3D Computer Vision I Winter Term 2007

Administrative Info Lecture by Prof. Nassir Navab Exercises by Dr. Selim Benhimane and Hauke Heibel 2+2 SWS, 5 ECTS, Theoretische Informatik, *Wahlpflichtfach* Note that the class is equally suited for bachelor, master and diploma students.

Time, Location & Requirements Tuesday 10:30 - 12:00 MI 03.13.010 Exercises: Thursday 13:00 - 14:30 MI 03.13.010 Requirements: * The classes and exam are in English * The final exam is written and only notes are allowed (no book) * The final exam contains 100 points, you need to have 50 points to pass it * Up to 40 bonus points can be earned from the homework and the intermediate exam

Site Content Overview Content Tuesdays Schedule (C: Lecture, E: Exercise, T: Test) Thursdays Schedule (C: Lecture, E: Exercise, T: Test) Readings

Announcements For the intermediate exam everybody is allowed to use an A4 paper with private notes (it can be written on both sides). Calculators are not allowed - you won't need them anyways. Homework Note For any homework everybody has exactly one week time to come up with a solution. If you get your exercise sheet on Thursday the 22nd around 1 o'clock your solution should be ready by Thursday the 29th at 1 o'clock. Actually you should have already sent them to us before you are coming to the next exercise.

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 modeling 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")

The fundamental mathematics and a profound comprehension of the basics of projective geometry as well as one-view geometry are the core of the lecture 3D Computer Vision.

Content

1. Intro, motivation & Overview
2. 2D Transformations
3. Projective 2D Geometry
4. 3D Transformations
5. Projective 3D Geometry
6. Parameter Estimation
7. Camera Models
8. Camera Calibration
9. Conclusion & Discussions

Tuesdays Schedule (C: Lecture, E: Exercise, T: Test)

Date	Type	Conducted by	Topic	Material/Literature
Tue, Oct 16, 2007	C	Prof. Nassir Navab	Introduction & Motivations
Tue, Oct 23, 2007	C	Prof. Nassir Navab	Transformations and 2D Projective Geometry	3D_CV_WT_2007_Lecture_02.pdf
Tue, Oct 30, 2007	E	Dr. Selim Benhimane	Demo, Matlab introduction, Homogeneous Coordinates, Vector Notation	MATLABWorkshop.pdf exercise01.pdf solution01.pdf
Tue, Nov 06, 2007	*	Stefanie Demirci	Field trip to Siemens Medical Solutions	Please until Thursday, Oct. 25, 2007.
Tue, Nov 13, 2007	E	Hauke Heibel	2D Projective Transformations & Conics	Cancelled due to SVV! Please nonetheless do the homework marked by (H) on the exercise sheet 2 (exercise02.pdf). Due date is Thursday the 22nd of November 2007. solution02.pdf
Tue, Nov 20, 2007	C	Prof. Nassir Navab	3D Projective Transformations
Tue, Nov 27, 2007	C	Prof. Nassir Navab	Parameter Estimation: Linear estimation, DLT, Cost Functions	3D_CV_WT_2007_Lecture_04.pdf
Tue, Dec 04, 2007	C	Prof. Nassir Navab	Parameter Estimation: Sampson Error, Statistical Cost Function
Tue, Dec 11, 2007	C	Dr. Selim Benhimane	Non-linear Parameter Estimation	3D_CV_WT_2007_Lecture_05.pdf
Tue, Dec 18, 2007	C	Prof. Nassir Navab	Algorithm Evaluation & Error Analysis
Tue, Dec 25, 2007	-	Christmas Holidays
Tue, Jan 01, 2008	-	Christmas Holidays
Tue, Jan 08, 2008	C	Prof. Nassir Navab	Projection Matrix	3D_CV_WT_2007_Lecture_06.pdf
Tue, Jan 15, 2008	C	Prof. Nassir Navab	Computation of the Projection Matrix
Tue, Jan 22, 2008	C	Prof. Nassir Navab	Camera Calibration	3D_CV_WT_2007_Lecture_07.pdf
Tue, Jan 29, 2008	C	Prof. Nassir Navab	Camera Distortion Calibration
Tue, Feb 05, 2008	T	Dr. Selim Benhimane	Final Exam

Thursdays Schedule (C: Lecture, E: Exercise, T: Test)

Date	Type	Conducted by	Topic	Exercises	Solutions
Thu, Oct 18, 2007	C	Prof. Nassir Navab	Basic Transformations & 2D Projective Geometry	3D_CV_WT_2007_Lecture_01.pdf
Thu, Oct 25, 2007	C	Prof. Nassir Navab	2D Projective Geometry	3D_CV_WT_2007_Lecture_03.pdf
Thu, Nov 01, 2007	-	Allerheiligen
Thu, Nov 08, 2007	C	Prof. Nassir Navab	3D Projective Geometry
Thu, Nov 15, 2007	E	Hauke Heibel	3D Projective Transformations	exercise03.pdf solution03.pdf
Thu, Nov 22, 2007	E	Hauke Heibel	Cross Ratio Computation and Applications	exercise04.pdf solution04.pdf
Thu, Nov 29, 2007	E	Hauke Heibel	Homography estimation	exercise05.pdf solution05.pdf
Thu, Dec 06, 2007	-	Dies Academicus
Thu, Dec 13, 2007	T	Hauke Heibel	Intermediate Exam See the Announcement above
Thu, Dec 20, 2007	E	Hauke Heibel	Intermediate Exam Correction	The results of your exam can be requested via e-mail (heibel@cs.tum.edu).
Thu, Dec 27, 2007	-	Christmas Holidays
Thu, Jan 03, 2008	-	Christmas Holidays
Thu, Jan 10, 2008	E	Dr. Selim Benhimane	Conics, Non-linear Parameter Estimation	exercise06.pdf solution06.pdf (updated) Levenberg Marquardt Method Homework deadline extended to the 24th of January. scriptGold.m jacobianReprojection.m
Thu, Jan 17, 2008	E	Dr. Selim Benhimane	Mosaicing, Projection matrices	exercise07.pdf solution07.pdf (updated)
Thu, Jan 24, 2008	E	Dr. Selim Benhimane	Error Propagation	exercise08.pdf solution08.pdf im1.pgm im2.pgm error_ellipse.m errorPropagation.m jacobianPropagation.m
Thu, Jan 31, 2008	E	Dr. Selim Benhimane	Self-calibration of the distortion of a zooming camera	exercise09.pdf solution09.pdf

Readings

• Primary Reading
  • 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