I am a Full Professor at the University of Tübingen and a Group Leader at MPI-IS Tübingen.
I am interested in computer vision and machine learning with a focus on 3D scene understanding, parsing, reconstruction, material and motion estimation for autonomous intelligent systems such as self-driving cars or household robots. In particular, me and my group investigate how complex prior knowledge can be incorporated into computer vision algorithms for making them robust to variations in our complex 3D world.
Website: http://www.cvlibs.net
Google Scholar: http://scholar.google.ca/citations?user=SrVnrPcAAAAJ&hl=en
Youtube: http://www.youtube.com/user/cvlibs
Facebook: https://www.facebook.com/andreas.geiger.395
computer vision machine learning robotics scene understanding
Abstract
We address the problem of 3D shape completion from sparse and noisy point clouds, a fundamental problem in computer vision and robotics. Recent approaches are either data-driven or learning-based: Data-driven approaches rely on a shape model whose parameters are optimized to fit the observations; ...
Generative Adversarial Networks (GANs) are powerful latent variable models that can be used to learn complex real-world distributions. Especially for images, GANs have emerged as one of the dominant approaches for generating new realistically looking samples after the model has been trained on some dataset. However, while very power...
Motivated by the success of deep learning techniques in matching problems, we present a method for learning context-aware features for solving optical flow using discrete optimization \cite{Guney2016ACCV}. Towards this goal, we present an efficient way of training a context network with a large receptive field size on top of a local...
Existing learning based solutions to 3D surface prediction cannot be trained end-to-end as they operate on intermediate representations (e.g., TSDF) from which 3D surface meshes must be extracted in a post-processing step (e.g., via the marching cubes algorithm). In this paper, we investigate the problem of end-to-end 3D surface pre...
Convolutional networks working on images are often agnostic of the image formation process such as perspective geometry and occlusion; it is often only of marginal interest. However, classical approaches to 3D reconstruction benefit greatly from explicit knowledge about 3D geometry and light propagation, as we have also shown \...
Much of our work focuses on 3D models of objects and scenes. We would like to take advantage of current deep learning approaches in representing and reasoning about 3D. Unfortunately, the standard 2D convolutional models do not readily extend to 3D because they do not match well current 3D data structures....
Michael Black Gernot Riegler Osman Ulusoy Andreas Geiger Anurag Ranjan Timo Bolkart Soubhik Sanyal
We view optical flow as the projection of the 3D motion field into the image plane. Until recently, optical flow algorithms were designed by hand and incorporated various heuristics. Deep learning methods provide an opportunity to move away from hand-crafted models but have several limitations. The key one...
Michael Black Andreas Geiger Anurag Ranjan Jonas Wulff Deqing Sun Varun Jampani Laura Sevilla Joel Janai Fatma Güney
Dense 3D reconstruction from RGB images is a highly ill-posed problem due to occlusions, textureless or reflective surfaces, varied scene geometry, and spatial discontinuities. We propose algorithms that bring in various types of geometric information that imposes long-range, or semantic, knowlege to addre...
To recover 3D motion \cite{Menze2018JPRS}, we have developed a state-of-the-art 3D scene flow estimation technique which exploits recognition (bounding boxes, instance segmentation and object coordinates) to support the challenging matching task \cite{Behl2017ICCV}.
While great progress has been made in recent years, large...
Historically, optical flow methods make generic, spatially homogeneous, assumptions about the spatial structure of the 2D image motion. In reality, optical flow varies across an image depending on object class. Simply put, different objects move differently. For rigid objects, the motion is related to the ...
Michael Black Jonas Wulff Anurag Ranjan Laura Sevilla Fatma Güney Varun Jampani Andreas Geiger Deqing Sun
Much of the recent progress in computer vision has been driven by high-capacity models trained on very large annotated datasets. Examples for such datasets include ImageNet for image classification, MS COCO for object localization or Cityscapes for semantic segmentation. Unfortunately, annotating large datasets at the pixel-level is...
Joel Janai Fatma Güney Jonas Wulff Michael Black Andreas Geiger
Omnidirectional cameras offer great benefits over classical cameras wherever a wide field of view is essential, such as in virtual reality applications or in autonomous robots. Unfortunately, standard convolutional neural networks are not well suited for this scenario as the natural projection surface is a sphere which cannot be unw...
