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.
Intermediate presentations: Your current progress is presented to the other participants (7 minutes (sharp!) presentation 10 minutes discussion). Use the CAMP templates for PowerPoint camp-tum-jhu-slides.zip, or Latex: CAMP-latex-template.
Final presentations: Your final result is presented to the other participants on July 24 starting at 3pm (10 minutes for presentation and live demo and 5 minutes of Q&A). Use the CAMP templates for PowerPoint camp-tum-jhu-slides.zip, or Latex: CAMP-latex-template.
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. |