Seminar: Computer Vision and Deep Learning for Autonomous Driving
PD Dr. Federico Tombari
, Nikolas Brasch
, Shun-Cheng Wu
, Mahdi Saleh
Seminar Module IN0014
- You can submit your motivation letter via email.
- Preliminary Meeting: *February 1st, 10:00-10:30am, slides
- The seminar includes a selection of the most recent and relevant papers in the field of computer vision and deep learning aimed at autonomous driving.
- Papers are selected to cover different aspects of the topic:
- Semantic Segmentation
- 3D Scene Understanding
- Multi-modal sensor fusion
- SLAM and 3D reconstruction
- 6D object pose estimation / 3D object recognition
- Joint depth and visual odometry prediction
- Synthetic dataset creation
- Generative models for sym-to-real
- Semi-supervised large-scale dataset annotations
- Trajectory planning and lane change prediction
- The full list of papers will be available a few weeks before the beginning of the semester.
In this Master Seminar (formerly Hauptseminar), each student is asked to select one paper from a list. In order to successfully complete the seminar, participants have to fulfill these requirements:
- Presentation: The selected paper is presented to the other participants (20 minutes presentation 10 minutes questions). Use the CAMP templates for PowerPoint camp-tum-jhu-slides.zip, or Latex: CAMP-latex-template.
- Blog Post: Will be clarified in the first session.
- Attendance: Participants have to participate actively in all seminar sessions.
The students are required to attend each seminar presentation which will be held during this course. Each presentation is followed by a discussion and everyone is encouraged to actively participate. The blog post must include all references used and must be written completely in your own words
. Copy and paste will not be tolerated. Both blog post and presentation have to be done in English
List of Topics and Material
A list of papers will be announced here.
The schedule for the paper presentations will be announced here during the semester. Each student is required to attend all presentations.