MZ

FotoFirst NameLast NamePosition
Mike Sips Visual Exploration of Space-Time Pattern in Multi-Dimensional and Heterogeneous Data Spaces
Mike Sips Visual Exploration of Space-Time Pattern in Multi-Dimensional and Heterogeneous Data Spaces
Michael Stark Visual Object Recognition and Scene Interpretation
Jürgen Steimle Embodied Interaction
Markus Steinberger GPU Scheduling and Parallel Computing
Carsten Stoll Optical Performace Capture
Robert Strzodka Integrative Scientific Computing
Robert Strzodka Integrative Scientific Computing
Holger Theisel Topological Methods for Vector Field Processing
Holger Theisel Topological Methods for Vector Field Processing

Researcher


Dr. Michael Zollhöfer


Visual Computing, Deep Learning and Optimization

Name of Research Group: Visual Computing, Deep Learning and Optimization
Homepage Research Group: web.stanford.edu/~zollhoef
Personal Homepage: zollhoefer.com
Mentor Saarbrücken: Hans-Peter Seidel
Mentor Stanford: Pat Hanrahan
Research Mission: The primary focus of my research is to teach computers to reconstruct and analyze our world at frame rate based on visual input. The extracted knowledge is the foundation for a broad range of applications not only in visual effects, computer animation, autonomous driving and man-machine interaction, but is also essential in other related fields such as medicine and biomechanics. Especially, with the increasing popularity of virtual, augmented and mixed reality, there comes a rising demand for real-time low latency solutions to the underlying core problems.    My research tackles these challenges based on novel mathematical models and algorithms that enable computers to first reconstruct and subsequently analyze our world. The main focus is on fast and robust algorithms that approach the underlying reconstruction and machine learning problems for static as well as dynamic scenes. To this end, I develop key technology to invert the image formation models of computer graphics based on data-parallel optimization and state-of-the-art deep learning techniques.    The extraction of 3D and 4D information from visual data is highly challenging and under-constraint, since image formation convolves multiple physical dimensions into flat color measurements. 3D and 4D reconstruction at real-time rates poses additional challenges, since it involves the solution of unique challenges at the intersection of multiple important research fields, namely computer graphics, computer vision, machine learning, optimization, and high-performance computing. However, a solution to these problems provides strong cues for the extraction of higher-order semantic knowledge. It is incredibly important to solve the underlying core problems, since this will have high impact in multiple important research fields and provide key technological insights that have the potential to transform the visual computing industry. In summer 2019 Michael Zollhöfer joined Facebook.

Researcher

Name of Researcher
Michels, Dominik
Homepage of Research Group
First Name
Dominik
Last Name
Michels
Foto
url_foto
Phone
Position
High Fidelity Algorithmics Group
Categories
Former Groups
Research Mission
Simulating natural phenomena—like molecular processes, slender object interaction, and nonlinear material behavior—accurately is challenging since the mathematical equations are difficult to handle and the solution quality is heavily dependent on properties of the underlying geometry. We develop high fidelity algorithms for the efficient physically-accurate simulation of natural phenomena and technical procedures as well as for visual computing applications covering design and animation. Amongst others, the focus lies in the preservation of physical and geometric quantities. That requires both fundamental and applied aspects on Computational Mathematics, leading to further open research questions in Symbolic and Numerical Algorithms and Mathematical Modeling.
mission_rtf
Name of Research Group

Personal Info

Photo