3D Computer Vision Summer Term 2009
Administrative Info
Lecture by Prof. Nassir Navab
Exercises by Diana Mateus , Stefan Hinterstoisser and Loren Schwarz
Type: Lecture Module IN2057
Programs: Informatics (Bachelor, Master)
Informatics (Diploma, Wahlpflichtfach, Theoretische Informatik)
Information Systems (Bachelor), Computational Science and Engineering (Master)
Master Sports Engineering
SWS: 2+2
ECTS: 5 Credits
Course Language: English
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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
- For the intermediate exam, only one handwritten Din A4 (front+back) page with notes is allowed
- For the final exam, only one handwritten Din A4 page (front+back) with notes is allowed; calculators are allowed (however, computers are not allowed!)
- 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
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Site Content
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Announcements
The final exam results and the lecture grades have been e-mailed to everyone who has participated at least in the Midterm exam. If you did not receive an e-mail with your grades today, please contact us. If you want to see your final exam, stop by our office next week (17.08.-20.08.).
Those of you who were not able to participate in the final exam on July 28, please provide a doctor's note confirming your sickness. Drop us an e-mail and we will announce a date for the repetition exam.
Registration
Neither the class nor the exercises require any registration.
Homeworks
Students have exactly one week to solve the homework (Solutions must be sent before the next exercise session)
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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.
Lecture Content
- Intro, motivation & Overview
- 2D Transformations
- Projective 2D Geometry
- 3D Transformations
- Projective 3D Geometry
- Parameter Estimation
- Camera Models
- Camera Calibration
- Conclusion & Discussion
Lecture Schedule
Exercise Schedule
Exams
- Midterm Exam: Thursday, June 18
- Final Exam: Tuesday, July 28 in MW2050, at 10.30-12.00
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