10 results

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Andreas Geiger
(Max Planck Research Group Leader)
+49 7071 601 1830
andreas.geiger@tuebingen.mp...
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Andreas Geiger

Max Planck Research Group Leader

I am interested in computer vision and machine learning with a focus on 3D scene understanding, parsing and reconstruction. During my Ph.D. I have developed probabilistic models for 3D traffic scene understanding from movable platforms.

Office: 1.B.04
+49 7071 601 1830


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Aseem Behl

Ph.D. Student

Advisor: Andreas Geiger

My research is in the area of Computer Vision and Machine Learning. Currently, I am working on problems related to estimation of 3D motion fields from stereo image sequences.

Office: 01.A.06


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Benjamin Coors

Ph.D. Student

Advisor: Andreas Geiger

I'm working on "Efficient Invariant Deep Models for Computer Vision" in cooperation with Bosch.


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Fatma Güney

Ph.D. Student

Advisor: Andreas Geiger

I am interested in estimating 3D scene representations from multi-view video sequences. In particular, I focus on combining semantic segmentation, object detection and classification with 3D reconstruction using efficient inference methods.


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Joel Janai

Ph.D. Student

Advisor: Andreas Geiger

Perception is a fundamental part of intelligence since perception is necessary to acquire knowledge and knowledge is necessary to understand perception. Therefore computer vision is one of the most important aspects in the realization of intelligent systems. My interest of research lies in computer vision and the combination with machine learning which, to my mind, will enable the realization of intelligent systems. Currently, I am working on optical flow and how to incorporate high-level information to alleviate this ill-posed problem.

Office: 1.B.01


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Yiyi Liao

Ph.D. Intern

Advisor: Andreas Geiger


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Lars Mescheder

Ph.D. Student

Advisor: Andreas Geiger

Human brains can learn from data without an obvious supervision target and can afterwards use this knowledge in a variety of situations and even generate new content. On the other hand, the applicability of modern supervised machine learning algorithms is limited by the amount of available training data. Generative probabilistic models seem to provide a promising way to tackle this problem and in addition allow us to incorporate knowledge of the underlying process. My long term goal is to use such models to tackle ambiguities in 3D reconstruction due to non-Lambertian materials and to reason about global illumination.


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Gernot Riegler

Ph.D. Student

Advisor: Andreas Geiger

Office: 1.B.12


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David Stutz

Master Student


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Osman Ulusoy
(Postdoctoral Researcher)
osman.ulusoy@tuebingen.mpg.de
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Osman Ulusoy

Postdoctoral Researcher

Advisor: Andreas Geiger

I am interested in recovering physical properties of general scenes from images. My current research focuses on reconstructing dense 3-d surface geometry, appearance and motion of dynamic scenes from multi-view image sequences. I believe accurate estimation of these properties will help understand the physical world.