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, Nikolas Brasch, Shun-Cheng Wu, Mahdi Saleh, Fabian Manhardt, Yanyan Li
Type: Seminar Module IN0014
SWS: 2+0
ECTS: 5 Credits
Location: Zoom meeting
Time: tba
Course Language: English


  • You can submit your motivation letter here.
  • Preliminary Meeting: *July 14th, 11:00-11:30am, Zoom meeting PW: 479686 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.

Nr Authors Title Topic Conference Link Tutor Student
1 Yang et al. 3DSSD: Point-based 3D Single Stage Object Detector Object detection CVPR 2020 paper Mahdi Saleh Ebraheem
2 Najibi et al. DOPS: Learning to Detect 3D Objects and Predict their 3D Shape Object detection CVPR 2020 paper Mahdi Saleh Fabian
3 Zhou et al. Tracking Objects as Points Object tracking ECCV 2020 paper Alexander Lehner Tim
4 Bijelic et al. Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather Sensor Fusion CVPR 2020 paper Markus Herb Gabriel
5 Zakharov et al Autolabeling 3D Objects with Differentiable Rendering of SDF Shape Priors Detection + Reconstruction CVPR 2020 paper Fabian Manhardt Min Shan
6 Yang et al. SurfelGAN?: Synthesizing Realistic Sensor Data for Autonomous Driving Reconstruction + Completion CVPR 2020 paper Yanyan Li Surendran
7 Guizilini et al. Semantically-Guided Representation Learning for Self-Supervised Monocular Depth Depth + Odometry ICLR 2020 paper Evin Pinar Örnek Enis
8 Xue et al. Learning Multi-view Camera Relocalization with Graph Neural Networks Relocalization CVPR 2020 paper Shun-Cheng Wu Frithjof
9 Zeng et al. DSDNet: Deep Structured self-Driving Network Motion planning ECCV 2020 paper Nikolas Brasch Matthias
10 Cheng et al. Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation Panoptic Segmentation CVPR 2020 paper Markus Herb Muhammed
11 Hu et al. Probabilistic Future Prediction for Video Scene Understanding Semantics/Depth/OpticalFlow ECCV 2020 paper Azade Farshad Cecilia
12 Hugues et al. KPConv: Flexible and Deformable Convolution for Point Clouds Pointcloud Segmentation ICCV 2019 paper Stefano Gasperini Pascal
13 Liu et al. Morphing and Sampling Network for Dense Point Cloud Completion Pointcloud Completion AAAI 2020 paper Yida Wang Lukas
14 Parl et al. Contrastive Learning for Unpaired Image-to-Image Translation Domain Adaptation ECCV 2020 paper Artem Savkin Erkam


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
Tue, July 14, 2020 11:00-11:30 Zoom Federico Preliminary meeting slides  
Fri, Nov 6, 2020 16:00-17:00 Zoom Federico Introductory Session slides video  
Fri, Nov 20, 2020 16:00-18:00 Zoom Ebraheem, Fabian, Tim Object Detection & Tracking Peter Ondrúška
Head Of Research at Lyft Level 5
Fri, Nov 27, 2020 16:00-18:00 Zoom Gabriel, Min Shan, Surendran Sensor fusion, 3D Reconstruction  
Fri, Dec 4, 2020 16:00-18:00 Zoom Enis, Frithjof, Matthias Motion estimation, Planning, Relocalization Adrien Gaidon
Senior Manager for Machine Learning Research
Toyota Research Institute
Fri, Dec 11, 2020 16:00-18:00 Zoom Muhammed, Cecilia Segmentation Thomas Sayre-McCord
Advanced Autonomy Function Lead
Fri, Jan 15, 2020 16:00-18:00 Zoom Lukas, Erkam Completion, Domain Adaptation  

Title: Computer Vision and Deep Learning for Autonomous Driving
Professor: PD Dr. Federico Tombari
Tutors: Nikolas Brasch, Shun-Cheng Wu, Mahdi Saleh, Fabian Manhardt, Yanyan Li
Type: Hauptseminar
Information: Hauptseminar, SWS: 2, ECTS: 5
Term: 2020WiSe

Edit | Attach | Refresh | Diffs | More | Revision r1.26 - 11 Dec 2020 - 15:17 - ShunChengWu

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