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Publications

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Author Title Type [ Year(Desc)]
2017
N. von Schmude, “Visual Localization with Lines”, vol. Dissertation. IWR, Univ. Heidelberg, 2017.
2018
M. Bopp, “Air-Flow and Stress Partitioning over Wind Waves in a Linear Wind-Wave Facility”, vol. Dissertation. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg, Heidelberg, 2018.
S. Lang and Ommer, B., “Attesting Similarity: Supporting the Organization and Study of Art Image Collections with Computer Vision”, Digital Scholarship in the Humanities, Oxford University Press, vol. 33, no. 4, pp. 845-856, 2018.
H. Abu Alhaija, Mustikovela, S. Karthik, Mescheder, L., Geiger, A., and Rother, C., “Augmented Reality Meets Computer Vision”, International Journal of Computer Vision, vol. In press, pp. 1–13, 2018.
H. Abu Alhaija, Mustikovela, S. K., Mescheder, A., Geiger, C., and Rother, C., “Augmented Reality Meets Computer Vision Efficient Data Generation for Urban Driving Scenes”, IJCV, pp. 1-12, 2018.PDF icon Technical Report (3.83 MB)
H. Abu Alhaija, Mustikovela, S. Karthik, Mescheder, L., Geiger, A., and Rother, C., “Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes”, International Journal of Computer Vision, vol. 126, pp. 961–972, 2018.
T. Hodaň, Michel, F., Brachmann, E., Kehl, W., Buch, A. Glent, Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., Sahin, C., Manhardt, F., Tombari, F., Kim, T. Kyun, Matas, J., and Rother, C., “BOP: Benchmark for 6D object pose estimation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11214 LNCS, pp. 19–35.
P. Bell and Ommer, B., “Computer Vision und Kunstgeschichte — Dialog zweier Bildwissenschaften”, in Computing Art Reader: Einführung in die digitale Kunstgeschichte, P. Kuroczyński et al. (ed.), 2018.PDF icon 413-17-83318-2-10-20181210.pdf (2.98 MB)
A. Arnab, Zheng, S., Jayasumana, S., Romera-paredes, B., Kirillov, A., Savchynskyy, B., Rother, C., Kahl, F., and Torr, P., “Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation”, Cvpr, vol. XX, pp. 1–15, 2018.
N. Sayed, Brattoli, B., and Ommer, B., “Cross and Learn: Cross-Modal Self-Supervision”, in German Conference on Pattern Recognition (GCPR) (Oral), Stuttgart, Germany, 2018.PDF icon Article (891.47 KB)PDF icon Oral slides (9.17 MB)
L. Cerrone, “Deep End-to-End Learning of a Diffusion Process for Seeded Image Segmentation”, Heidelberg University, 2018.
A. Sanakoyeu, Bautista, M., and Ommer, B., “Deep Unsupervised Learning of Visual Similarities”, Pattern Recognition, vol. 78, 2018.PDF icon PDF (8.35 MB)
C. Weilbach, “Dictionary Learning with Bayesian GANs for Few-Shot Classification”, Heidelberg University, 2018.
A. - S. Wahl, Erlebach, E., Brattoli, B., Büchler, U., Kaiser, J., Ineichen, V. B., Mosberger, A. C., Schneeberger, S., Imobersteg, S., Wieckhorst, M., Stirn, M., Schroeter, A., Ommer, B., and Schwab, M. E., “Early reduced behavioral activity induced by large strokes affects the efficiency of enriched environment in rats”, Sage Journals, vol. Journal of Cerebral Blood Flow & Metabolism, 2018.PDF icon 0271678x18777661.pdf (770.87 KB)
T. Hehn and Hamprecht, F. A., “End-to-end Learning of Deterministic Decision Trees”, German Conference on Pattern Recognition. Proceedings, vol. LNCS 11269. Springer, pp. 612-627, 2018.PDF icon Technical Report (1.4 MB)
