TeachingSs19CVDLAD

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

Seminar: Computer Vision and Deep Learning for Autonomous Driving

PD Dr. Federico Tombari, Fabian Manhardt, Helisa Dhamo, Shun-Cheng Wu, Manuel Nickel

Type: Seminar Module IN0014
SWS: 2+0
ECTS: 5 Credits
Location: CAMP Seminar Room, 03.13.010
Time: Monday, 4.00pm-5.30pm
Course Language: English

Announcements

  • Preliminary Meeting: Jan. 28th, 4pm, in room: CAMP Seminar Room, 03.13.010
    The slides can be found here
  • If interested in registering for the Seminar, you can send your motivation letter (plus relevant courses that you have taken) to cvdlad-sose19[at]googlegroups.com until Feb. 14th.
    Please note that the official application has to be done via the TUM Matching System.

Introduction

  • 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.

Registration

Requirements

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 Li et al. UnDeepVO?: Monocular Visual Odometry through Unsupervised Deep Learning SLAM/Depth Prediction ICRA 2018 paper Jakob Mayr  
1 Dube et al. SegMap?: 3D Segment Mapping using Data-Driven Descriptors Feature learning for SLAM RSS 2018 paper Markus Herb  
1 Yang et al. Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry SLAM with deep learned priors ECCV 2018 paper Markus Herb  
2 Li et al. Weakly- and Semi-Supervised Panoptic Segmentation Semantic and Instance Segmentation ECCV 2018 paper Niko Brasch  
2 Sandler et al. MobileNetV2?: Inverted Residuals and Linear Bottlenecks Lightweight CNNs CVPR 2018 paper Niko Brasch  
2 Manhardt et al. ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape Pose Estimation CVPR 2019 paper Fabian Manhardt  
3 Wang et al. Video-to-Video Synthesis GAN / Synthetic Data NIPS 2018 paper Artem Savkin  
3 Qi et al. Semi-parametric Image Synthesis GAN / Synthetic Data CVPR 2018 paper Artem Savkin  
3 Tulyakov et al. MoCoGAN?: Decomposing Motion and Content for Video Generation GAN / Synthetic Data CVPR 2018 paper Artem Savkin  
4 Maqueda et al. Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars Imitation Learning CVPR 2018 paper Jakob Mayr  
4 Isele et al. Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning Reinforcement Learning ICRA 2018 paper Oliver Scheel  
4 Alahi et al. Social LSTM Trajectory Prediction CVPR 2016 paper Oliver Scheel  

Schedule

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
Mo 28.01.2019 16:00 - 17:30 3.13.010, Seminarraum Federico and tutors Kickoff meeting  
Mo 29.04.2019 16:00 - 18:00 3.13.010, Seminarraum Federico Introductory talk -
Mo 13.05.2019 16:00 - 18:00 3.13.010, Seminarraum TBA TBA TBA
Mo 03.06.2019 16:00 - 18:00 3.13.010, Seminarraum TBA TBA C. Unger (BMW)
Mo 24.06.2019 16:00 - 18:00 3.13.010, Seminarraum TBA TBA F. Petit (Blickfeld)
Mo 15.07.2019 16:00 - 18:00 3.13.010, Seminarraum TBA TBA S. Ramos (Zenuity)


TeachingForm
Title: Computer Vision and Deep Learning for Autonomous Driving
Professor: PD Dr. Federico Tombari
Tutors: Fabian Manhardt; Helisa Dhamo; Shun-Cheng Wu; Manuel Nickel
Type: Hauptseminar
Information: Hauptseminar, SWS: 2, ECTS: 5
Term: 2019SoSe
Abstract:  


Edit | Attach | Refresh | Diffs | More | Revision r1.14 - 25 Mar 2019 - 14:27 - FabianManhardt

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