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Computer Vision Group
TUM Department of Informatics
Technical University of Munich

Technical University of Munich



This is my old web page. In September 2014, I have moved to the Computer Vision and Geometry Group at ETH Zurich. My new web page can be found here.

A list of my publications is available here.

Brief Bio

Martin R. Oswald received his "Vordiplom" degree (2003) and a "Diplom" degree (2007) in Computer Science from Dresden University of Technology. In 2005 he studied Machine Learning and Computer Vision at the University of Technology, Sydney. From 2005 to 2007 he worked at the Institute for Civil Engineering at Dresden University of Technology studying neural networks and their application to engineering problems. Subsequently, he studied reliability estimation methods in civil engineering and received a Master's degree in civil engineering from the University of Technology Federico Santa Maria, Valparaiso - Chile (2008). From January 2009 to August 2014 he was a Ph.D. student in the Computer Vision Group at Technische Universität München, Germany, headed by Professor Daniel Cremers.

Research Interests

Spatiotemporal Multi-view 3D Reconstruction, Video Processing, Single View Reconstruction, Flow Estimation, Image Segmentation, Variational Methods, Optimization Methods, Probabilistic Methods, Machine Learning, Artificial Intelligence.

I have been working on the following research projects:

Spatio-Temporal Multi-View 3D Reconstruction

Problem: Recover the 3D geometry of a scene from multiple synchronously captured videos.

The video below shows reconstruction results of our method with our proposed generalized connectivity constraints (published at ECCV 2014).

The following video shows results of our method which additionally estimates and integrates normal information (published at BMVC 2014)

The following video shows early results of our method (published at ICCV-4DMOD 2013).

For more information, see the related publication.
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Conference and Workshop Papers
[]Generalized Connectivity Constraints for Spatio-temporal 3D Reconstruction (M. R. Oswald, J. Stühmer and D. Cremers), In European Conference on Computer Vision (ECCV), 2014.  [bibtex] [pdf] [video]
[]Surface Normal Integration for Convex Space-time Multi-view Reconstruction (M. R. Oswald and D. Cremers), In British Machine Vision Conference (BMVC), 2014.  [bibtex] [pdf] [video]
[]Spatial and Temporal Interpolation of Multi-View Image Sequences (T. Gurdan, M. R. Oswald, D. Gurdan and D. Cremers), In German Conference on Pattern Recognition (GCPR), volume 36, 2014.  [bibtex] [pdf] [video]
[]A Convex Relaxation Approach to Space Time Multi-view 3D Reconstruction (M. R. Oswald and D. Cremers), In ICCV Workshop on Dynamic Shape Capture and Analysis (4DMOD), 2013.  [bibtex] [pdf]
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Single-View 3D Reconstruction

Problem: Recover the 3D geometry of an object from a single input image. For example:

Input image
Input image
3D Reconstruction
3D Reconstruction
Input Image

The following video shows several results generated by our single-view reconstruction tool.

For more details have a look at the project page.

Rechte Seite

Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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Bernt Schiele (Max Planck Institute for Informatics) will give a talk in the TUM AI lecture series on June 10th, 3pm! Livestream

French-German Machine Learning Symposium

French-German Machine Learning Symposium

The French-German Machine Learning Symposium aims to strengthen interactions and inspire collaborations between both countries. We invited some of the leading ML researchers from France and Germany to this two-day symposium to give a glimpse into their research, and engage in discussions on the future of machine learning and how to strengthen research collaborations in ML between France and Germany.

The list of speakers includes Yann LeCun, Cordelia Schmid, Jean-Bernard Lasserre, Bernhard Schölkopf, and many more! For the full program please visit the webpage.


Ron Kimmel (Technion - Israel Institute of Technology) will give a talk in the TUM AI lecture series on May 6th, 3pm! Livestream


4Seasons Dataset: We have released a novel dataset for benchmarking multi-weather SLAM in autonomous driving.


Hao Li (Pinscreen) will give a talk in the TUM AI lecture series on April 22nd, 8pm! Livestream