Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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Seminar: Computer Vision and Deep Learning for Autonomous Driving

PD Dr. Federico Tombari, Helisa Dhamo, Shun-Cheng Wu, Yida Wang, Yanyan Li
Type: Seminar Module IN0014
SWS: 2+0
ECTS: 5 Credits
Location: CAMP Seminar Room, 03.13.010
Time: tba
Course Language: English



  • 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:
    • (Lightweight) Semantic Segmentation
    • Scene Understanding
    • Multi-modal sensor fusion
    • SLAM and 3D reconstruction
    • Joint depth and visual odometry prediction
    • Synthetic dataset creation
    • Car simulators
    • 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.

Nr Authors Title Topic Conference Link Tutor Student
1 Liang et al. Multi-Task Multi-Sensor Fusion for 3D Object Detection 3D Object Detection CVPR 2019 paper Nikolas Brasch Maximilian M
2 Ku et al. Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction 6D Pose Estimation CVPR 2019 paper Mahdi Saleh Maximilian K
3 Voigtlaender et al. MOTS: Multi-Object Tracking and Segmentation 2D Tracking and Segmentation CVPR 2019 paper Nikolas Brasch Ege
4 Casser et al. Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos Depth Prediction AAAI 2019 paper Markus Herb Evin
5 Li et al. Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving Ego-Motion & 3D Object Tracking ECCV 2018 paper Markus Herb Emanuel
6 Behley et al. Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments LIDAR SLAM & Dense Reconstruction RSS 2018 paper Markus Herb Ivan
7 Gupta et al. Social GAN Trajectory Prediction CVPR 2018 paper Oliver Scheel Mehmet
8 Cui et al. Multimodal Trajectory Predictions for Autonomous Driving Using Deep Convolutional Networks Trajectory Prediction ICRA 2019 paper Oliver Scheel Artemii
9 Bansal et al. ChauffeurNet?: Learning to Drive by Imitating the Best and Synthesizing the Worst End-to-End Driving RSS 2019 paper Oliver Scheel Alexander
10 Luo et al. Taking A Closer Look at Domain Shift:Category-level Adversaries for Semantics Consistent Domain Adaptation Domain Adaptation CVPR 2019 paper Artem Savkin Mert
11 Fu et al. Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping Domain Adaptation CVPR 2019 paper Artem Savkin Erkam
12 Li at al. Attention-guided Unified Network for Panoptic Segmentation 2D Panoptic Segmentation CVPR 2019 paper Yanyan Li Lukas


The schedule for the paper presentations will be announced here during the semester. Each student is required to attend all presentations.

Date Time Place Speaker Topic Invited External Talk
Fri, October 18, 2019 16:00-18:00 3.13.010, Seminarraum Federico Introductory talk slides  
Fri, November 8, 2019 16:00-18:00 3.13.010, Seminarraum Maximilian M
Maximilian K
Object Detection, Pose Estimation and Tracking A. Braun (Bosch)
Fri, November 29, 2019 16:00-18:00 3.13.010, Seminarraum Evin
Depth prediction, SLAM and Reconstruction O. Lazarevych (Lyft)
Fri, December 13, 2019 16:00-18:00 3.13.010, Seminarraum Mehmet
Trajectory prediction and End-to-End Driving N. Kose Cihangir (Intel)
Fri, January 17, 2020 16:00-18:00 3.13.010, Seminarraum Mert
Domain Adaptation and Panoptic Segmentation M.-A. Nikouei Mahani (BMW)

Title: Computer Vision and Deep Learning for Autonomous Driving
Professor: PD Dr. Federico Tombari
Tutors: Helisa Dhamo, Shun-Cheng Wu, Yida Wang, Yanyan Li
Type: Hauptseminar
Information: Hauptseminar, SWS: 2, ECTS: 5
Term: 2019WiSe

Edit | Attach | Refresh | Diffs | More | Revision r1.16 - 28 Oct 2019 - 12:54 - NikolasBrasch

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