TeachingWs04CV

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

3D Computer Vision

by Nassir Navab
exercises by Darko Zikic, Hauke Heibel, and Martin Groher

4+2 SWS
Theoretische Informatik
Wahlpflichtfach

Announcements

  • Invited Talk by Dr. Mirko Appel (Siemens Corporate Technology, München-Perlach):

    Combined Orthographic and Perspective (COP) Images: Augmentation, Calibration & Reconstruction

  • Exam
    • Thursday, 10th of February, 5.00PM - 6.30PM (Hörsaal 1!!)
      The exam will be partly closed, partly open book, so take your notes / textbooks with you. Moreover, you may use a basic calculator.

  • Invited Talk by Prof. G. Hager (Johns Hopkins University, MD, USA):

    Direct Methods for Visual Tracking: Basic Principles and Recent Progress

    Abstract:
    Over the last decade, direct methods for motion recovery have become widely used as a basis for visual tracking. In this talk, I will first review the basic principles of direct methods for motion tracking. Having done so, I will describe in some detail more recent work on direct methods for deformable stereo surfaces, and robotic tool tracking in endoscopic video. I will finally discuss a method of unifying many tracking methods, including direct methods, using kernel-based motion tracking methods.

  • Thanks a lot to all of you who participated in the poll! The results can be viewed here.

Overview

Making a computer see was something that leading experts in the field of Artificial Intelligence thought to be at the level of difficulty of a summer student's project back in the sixties. Forty years later the task is still unsolved and seems formidable. A whole field, called Computer Vision, has emerged as a discipline in itself with strong connections to mathematics and computer science and looser connections to physics, the psychology of perception and the neuro sciences.

Over the past decade there has been a rapid development in the understanding and modelling of the geometry of multiple views in computer vision. The theory and practice have now reached a level of maturity where excellent results can be achieved for problems that were unsolved a decade ago, and often thought unsolvable. These tasks and algorithms include problems like:
Given two/three/multiple images, and no further information, compute/estimate:

  • matches between the images
  • the 3D position of the points that generate these matches
  • the cameras that generate the images

adapted form Hartley & Zisserman's "Multiple View Geometry in Computer Vision"

This tasks and algorithms as well as the methods which allow us to reason about the quality of our results are the core of the lecture 3D Computer Vision.

Time & Location

  • Lecture
    • Tuesday 10:30AM-12:00 s.t. (MI 00.13.009A)
    • Thursday 12:00-13:30PM s.t. (MI 00.13.009A)

  • Exercise
    • Thursday 3:15PM-4:45PM c.t. (MI 00.13.009A)

Topics

  1. Intro, motivation & Overview
  2. 2D Transformations
  3. Projective 2D Geometry
  4. 3D Transformations
  5. Projective 3D Geometry
  6. Camera Models
  7. Camera Calibration
  8. Single View Geometry
  9. Stereo Vision I – Image Rectification & Disparity maps
  10. Linear Algebra I - Singular Value Decomposition
  11. 3D reconstruction I: Factorization Method
  12. Epipolar Geometry : Essential & Fundamental Matrices
  13. Stereo Vision II – 3D Reconstruction
  14. Planes & Homographies
  15. Basics of Image Mosaicing
  16. Representation and Motion of Lines & Cylinders
  17. Three View Geometry of Lines & Cylinders
  18. The Trifocal Tensor
  19. 3D reconstruction II: Multi-view Reconstruction
  20. 3D reconstruction III: Motion and Structure from Motion
  21. Mixed Camera Models (Perspective and Orthographic)
  22. Combined Othrographic and Perspective (COP) Images: Calibration & Reconstruction
  23. 3D reconstruction IV: Fusion of 3D Maps (ICP)
  24. Image-based Rendering I
  25. Image-based Rendering II
  26. Conclusion & Discussions

