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, Prof. Nassir Navab, Fabian Manhardt, Johanna Wald

Type: Seminar Module IN0014
SWS: 2+0
ECTS: 5 Credits
Location: CAMP Seminar Room, 03.13.010
Time: Wednesday, 10.00am-11.30am
Course Language: English


  • The slides of the preliminary meeting are available.
  • If interested in registering for the Seminar, you can send us your motivation letter (plus relevant courses that you have taken) at cvdlad-w18[at]googlegroups.com. Please note that the official application has to be done via the TUM Matching System.


  • 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.
  • Written Report: A document of maximum 8 pages is written and submitted one week after the presentation. Please download and use the llncs2e.zip Latex template.
  • 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 report must include all references used and must be written completely in your own words. Copy and paste will not be tolerated. Both report 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 A. Paszke et al. ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation Lightweight CNNs Arxiv 2018 link Nikolas Brasch Egor Labintcev
1 R. Mahjourian et al. Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints Depth/Odomoetry Prediction CVPR 2018 link Nikolas Brasch Jan-Hendrik Neudeck
2 R. Wang et al. Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras SLAM ICCV 2017 link Jakob Mayr Sandeep Kumar
2 S. Brahmbhatt et al. Geometry-Aware Learning of Maps for Camera Localization SLAM CVPR 2018 link Markus Herb Mubashir Hanif
3 Y. Zhou et al. VoxelNet?: End-to-End Learning for Point Cloud Based 3D Object Detection 3D Object Detection CVPR 2018 link Johanna Wald Sascha Geringer
3 D. Xu et al. PointFusion?: Deep Sensor Fusion for 3D Bounding Box Estimation 3D Object Detection CVPR 2018 link Markus Herb Simon Weigl
4 Huang et al. The ApolloScape? Dataset for Autonomous Driving Large-scale dataset annotation Arxiv 2018 link Johanna Wald Alessio Tofani
4 A. Dosovits. CARLA: An Open Urban Driving Simulator Car simulators CoRL? 2017 link Artem Savkin Josefine Gaßner
5 M. Liu et al. Unsupervised Image-to-Image Translation Networks GANs for synthetic dataset generation NIPS 2017 link Artem Savkin Muhammad Ferjad Naeem
5 T.-C. Wang et al. High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs GANs for synthetic dataset generation CVPR 2018 link Jakob Mayr Tsuyoshi Beheim
6 Vemula et al. Social Attention: Modeling Attention in Human Crowds Trajectory prediction ICRA 2018 link Oliver Scheel Ali Kareem Raja
6 F. Codevilla et al. End-to-end Driving via Conditional Imitation Learning Trajectory prediction ICRA 2018 link Oliver Scheel Florian Ölsner


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
Wed 17.10.2018 10:00 - 11:30 3.13.010, Seminarraum Federico and tutors Introductory meeting  
Wed 24.10.2018 10:00 - 11:30 3.13.010, Seminarraum Egor Labintcev
Jan-Hendrik Neudeck
Lightweight CNNs
Depth/Odometry Prediction
A. Pasternak, Apex.AI
Wed 07.11.2018 10:00 - 11:30 3.13.010, Seminarraum Sandeep Kumar
Mubashir Hanif
SLAM M. Herb, Audi
Wed 21.11.2018 10:00 - 11:30 3.13.010, Seminarraum Sascha Geringer
Simon Weigl
3D Object Detection D. Zikic, Lyft
Wed 28.11.2018 10:00 - 11:30 3.13.010, Seminarraum Alessio Tofani
Josefine Gaßner
Large-scale dataset annotation
Car simulators
J. Jamaszyk, Nvidia
Wed 05.12.2018 10:00 - 11:30 3.13.010, Seminarraum Muhammad Ferjad Naeem
Tsuyoshi Beheim
GANs for synthetic dataset generation L. Schwarz, BMW
Wed 12.12.2018 10:00 - 11:30 3.13.010, Seminarraum Ali Kareem Raja
Florian Ölsner
Trajectory prediction A. Abramov, Continental
Wed 16.01.2019 10:00 - 11:30 3.13.010, Seminarraum   Backup date  

Title: Computer Vision and Deep Learning for Autonomous Driving
Professor: PD Dr. Federico Tombari; Prof. Nassir Navab
Tutors: Fabian Manhardt; Johanna Wald
Type: Hauptseminar
Information: Hauptseminar, SWS: 2, ECTS: 5
Term: 2018WiSe

Edit | Attach | Refresh | Diffs | More | Revision r1.29 - 16 Nov 2018 - 08:58 - FedericoTombari

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