Thomas Nestmeyer

Ph.D. Student

Perceiving Systems

Office: N3.004
Max-Planck-Ring 4
72076 Tübingen
Germany

+49 7071 601 1812

Find me on Google Scholar

GitHub Repository

Advisor(s):
Peter Vincent Gehler

Education

2014 - now: Ph.D. Student
Max Planck Institute for Intelligent Systems, Perceiving Systems Department, Tübingen, Germany

2004 – 2013: Diploma Student
Computer Science and Mathematics - Eberhard-Karls-Universität Tübingen, Germany

2013 Diploma in Computer Science from the Eberhard-Karls-Universität Tübingen, Germany
Diploma Thesis "Decentralized Multi-target Exploration and Connectivity Maintenance with a Multi-robot System" at the Max Planck Institute for Biological Cybernetics, Tübingen, Germany
2010 Visiting Student
Computer Science - University of Otago, Dunedin, New Zealand
2007 Pre-Diploma in Mathematics from the Eberhard-Karls-Universität Tübingen, Germany

2003 - 2004: Military Service
German Air Force in Roth and Landsberg, Germany

1994 – 2003: High School Student
Gymnasium Neckartenzlingen in Neckartenzlingen, Germany

Experience

2017: Teaching Assistant at the Julius-Maximilians Universität Würzburg, Germany
Summer term: Probabilistic Inference

2012 - 2014: Research Assistant and later Research Associate at the Max Planck Institute for Biological Cybernetics, Tübingen, Germany
Working in the field of multiple robot systems on "Decentralized Simultaneous Multi-target Exploration with Continual Connectivity Maintenance"

2005 - 2012: Teaching Assistant at the Eberhard-Kars-Universität Tübingen, Germany
Computer Engineering (2 semesters), Mathematics for Computer Scientists (4 semesters), Theoretical Computer Science (2 semesters), Algebraical and Combinatorial Applications in Computer Science (1 semester), Pre-Course Mathematics (2 weeks per course, 6 courses)

2006 - 2011: Research Assistant at the Eberhard-Kars-Universität Tübingen, Germany
Chairs of Computer Engineering (1 year), Discrete Mathematics (1 year), Formal Languages (0.5 years)

Research Interest

I work on decomposing photographs into intrinsic layers, meaning its reflectance and shading components. Knowing this information helps in many computer vision areas. I am specifically interested in physically correct inpainting. In this process, one needs to estimate where the light sources are, which material the objects are made of, what the room geometry is, etc.

Intrinsic Properties of Scenes

Intrinsic images correspond to physical properties of the scene. It is a long-standing hypothesis that these fundamental scene properties provide a foundation for scene interpretation.

To decouple albedo and shading given a single image, we introduce a novel prior on albedo, that models albedo values as being drawn fr...

Naejin Kong Martin Kiefel Peter Vincent Gehler Michael Black Thomas Nestmeyer

Reflectance Filtering

This is the project page for the paper "Reflectance Adaptive Filtering Improves Intrinsic Image Estimation" \cite{nestmeyer2017reflectanceFiltering}, which appears at CVPR 2017 and can be found at https://arxiv.org/abs/1612.05062.

...

Thomas Nestmeyer Peter Vincent Gehler

1 result (BibTeX)

2017

Reflectance Adaptive Filtering Improves Intrinsic Image Estimation

Nestmeyer, T., Gehler, P. V.

In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 1771-1780, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

pre-print DOI Project Page [BibTex]

2017

Nestmeyer, T., Gehler, P. V. Reflectance Adaptive Filtering Improves Intrinsic Image Estimation In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 1771-1780, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

pre-print DOI Project Page [BibTex]