F. Draxler, “The Energy Landscape of Deep Neural Networks”, Heidelberg University, 2018.
F. Draxler, Veschgini, K., Salmhofer, M., and Hamprecht, F. A., “Essentially No Barriers in Neural Network Energy Landscape”, ICML. Proceedings, vol. 80. p. 1308--1317, 2018.PDF icon Technical Report (685.93 KB)
S. Haller, Swoboda, P., and Savchynskyy, B., “Exact MAP-Inference by Confining Combinatorial Search With LP Relaxation”, in Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, 2018.PDF icon 2018-02-02_aaai_dense_combilp.pdf (325.08 KB)
M. Storath and Weinmann, A., “Fast median filtering for phase or orientation data”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 3, pp. 639–652, 2018.PDF icon Technical Report (7.32 MB)
F. Rathke and Schnörr, C., “Fast Multivariate Log-Concave Density Estimation”, preprint: ArXiv, 2018.PDF icon Technical Report (3.54 MB)
F. Rathke and Schnörr, C., “Fast Multivariate Log-Concave Density Estimation”, preprint: arXiv, 2018.
D. Fortun, Storath, M., Rickert, D., Weinmann, A., and Unser, M., “Fast Piecewise-Affine Motion Estimation Without Segmentation”, IEEE Transactions on Image Processing, vol. 27 , no. 11, pp. 5612 - 5624, 2018.
A. Zern, Rohr, K., and Schnörr, C., “Geometric Image Labeling with Global Convex Labeling Constraints”, in EMMCVPR, 2018, vol. 10746, pp. 533–547.
H. Abu Alhaija, Mustikovela, S. K., Geiger, A., and Rother, C., “Geometric Image Synthesis”, ACCV. Proceedings, in press. 2018.PDF icon Technical Report (1.83 MB)
A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, preprint: arXiv, 2018.
R. Hühnerbein, Savarino, F., Aström, F., and Schnörr, C., “Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment”, SIAM Journal on Imaging Sciences, vol. 11, no. 2, pp. 1317-1362, 2018.PDF icon Technical Report (2.62 MB)
R. Hühnerbein, Savarino, F., Aström, F., and Schnörr, C., “Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment”, SIAM J. Imaging Science, vol. 11, pp. 1317–1362, 2018.
U. Büchler, Brattoli, B., and Ommer, B., “Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning”, in Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 2018.PDF icon Article (5.34 MB)PDF icon buechler_eccv18_poster.pdf (1.65 MB)
J. Kunz and Jähne, B., “Investigating small scale air-sea exchange processes via thermography”, Front. Mech. Eng., vol. 26, 2018.
O. Hosseini Jafari, Mustikovela, S. K., Pertsch, K., Brachmann, E., and Rother, C., “iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects”, ACCV. Proceedings, in press. 2018.PDF icon Technical Report (3.28 MB)
F. Schimmel, “Learnability of Approximated Graph Cut Segmentation”, Heidelberg University, 2018.
E. Brachmann and Rother, C., “Learning Less is More - 6D Camera Localization via 3D Surface Regression”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2018, pp. 4654–4662.
M. Weiler, Hamprecht, F. A., and Storath, M., “Learning Steerable Filters for Rotation Equivariant CNNs”, CVPR. Proceedings. pp. 849-858, 2018.PDF icon Technical Report (1.35 MB)
O. Ghori, Mackowiak, R., Bautista, M., Beuter, N., Drumond, L., Diego, F., and Ommer, B., “Learning to Forecast Pedestrian Intention from Pose Dynamics”, in Intelligent Vehicles, IEEE, 2018, 2018.
W. Erb, Weinmann, A., Ahlborg, M., Brandt, C., Bringout, G., Buzug, T. M., Frikel, J., Kaethner, C., Knopp, T., März, T., Möddel, M., Storath, M., and Weber, A., “Mathematical Analysis of the 1D Model and Reconstruction Schemes for Magnetic Particle Imaging”, Inverse Problems, vol. 34, no. 5, 2018.

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