TeachingSS18PLARR

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

Praktikum/Lab Course - Perception and Learning in Robotics and Augmented Reality

This praktikum introduces common tools and principles in computer vision and machine learning for object recognition and pose estimation, for applications in the field of Augmented Reality and Robotics. Starting from the basics, students are gradually taught to develop a framework that can process images and 3D data with the goal of perceiving shapes and recognizing objects in real environments, under pose variations, clutter and occlusion.

Lecturers: Dr. Federico Tombari, Prof. Nassir Navab
Tutors: Iro Laina, Helisa Dhamo, Fabian Manhardt, Huseyin Coskun, Yida Wang
Type: Master Practical Course Module IN2106
Course Title: Perception and Learning in Robotics and Augmented Reality - Link to TUMOnline

Programs lab course: Biomedical Computing (Master), Informatics (Master), Informatics (Diploma)
Programs elective course: Electrical Engineering, Mechanical Engineering, School of Management
SWS: 6
ECTS: 10 Credits

Meetings:
Preliminary Meeting: Jan. 30th, 14:00, Location: 03.13.010

The course will be conducted in English.

Google Group for Q&A: plarr-2018 |[@]| googlegroups.com



Important News

  • 16/04/2018: The project proposals have been announced. The deadline for submitting your preferences is 23/04/2018.
  • The schedule for the upcoming semester has been announced. In the first lecture, we will additionally hold an (optional) introduction to Git, GitLab and CMake for those who are not familiar with these tools that will be required during the Praktikum.
  • If interested in registering for the Praktikum, you can send us your motivation letter (plus relevant courses that you have taken) at plarr-2018|[at]|googlegroups.com. However, please note that the official application has to be done via the TUM Matching System.


Schedule

Date Location Topic Assignments Conducted by Material Due
30.01.2018, 14:00 03.13.010 Preliminary meeting ("Vorbesprechung") - Federico Tombari Course info -
10.04.2018, 14:00 - 16:00 03.13.010 Introduction and computer vision basics OpenCV Federico Tombari Lecture
Exercise
CMake GIT Tutorial
16.04.2018 23:59
17.04.2018, 14:00 - 15:30 03.13.010 Feature description and matching OpenCV Federico Tombari Lecture
Exercise
23.04.2018 23:59
24.04.2018, 14:00 - 15:30 03.13.010 Fundamentals of Deep Learning and CNNs TensorFlow Iro Laina Lecture
Exercise
04.05.2018 23:59
08.05.2018, 14:00 - 15:30 03.13.010 CNNs in computer vision and perception TensorFlow Iro Laina Lecture
Exercise
25.05.2018 23:59
29.05.2018, 14:00 - 15:30 03.13.010 3D sensors and 3D representations Eigen / OpenNI Federico Tombari    
05.06.2018, 14:00 - 15:30 03.13.010 Surface matching and registration PCL Federico Tombari    
12.06.2018, 14:00 - 15:30 03.13.010 3D object detection and pose estimation PCL Federico Tombari    
19.06.2018, 14:00 - 15:30 03.13.010 Human pose estimation (TBC) - TBA    
Project preparation            
03.07.2018 03.13.010 Intermediate presentations - Participants    
17.07 - 20.07.2018 (TBD) 03.13.010 Final presentations - Participants    


Projects

A list of all projects can be downloaded here.

Topic Project Description Mentor Team
Object detection and pose estimation for robotic manipulation Project 1 Fabian Manhardt Hooriya Anam, Sangram Gupta (Team 2)
Monocular estimation of grasp position and pose for robotic grippers Project 2 Ghazal Ghazaei Kadir Tandogan Tilki, Mario Pfaller (Team 7)
RGB-D detection and pose estimation for bin picking Project 3 Juan Jose Gonzalez Mahsa Baghaei Heravi, Manuel Stoiber (Team 1)
Human sign language from RGB for human-robot interaction Project 4 Yida Wang Gerhard Konnerth, Christina Bukas (Team 3)
Augmented reality (AR) for video-gaming Project 5 Helisa Dhamo Giulia Marchesi, Francesco Cognolato (Team 8)
Depth and surface normals estimation for scene augmentation (AR) Project 6 Iro Laina HyunJun Jung, Rajat Chhabra (Team 5)
Black Mirror (diminished reality) Project 7 Iro Laina Lukasz Lazarski, Kostiantyn Ahapov (Team 9)
Multi-camera AR Project 8 Nikolas Brasch Saurabh Khanduja, Fatma Hayirci (Team 10)
Safety distance and brake assistant Project 9 Jakob Mayr Han Zhen, Li Xiaojing (Team 4)
Traffic sign detection Project 10 Oliver Scheel Christian Diller, Alexander Lehner (Team 6)




TeachingForm
Title: Perception and Learning in Robotics and Augmented Reality
Professor: Dr. Federico Tombari, Prof. Nassir Navab
Tutors: Iro Laina, Helisa Dhamo, Fabian Manhardt, Huseyin Coskun, Yida Wang
Type: Praktikum
Information: 6 SWS, 10 ECTS Credits (IN2106)
Term: 2018SoSe
Abstract: This praktikum introduces common tools and principles in computer vision and machine learning for object recognition and pose estimation, for applications in the field of Augmented Reality and Robotics. Starting from the basics, students are gradually taught to develop a framework that can process images and 3D data with the goal of perceiving shapes and recognizing objects in real environments, under pose variations, clutter and occlusion.


Edit | Attach | Refresh | Diffs | More | Revision r1.24 - 09 May 2018 - 15:43 - IroLaina

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