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Advanced Techniques in 3D Reconstruction from Multiple Views: Applications and Examples

This research explores methods for 3D reconstruction from multiple camera angles, focusing on camera parameters, object positions, and scene characteristics. Examples include robot navigation, stereo reconstruction, and scene reconstruction. Practical applications include robot object manipulation, spatial orientation in space, and pose estimation.

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Advanced Techniques in 3D Reconstruction from Multiple Views: Applications and Examples

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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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