3D-Szenenrekonstruktion aus Bildern Show URL Convert to PDF XML representation


Modulcode: Inf-CV
Englische Bezeichnung: Image-based 3D Scene Reconstruction
Modulverantwortliche(r): Prof. Dr.-Ing. Reinhard Koch
Turnus: unregelmäßig (WS16/17, SS18)
Präsenzzeiten: 4V 2Ü
Workload: 240 Std.
Dauer: ein Semester
Modulkategorien: IG (MSc Inf.) IS (MSc Inf.) MV (MSc Inf.) WI (MEd Inf) WPI (MEd Inf) WI (MSc Inf (15))
Lehrsprache: Englisch
Voraussetzungen: Inf-EinfBV


Teaching computers to see and to interpret the surrounding environment is an important step towards intelligent and autonomous systems. The goal of this lecture is to introduce the students into the concepts and algorithms of computer vision and 3D modelling for robotics, autonomous vehicles, drone vision and Augmented Reality

Computer vision methods in image sequence analysis are presented. Aim is the geometrical and visual surface reconstruction of 3-D objects as well as object tracking and camera motion tracking from image sequences.


The students learn to handle entities of projective geometry and image-based geometric transformations and implement these in the context of image-based 3-D scene reconstruction. Programming exercises are solved with the help of MATLAB and simple C++ examples in a dedicated framework.


The following topics are discussed:

  • Image sequence correspondence analysis
  • Basics of projective geometry
  • Homographies and panoramic images from rotating cameras
  • Multi-view geometry from a moving camera
  • Epipolar geometry and depth estimation
  • Camera tracking and pose estimation
  • Application in the field of augmented reality and image-based modeling

All lecture slides and course material will be in English. The lecture will be held in English if at least one student does not speak German. Otherwise the course students may choose to have the lecture language either in German or in English.

Weitere Voraussetzungen:

Basic mathematical knowledge from Bachelor courses in linear algebra, 3-D geometry and solving of linear equations. Basic knowledge in image processing, like the Bachelor lecture InfEinfBV (Introduction to Image Processing) is presupposed.


Written exam (120 Minutes)

Lehr- und Lernmethoden:



Szeliski, Rick: Computer Vision: Algorithms and Applications. Springer 12010. Elektronische Version: http://szeliski.org/Book/

Hartley, Zissermann: Multiple View Geometry, Cambridge 2003.

Schreer: Stereoanalyse und Bildsynthese, Springer 2004