Computer Graphics Laboratory ETH Zurich


PhD Seminar - AS 17

Course Topics and Objectives

In this doctoral seminar, current research at the Institute for Visual Computing will be presented and discussed. The goal is to learn about current research projects at our institute, to strengthen our expertise in the field, to provide a platform where research challenges can be discussed, and also to practice scientific presentations.

Course Setup

The seminar takes place in the spring and autumn semester.


This course requires solid knowledge in the area of Computer Graphics and Computer Vision as well as state-of-the-art research.

Performance assessment

Ungraded semester performance. Presence is mandatory (75% of the seminar talks, i.e., 9 out of 12) to pass the seminar. Presence is formally controlled. Every participant has to present his/her research once a year.


Number 264-5800-06L
Organizers M. Gross, O. Sorkine-Hornung, M. Pollefeys
Coordinator Vinicius Da Costa De Azevedo
Location CAB G 52, Fridays 12:15-13:00
ECTS Credits 1


Date Name Topic
22/09 Dr. Severin Klinger TBA
29/09 Monique Meuschke Visualization and Interactive Exploration of Risk Criteria for Cardiovascular Diseases
06/10 No Seminar -
13/10 Dr. Vagia Tsiminaki Appearance Modeling for 4D Multi-view Representations
20/10 Dr. Kai Lawonn Visual enhancement of focus structures in selected applications
27/10 Dr. Zhaopeng Cui Global Structure-from-Motion and Its Applications
03/11 PhD Students Presentation Lucas Teixeira: Computer vision for autonomous aerial robots
Daniel Thul: Temporally Coherent Approximate Convex Decomposition
10/11 PhD Students Presentation Byungsoo Kim: Subspace fluid simulation and control with machine learning
17/11 No Seminar -
24/11 PhD Students Presentation Gökcen Cimen: Intuitive Interfaces for Interactively-Controlled Physically Simulated Characters
Rafael Wampfler: Investigation of User Emotion for Improving Learning Applications
01/12 PhD Students Presentation Fabiola Maffra: Place Recognition for UAV Navigation
Thomas Müller: Machine Learning for Light-Transport Simulation
08/12 Dr. Nils Thuerey Deep Learning for Physics Problems, especially Fluids