Learning to solve optical flow in an end-to-end fashion from examples is attractive as deep neural networks allow for learning more complex hierarchical flow representations directly from annotated data. However, training such models requires large datasets and obtaining ground truth for real images is challenging as labeling dense ...
Joel Janai Fatma Güney Anurag Ranjan Michael Black Andreas Geiger
Access to 3D datasets and benchmarks is crucial for driving progress in the field. Beyond our popular KITTI dataset, we have therefore proposed novel large-scale datasets for single-image depth prediction and depth map completion \cite{Uhrig2017THREEDV}, as well as two-view and multi-view 3D reconstruction in indoor and outdoor envi...
3D deep learning techniques are notoriously memory-hungry, due to the high-dimensional input and output spaces. However, for most applications, not all areas of space are equally informative or important. In order to allow deep learning techniques to scale to spatial resolutions of 256³ and beyond, we have developed the OctNet ...
Intelligent systems not only require the relative motion of objects around them \cite{Liu2017IROS}\cite{Liu2018IROS}, but typically also a precise global location \cite{Camposeco2017ICCV} with respect to a map, i.e. for planning or navigation tasks. With LOST \cite{Brubaker2016PAMI}, we have demonstrated that localization solel...
Andreas Geiger Joel Janai Fatma Güney Aseem Behl Laura Sevilla Yiyi Liao Benjamin Coors Moritz Menze Jun Xie
In this paper \cite{Uhrig2017THREEDV}, we consider convolutional neural networks operating on sparse inputs with an application to depth upsampling from sparse laser scan data. First, we show that traditional convolutional networks perform poorly when applied to sparse data even when the location of missing data is provided to the n...
Sevilla-Lara, L., Liao, Y., Guney, F., Jampani, V., Geiger, A., Black, M. J.
In German Conference on Pattern Recognition (GCPR), October 2018 (inproceedings)
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Liu, P., Geppert, M., Heng, L., Sattler, T., Geiger, A., Pollefeys, M.
In International Conference on Intelligent Robots and Systems (IROS) 2018, International Conference on Intelligent Robots and Systems, October 2018 (inproceedings)
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Cherabier, I., Schönberger, J., Oswald, M., Pollefeys, M., Geiger, A.
In Computer Vision – ECCV 2018, Springer International Publishing, Cham, September 2018 (inproceedings)
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Coors, B., Condurache, A. P., Geiger, A.
European Conference on Computer Vision (ECCV), September 2018 (conference)
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Barsan, I. A., Liu, P., Pollefeys, M., Geiger, A.
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2018, IEEE, International Conference on Robotics and Automation, May 2018 (inproceedings)
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Paschalidou, D., Ulusoy, A. O., Schmitt, C., Gool, L., Geiger, A.
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2018, 2018 (inproceedings)
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Liao, Y., Donne, S., Geiger, A.
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2018, 2018 (inproceedings)
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Schönberger, J., Pollefeys, M., Geiger, A., Sattler, T.
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2018, 2018 (inproceedings)
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Alhaija, H., Mustikovela, S., Mescheder, L., Geiger, A., Rother, C.
International Journal of Computer Vision (IJCV), 2018, 2018 (article)
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Mescheder, L., Geiger, A., Nowozin, S.
International Conference on Machine learning (ICML), 2018 (conference)
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Coors, B., Condurache, A., Mertins, A., Geiger, A.
In International Conference on Computer Vision Theory and Applications, International Conference on Computer Vision Theory and Applications, 2018 (inproceedings)
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Menze, M., Heipke, C., Geiger, A.
ISPRS Journal of Photogrammetry and Remote Sensing, 2018 (article)
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Mescheder, L., Nowozin, S., Geiger, A.
In Proceedings from the conference "Neural Information Processing Systems 2017., (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., Advances in Neural Information Processing Systems 30 (NIPS), December 2017 (inproceedings)
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Behl, A., Jafari, O. H., Mustikovela, S. K., Alhaija, H. A., Rother, C., Geiger, A.
In Proceedings IEEE International Conference on Computer Vision (ICCV), IEEE, Piscataway, NJ, USA, IEEE International Conference on Computer Vision (ICCV), October 2017 (inproceedings)
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Uhrig, J., Schneider, N., Schneider, L., Franke, U., Brox, T., Geiger, A.
International Conference on 3D Vision (3DV) 2017, International Conference on 3D Vision (3DV), October 2017 (conference)
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Riegler, G., Ulusoy, A. O., Bischof, H., Geiger, A.
