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
Matthias Niessner
Homepage of Research Group
First Name
Matthias
Last Name
Niessner
Foto
url_foto
wp.mpi-inf.mpg.de/mpc-vcc/files/2013/11/Niessner-small.jpg
Homepage
niessnerlab.org/members/matthias_niessner/profile.html
Phone
Position
Semantics and Geometry
Mentor in Saarbruecken
Hans-Peter Seidel
Mentor in Stanford
Pat Hanrahan
Categories
Former Groups
Research Mission
My research is devoted to the exciting area of automated content creation. That includes the generation of three-dimensional scene environments from the real-world for live and offline reconstruction scenarios. Beyond static geometry and material retrieval, there are even greater challenges such as interactive performance capture of dynamic objects and the full semantic understanding of scene environments. All that involves large-scale data acquisition from various types of sensors, leading to further open research questions regarding large-scale data analysis, visualization, and processing.
mission_rtf
Name of Research Group
Semantics and Geometry

Personal Info

Photo
Website, Blog or Social Media Link