3D Computer Vision



Important. This is not a block course. First lecture is Monday 15.4 9:30 - 11:00 in Math. B/128

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.

Content:
- Brief introduction to required Machine Learning concepts (Neural Networks, Invertible Networks, etc)
- Basic Image processing (Filtering, Bilateral Filter)
- Sparse feature detection and description (points, edges, LIFT) w/ and w/o Neural networks
- Projective Geometry, Epipolar Geometry
- Sparse reconstruction (image matching, image descriptors) w/ and w/o Neural Networks
- Robust matching w/ Neural networks (Differentiable RASANC and related pipelines)
- Dense Reconstruction, SLAM and Camera Localization
- 3D Object detection (End-to-End Trainable Pipelines) w/ and w/o Neural Networks
- 3D Object tracking (6D Pose estimation, Kalman Filter, Particle Filter)
- 3D Training data generation and Instance Segmentation
- Poster Session Presentation by the students


Formalities:
Teaching assistant (main point of contact): Titus Leistner titus.leistner@iwr.uni-heidelberg.de

Registration: Please register yourself via Müsli (https://muesli.mathi.uni-heidelberg.de/lecture/view/974). If you have problems please contact teaching assistant.

Prerequisite: no prerequisites, but it is recommended to have Machine Learning Background, e.g. Fundamentals of Machine Learning or equivalent

Exam: graded final report (about 10 pages). The final report has to be submitted until 15.10.2019 (please send an email to Titus Leistner titus.leistner@iwr.uni-heidelberg.de if you need an extension of this deadline. Please explain why you need the extension.)

Leistungspunkte: 6 LP

Amount of work: 180h thereof 30h Lectures, 30h Exercises, 20h Revision and Home Exercise, 70h programming a mini research project, and 30h preparation of final report.
(Comment, the mini research project will start at the end of lecture).

Usability: Physics, MSc. Angewandte Informatik MSc. Scientific Computing

Slides
All the slides are uploaded to heiBOX.
The password will be disclosed in the second lecture.

Schedule
April 15Lecture 1
April 18Lecture 2
April 22Holiday
April 25Deep Learning Exercise 1
April 29Lecture 3
May 2Lecture 4
May 6Lecture 5
May 9Lecture 6
May 13Lecture 7
May 16Deep Learning Exercise 2
May 20Lecture 8
May 23Deep Learning Exercise 3
May 27Lecture 9
May 29Holiday
June 3Lecture 10
June 6DSAC Exercise 1
June 10Holiday
June 13DSAC Exercise 2
June 17Lecture 11
June 20Holiday
June 24Lecture 12
June 27-
July 1Lecture 13
July 4Tracking Exercise 1
July 8Lecture 14
July 11Exercise Tracking 2
July 15Lecture 15
July 18-
July 22Mini Project Discussion
July 25Last Chance for Exercise Submissions

Teaching goals:
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 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.

Information about when and where the course takes place, can be found here: https://hci.iwr.uni-heidelberg.de/teaching