International Conference on 3D Vision (3DV) 2017, International Conference on 3D Vision (3DV), October 2017 (conference)
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Liu, P., Heng, L., Sattler, T., Geiger, A., Pollefeys, M.
In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Piscataway, NJ, USA, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2017 (inproceedings)
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Alhaija, H. A., Mustikovela, S. K., Mescheder, L., Geiger, A., Rother, C.
In Proceedings of the British Machine Vision Conference 2017, Proceedings of the British Machine Vision Conference, September 2017 (inproceedings)
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Mescheder, L., Nowozin, S., Geiger, A.
In Proceedings of the 34th International Conference on Machine Learning, 70, Proceedings of Machine Learning Research, (Editors: Doina Precup, Yee Whye Teh), PMLR, International Conference on Machine Learning (ICML), August 2017 (inproceedings)
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Janai, J., Güney, F., Wulff, J., Black, M., Geiger, A.
In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pages: 1406-1416, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)
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Riegler, G., Ulusoy, O., Geiger, A.
In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)
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Schöps, T., Schönberger, J. L., Galliani, S., Sattler, T., Schindler, K., Pollefeys, M., Geiger, A.
In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)
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Camposeco, F., Sattler, T., Cohen, A., Geiger, A., Pollefeys, M.
In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)
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Ulusoy, A. O., Black, M. J., Geiger, A.
In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)
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Janai, J., Güney, F., Behl, A., Geiger, A.
Arxiv, 2017 (article)
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Ulusoy, A. O., Black, M. J., Geiger, A.
In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June 2016 (inproceedings)
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Xie, J., Kiefel, M., Sun, M., Geiger, A.
In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June 2016 (inproceedings)
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Mescheder, L., Nowozin, S., Geiger, A.
Arxiv, 2016 (article)
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Brubaker, M. A., Geiger, A., Urtasun, R.
IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 2016 (article)
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Zhou, C., Güney, F., Wang, Y., Geiger, A.
In International Conference on Computer Vision (ICCV), December 2015 (inproceedings)
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Lenz, P., Geiger, A., Urtasun, R.
In International Conference on Computer Vision (ICCV), International Conference on Computer Vision (ICCV), December 2015 (inproceedings)
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(Best Paper Award)
Ulusoy, A. O., Geiger, A., Black, M. J.
In 3D Vision (3DV), 2015 3rd International Conference on, pages: 10-18, Lyon, October 2015 (inproceedings)
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(Best Paper Award)
In German Conference on Pattern Recognition (GCPR), 9358, pages: 183-195, Lecture Notes in Computer Science, Springer International Publishing, 2015 (inproceedings)
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Menze, M., Heipke, C., Geiger, A.
In German Conference on Pattern Recognition (GCPR), 9358, pages: 16-28, Springer International Publishing, 2015 (inproceedings)
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Menze, M., Heipke, C., Geiger, A.
In Proc. of the ISPRS Workshop on Image Sequence Analysis (ISA), 2015 (inproceedings)
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Schoenbein, M., Geiger, A.
International Conference on Intelligent Robots and Systems, pages: 716 - 723, IEEE, Chicago, IL, USA, IEEE/RSJ International Conference on Intelligent Robots and System, October 2014 (conference)
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Roser, M., Dunbabin, M., Geiger, A.
IEEE International Conference on Robotics and Automation, pages: 3840 - 3847 , Hong Kong, China, IEEE International Conference on Robotics and Automation, June 2014 (conference)
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Schoenbein, M., Strauss, T., Geiger, A.
IEEE International Conference on Robotics and Automation, pages: 4443 - 4450, Hong Kong, China, IEEE International Conference on Robotics and Automation, June 2014 (conference)
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Geiger, A., Lauer, M., Wojek, C., Stiller, C., Urtasun, R.
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 36(5):1012-1025, published, IEEE, Los Alamitos, CA, May 2014 (article)
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Zhang, H., Geiger, A., Urtasun, R.
In International Conference on Computer Vision, pages: 3056-3063, Sydney, Australia, December 2013 (inproceedings)
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Geiger, A., Lenz, P., Stiller, C., Urtasun, R.
International Journal of Robotics Research, 32(11):1231 - 1237 , Sage Publishing, September 2013 (article)
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(CVPR13 Best Paper Runner-Up)
Brubaker, M. A., Geiger, A., Urtasun, R.
In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2013), pages: 3057-3064, IEEE, Portland, OR, June 2013 (inproceedings)
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