| Foto | First Name | Last Name | Position |
|---|---|---|---|
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Vera | Demberg | Cognitive Models of Human Language Processing and their Application to Dialogue Systems |
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Elmar | Eisemann | Computer Graphics and Visualization |
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Markus | Flierl | Visual Sensor Networks |
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Markus | Flierl | Visual Sensor Networks |
|
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Stefan | Funke | Geometry-Guided Design and Analysis of Wireless Sensor Networks |
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Stefan | Funke | Geometry-Guided Design and Analysis of Wireless Sensor Networks |
|
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Joachim | Giesen | Learning of Geometry: Given samples obtained from a shape we want to learn some of its geometric and topological characteristics. A popular example that fits in this framework is surface reconstruction: to obtain a digital model of some solid one samples |
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Joachim | Giesen | Learning of Geometry |
|
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Stefan | Gumhold | 3D Animation Processing |
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Stefan | Gumhold | 3D Animation Processing |
Researcher
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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
- Lakshman, Haricharan
- Homepage of Research Group
- First Name
- Haricharan
- Last Name
- Lakshman
- Foto
- Homepage
- harrycharan.gitlab.io/
- Phone
- Position
- Immersive Video
- Mentor in Saarbruecken
- Hans-Peter Seidel
- Mentor in Stanford
- Bernd Girod
- Categories
- Former Groups
- Research Mission
- The goal of our research group is to create systems for life-like video experience. In particular, we focus on novel video processing algorithms for (a) capture & postproduction, (b) compression & delivery, and (c) rendering stages of an immersive video pipeline. In the content creation stage, we investigate various aspects in producing omnidirectional videos to capture a 3D scene. Due to its nature, this entails very high data volume. Some of the questions we would like to address are: What representations of a 3D scene do we use? How do we minimize the data transfer – can we use receiver-driven communication schemes? What bandwidths do we need for live transmission over the Internet to achieve immersion? At the receiver side, we would like to develop video based realistic rendering of the 3D scene and enable content adaptation for different output devices or viewer preferences. Overall, the pipeline would enable us to revolutionize the viewing experience from passive onlooking to active engagement of the audience by choosing what or where they want to view within the scene. We are at the confluence of various tech trends in acquisition and display devices where such immersive experiences may become realizable in the near future.
- mission_rtf
- Name of Research Group
- Immersive Video
