[ICV3DR] - [2024Winter] - [en] - [Computer Vision: 3D Reconstruction]


Computer Vision: 3D Reconstruction [2024 SoSe]
Code
ICV3DR
Name
Computer Vision: 3D Reconstruction
CP
6
Duration
one semester
Offered
every winter semester
Format
Lecture 2 SWS + Exercise 2 SWS
Workload
180 h; thereof
30 h lectures
30 h exercises
20 h revision and home exercise
70 h programming a mini research project
30 h preparation of final report
Availability
M.Sc. Angewandte Informatik
M.Sc. Data and Computer Science
M.Sc. Scientific Computing
Language
English
Lecturer(s)
Carsten Rother
Examination scheme
Learning objectives The students
- Understand the principles behind estimating 3D Point Clouds and Motion from two or more images. They are able to apply this knowledge to new tasks in the field of 3D reconstruction.
- Understanding the principles of an image processing, the image formation process and corresponding Geometry. This can be utilized to design new algorithms, for e.g. 3D motion estimation for autonomous driving.
- Understand and implement methods that combine machine learning-based methods with classical computer vision-based techniques.
- Have studied various state-of-the-art computer vision systems and approaches, and are then able to evaluate and classify new systems and approaches.
- Understand and implement different approaches for object tracking and object-instance recognition.
Learning content This lecture covers areas of computer vision which deal with 3D reconstruction and scene understanding. This means, for instance, to recover a 3D scene from a set of photographs or video, or to extract and track objects in the scene. We discuss the underlying principles and methods to solve such tasks. In particular, we cover techniques from deep learning, traditional approaches, and mixtures of the two. We also introduce the necessary background knowledge, e.g. camera models, deep learning, image formation model, Kalmann Filters, etc.
Requirements for participation recommended is a basic machine learning background (e.g. Fundamentals of Machine Learning, Advanced Machine Learning or equivalent)
Requirements for the assignment of credits and final grade The module is completed with a graded examination. This examination is either a graded final report (about 10 pages) or an oral examination. The grade of this examination gives the grade for this module. Details for this examination as well as the requirements for the assignment of credits will be given by the lecturer an the beginning of this course.
Useful literature