Course Schedule

Oct 19, 2004 Intro & Motivation  
Oct 21, 2004 Projective Geometry in 2D Slides
Oct 26, 2004 Projective Geometry in 3D / 1D Slides
Oct 28, 2004 Projective Geometry Summary / Conics and Quadrics Slides
Nov 02, 2004 Siemens Field Trip  
Nov 04, 2004 1st Intermediate Test, Multivariate Functions Reminder Results of the Intermediate Test, Multivariate Calculus Reminder
Nov 09, 2004 Parameter Estimation: Linear Estimation Methods (DLT) Slides
Nov 11, 2004 Parameter Estimation: Statistical error models Slides
Nov 16, 2004 No Class Today (SVV)  
Nov 18, 2004 Parameter Estimation: Sampson Error, normalization Slides
Nov 23, 2004 Error Analysis Slides
Nov 25, 2004 Non-linear Parameter Estimation Slides
Nov 30, 2004 Camera Models Slides
Dec 02, 2004 No Lecture (Dies Academicus)  
Dec 07, 2004 Camera Models II Slides
Dec 09, 2004 2nd Intermediate Test (the not so easy one) Results of the 2nd Intermediate Test
Dec 14, 2004 Invited Talk: Direct Methods for Visual Tracking: Basic Principles and Recent Progress
Prof. G. Hager (Johns Hopkins University, MD, USA)
Slides
Dec 16, 2004 Computation of the Projection Matrix Slides
Dec 21, 2004 Epipolar Geometry Slides
Dec 23, 2004 no class: Merry Christmas!!  
Jan 11, 2004 Epipolar Geometry II Slides
Jan 13, 2004 Epipolar Geometry III Slides
Jan 18, 2004 Epipolar Geometry IV Slides
Jan 20, 2004 Invited Talk: Combined Orthographic and Perspective (COP) Images: Augmentation, Calibration & Reconstruction
Dr. Mirko Appel (Siemens Corporate Technology)
Slides
Jan 25, 2004 The Trifocal Tensor I, Plücker Line Coordinates Slides
Jan 27, 2004 The Trifocal Tensor II, Cylinders Slides , Slides on Cylinders
Feb 01, 2004 The Trifocal Tensor III Slides
Feb 03, 2004    
Feb 08, 2004    
Feb 10, 2004    

Exercises

Oct 21, 2004 MatLab, Homogeneous Coordinates Darko Zikic & Martin Groher Exercise Sheet 1 MatLab Worksheet , 2D Projective Space Handout , Conics & Quadrics Basics Solution for Exercise Sheet 1
Oct 28, 2004 Transformations, Conics, Quadrics Darko Zikic & Martin Groher Exercise Sheet 2 MatLab Worksheet Solution for Exercise Sheet 2
Nov 4, 2004 Multivariate Functions, Affine Rectification, SVD Darko Zikic & Martin Groher Exercise Sheet 3 SVD Basics  
Nov 11, 2004 Parameter Estimation, Error Models Martin Groher Exercise Sheet 4   Solution for Exercise Sheet 4
Nov 18, 2004 Covariance, Normalization of DLT, Mosaicing Darko Zikic Exercise Sheet 5 Probability and Statistics Reminder,
nice example for mosaicing: Spiegel Online Article
 
Nov 25, 2004 Nonlinear Estimation and Error Propagation Martin Groher Exercise Sheet 6 JacobianReprojection.pdf Solution for Exercise Sheet 6
Dec 2, 2004 no exercises (Dies Academicus)        
Dec 9, 2004 Invited Talk: ARToolkit (Martin Bauer),
Camera Models / Calibration
Martin Groher Exercise Sheet 7 exercise06e3.m Solution for Exercise Sheet 7
Dec 16, 2004 Camera Calibration Martin Groher Exercise Sheet 8 Paper of Zhengyou Zhang Solution to Exercise Sheet 8
Dec 23, 2004 no exercises: Merry Christmas!        
Jan 13, 2004 Camera Calibration II, Radial Lens Distortion Martin Groher Exercise Sheet 9   Solution to Exercise Sheet 9
Jan 20, 2004 Mono- and Stereo Image Rectification Hauke Heibel   Slides  
Jan 27, 2004 The Absolute Conic Martin Groher Exercise Sheet 10    
Feb 3, 2004          
Feb 10, 2004          

Programming Assignment

  • Pix
    • here are some test images. some are more suitable than others. but all quite boring. so feel encouraged to make your own images. the cooler the better...

       

Readings

  • Primary Readings
    • Multiple View Geometry in Computer Vision; by Richard Hartley, Andrew Zisserman
  • General Introduction to 3D Computer Vision
    • Three-Dimensional Computer Vision; by Olivier Faugeras
    • Computer Vision: A Modern Approach; by David A. Forsyth & Jean Ponce
    • Introductory Techniques for 3-D Computer Vision; by Emanuele Trucco & Alessandro Verri
  • More Specific Readings
    • The Geometry of Multiple Images: The Laws That Govern the Formation of Multiple Images of a Scene and Some of Their Applications; by Olivier Faugeras, Quang-Tuan Luong, Theodore H. Papadopoullos; MIT Press; 2001

TeachingForm
Title: 3D Computer Vision
Professor: Nassir Navab
Tutors: Martin Groher, Darko Zikic, Hauke Heibel
Type: Lecture
Information: 4+2 SWS, Theoretische Informatik, Wahlpflichtfach
Term: 2004WiSe
Abstract:  


Edit | Attach | Refresh | Diffs | More | Revision r1.65 - 29 Jun 2005 - 11:50 - DarkoZikic

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