TeachingSS17PLARR

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: Christian Rupprecht, Iro Laina, Keisuke Tateno
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: Feb. 2nd, 18:00, Location: 03.13.010

The course will be conducted in English.

Google Group for Q&A: link

Important News

Please check back here regularly, as important news will be posted here.


Schedule (Preliminary)

Date Location Topic Assignments Conducted by Materials Due
02.02.2017, 18:00 03.13.010 Preliminary meeting ("Vorbesprechung") - Course Tutors Course info -
27.04.2017, 14.00 - 15.30 02.07.023 Introduction & Computer Vision basics OpenCV Federico Lecture
Exercise
Code (fixed)
03.05.2017 23:59
04.05.2017, 14.00 - 15.30 02.07.023 Feature description and matching OpenCV Federico Lecture
Exercise
Code
10.05.2017 23:59
11.05.2017, 14.00 - 15.30 02.07.023 Fundamentals of CNNs and deep feature learning TensorFlow Iro Lecture
Exercise
Code
18.05.2017 23:59
18.05.2017, 14.00 - 15.30 02.07.023 Image classification and object detection with CNNs TensorFlow Iro Lecture
Exercise
Code
30.05.2017 23:59
01.06.2017, 14.00 - 15.30 02.07.023 3D sensors and 3D representations OpenNI, Eigen Federico Lecture
Exercise
Code
07.06.2017 23:59
08.06.2017, 14.00 - 15.30 02.07.023 Surface matching and registration PCL Federico Lecture
Exercise
Code
21.06.2017 23:59
22.06.2017, 14.00 - 15.30 02.07.023 3D descriptors for object detection and 6DoF pose estimation PCL Federico Lecture
Exercise
Code
28.06.2017 23:59
29.06.2017, 14.00 - 15.30 02.07.023 Material for Challenge I   Federico, Christian Slides
Validation Data
-
06.07.2017, 14.00 - 15.30 02.07.023 Support for Challenge I   Federico, Christian - 13.07.2017 23:59
13.07.2017, 14.00 - 15.30 02.07.023 Material for Challenge II   Federico, Christian Yet Another Training Dataset
Challenge2 Validation
cancelled,used for final challenge only
03.08.2017, 14.00 - 18.00 00.13.009A Final Challenge   Final Challenge Images
Secret Object
   


TeachingForm
Title: Perception and Learning in Robotics and Augmented Reality
Professor: Dr. Federico Tombari, Prof. Nassir Navab
Tutors: Christian Rupprecht, Iro Laina; Keisuke Tateno
Type: Praktikum
Information: 6 SWS, 10 ECTS Credits (IN2106)
Term: 2017SoSe
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 r1.46 - 03 Aug 2017 - 12:20 - ChristianRupprecht

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