x? • How do we match x to X? • How do we deal with incorrect matches (outliers)? • Build a full system.

  10. Criminisi

  11. MIT city scanning project • Produce image hemispheres • Localize hemispheres using GPS • Match hemispheres using vision methods • Find accurate positions of cameras • Reconstruct buildings • Reconstruct fine details

  12. 3D reconstruction from image sequence

  13. Cipolla 2008

  14. Elections November 2008

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