Module: 3D Computer Vision
|3D Computer Vision||Lecture||2||Winter Semester|
|3D Computer Vision||Recitation Section (small)||2||Winter Semester|
Prof. Rolf-Rainer Grigat
Recommended Previous Knowledge:
- Knowlege of the modules Digital Image Analysis and Pattern
Recognition and Data Compression are used in the practical
- Linear Algebra (including PCA, SVD), nonlinear optimization
(Levenberg-Marquardt), basics of stochastics and basics of Matlab
are required and cannot be explained in detail during the
Students can explain and describe the field of projective geometry.
Students are capable of
- Implementing an exemplary 3D or volumetric analysis task
- Using highly sophisticated methods and procedures of the subject area
- Identifying problems and
- Developing and implementing creative solution suggestions.
With assistance from the teacher students are able to link the contents of the three subject areas (modules)
- Digital Image Analysis
- Pattern Recognition and Data Compression
- 3D Computer Vision
in practical assignments.
Students can collaborate in a small team on the practical realization and testing of a system to reconstruct a three-dimensional scene or to evaluate volume data sets.
Students are able to solve simple tasks independently with reference to the contents of the lectures and the exercise sets.
Students are able to solve detailed problems independently with the aid of the tutorial’s programming task.
ECTS-Credit Points Module:
Workload in Hours:
Independent Study Time: 124, Study Time in Lecture: 56
Course: 3D Computer Vision
- Projective Geometry and Transformations in 2D und 3D in
- Projection matrix, calibration
- Epipolar Geometry, fundamental and essential matrices, weak calibration, 5 point algorithm
- Homographies 2D and 3D
- Trifocal Tensor
- Correspondence search
- Skriptum Grigat/Wenzel
- Hartley, Zisserman: Multiple View Geometry in Computer Vision. Cambridge 2003.