| 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 | ||