TeachingSS20PLARR

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: PD Dr. Federico Tombari
Tutors: Helisa Dhamo, Fabian Manhardt, Alexander Winkler
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: on Wed 05.02.2020, 16:00 , Location: 01.10.011 . See slides for more info.
The praktikum will take place remotely, on Fridays, 3pm-5pm.

The course will be conducted in English.



Important News

  • We created a Moodle Forum for discussions.
  • If interested in registering for the Praktikum, you can send us your motivation letter/email at plarr-2020[at]googlegroups.com, until 12.02.2020. We look forward to see your reasons for attending the praktikum, related courses you have taken as well as experience in computer vision. However, please note that the official application has to be done via the TUM Matching System.
  • The slides of the preliminary meeting can be found here.

Schedule

Date Location Topic Assignments Conducted by Material Due
24.04.2020 3pm-5pm online Introduction and Computer Vision Basics OpenCV? Federico Tombari Lecture slides
Lecture video
Exercise
Cmake-git tutorial
08.05.2020 10am
08.05.2020 online 2D Feature Description and Matching OpenCV? Benjamin Busam Lecture slides
Lecture video
Exercise
15.05.2020 10am
15.05.2020 online Fundamentals of Deep Learning and CNNs PyTorch? Helisa Dhamo Lecture slides
Lecture video
Exercise
22.05.2020 1pm
22.05.2020 online Deep Learning for Computer Vision and Perception PyTorch? Helisa Dhamo
Fabian Manhardt
Lecture slides
Lecture video
Exercise
29.05.2020 1pm
29.05.2020 online 3D Sensors and 3D Representation Eigen Federico Tombari Lecture slides
Lecture video
Exercise
05.06.2020 1pm
05.06.2020 online Surface Matching and Registration Open3D Federico Tombari Lecture slides
Exercise
Open3D tutorial
 
12.06.2020 online 3D Object Detection and Pose Estimation OpenCV?, Open3D Fabian Manhardt    
19.06.2020 - 24.07.2020 online Project preparation        
10.07.2020   Intermediate presentations        
24.07.2020   Final presenations        
             


Projects

A list of all projects can be downloaded here. The individual project introductions by the mentors can be found here.

Project Topic Mentor Team Presentation Schedule
1 Flatten the Curve: Social Distancing in the Wild Evin Pınar Örnek Team 1: Dilara, Enis, Ehsan  
2 Object Trajectory Augmentation using Point Clouds Mahdi Saleh, Stefano Gasperini Team 2: Arian, Yan Chi, Wai Yan  
3 Diminished Reality Shun-Cheng Wu Team 3: Severin, Siyam, Anindya  
4 Human Sign Language from RGB for Human-Robot Interaction Yida Wang Team 4: Dhaval, Zhongyi, Yige  
5 Depth-aware Mixed Reality: Capture the AR-Flag Benjamin Busam, Patrick Ruhkamp Team 5: Jeremias, Muhammad, Ihsan  
6 Dense Monocular 3D Mapping for AR Artem Savkin, Markus Herb Team 6: Hanzhi, Chendi, Daoyi  
7 Augmented Reality (AR) Platform in Dynamic Environments Yanyan Li, Nikolas Brasch Team 7: Ruixiang, Noir  




TeachingForm
Title: Perception and Learning in Robotics and Augmented Reality
Professor: PD Dr. Federico Tombari
Tutors: Helisa Dhamo, Fabian Manhardt, Alexander Winkler
Type: Praktikum
Information: 6 SWS, 10 ECTS Credits (IN2106)
Term: 2020SoSe
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 r1.45 - 05 Jun 2020 - 15:47 - FabianManhardt

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