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C. Haubold, Ales, J., Wolf, S., and Hamprecht, F. A., “A Generalized Successive Shortest Paths Solver for Tracking Dividing Targets”, ECCV. Proceedings, vol. LNCS 9911. Springer, pp. 566-582, 2016.

Technical Report (1.18 MB)

J. Schmähling and Hamprecht, F. A., “Generalizing the Abbott-Firestone curve by two new surface descriptors”, Wear, vol. 262, pp. 1360-1371, 2007.

Technical Report (877.34 KB)

S. Wanner, Fehr, J., and Jähne, B., “Generating EPI representations of 4D light fields with a single lens focused plenoptic camera”, Advances in Visual Computing. Springer, p. 90--101, 2011.

F. A. Hamprecht, Scott, W. R. P., and van Gunsteren, W. F., “Generation of pseudo-native protein structures for threading”, Proteins, vol. 28, pp. 522-529, 1997.

J. Klinke and Long, S. R., “Generation of short waves by wave-current interaction”, in Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International, 2000, p. 1084--1086.

J. C. Rubio, Eigenstetter, A., and Ommer, B., “Generative Regularization with Latent Topics for Discriminative Object Recognition”, Pattern Recognition, vol. 48, p. 3871--3880, 2015.

Technical Report (5.49 MB)

V. Gulshan, Rother, C., Criminisi, A., Blake, A., and Zisserman, A., “Geodesic star convexity for interactive image segmentation”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 3129–3136.

U. Köthe, Andres, B., Kröger, T., and Hamprecht, F. A., “Geometric Analysis of 3D Electron Microscopy Data”, in Proceedings of Workshop on Discrete Geometry and Mathematical Morphology (WADGMM), 2010, pp. 22-26.

Technical Report (1.43 MB)

F. Aström and Schnörr, C., “A Geometric Approach for Color Image Regularization”, Comp. Vision Image Understanding, vol. 165, pp. 43–59, 2017.

F. Aström and Schnörr, C., “A Geometric Approach to Color Image Regularization”. 2016.

F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “A Geometric Approach to Image Labeling”, in Proc. ECCV, 2016.

A. Zern, Rohr, K., and Schnörr, C., “Geometric Image Labeling with Global Convex Labeling Constraints”, in EMMCVPR, 2018, vol. 10746, pp. 533–547.

A. Zern, Rohr, K., and Schnörr, C., “Geometric Image Labeling with Global Convex Labeling Constraints”, in Proc. EMMCVPR, 2017.

H. Abu Alhaija, Mustikovela, S. K., Geiger, A., and Rother, C., “Geometric Image Synthesis”, ACCV. Proceedings, in press. 2018.

Technical Report (1.83 MB)

H. Abu Alhaija, Mustikovela, S. Karthik, Geiger, A., and Rother, C., “Geometric Image Synthesis”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, vol. 11366 LNCS, pp. 85–100.

A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, Inverse Problems, vol. 36, p. 034004 (33pp), 2020.

A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, Inverse Problems, 2019.

A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, preprint: arXiv, 2018.

M. Schultz, “Geometrische Kalibrierung von CCD-Kameras”, University of Heidelberg, 1997.

R. Rombach, Esser, P., and Ommer, B., “Geometry-Free View Synthesis: Transformers and no 3D Priors”, in Proceedings of the Intl. Conf. on Computer Vision (ICCV), 2021.

B. Savchynskyy and Schmidt, S., “Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study”, in Workshop on Inference for Probabilistic Graphical Models at ICCV. Proceedings, 2013.

B. Savchynskyy and Schmidt, S., “Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study”, arXiv:1210.4081, 2012.

T. Dierig, “Gewinnung von Tiefenkarten aus Fokusserien”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2002.

F. Michel, Kirillov, A., Brachmann, E., Krull, A., Gumhold, S., Savchynskyy, B., and Rother, C., “Global hypothesis generation for 6D object pose estimation”, in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017, vol. 2017-Janua, pp. 115–124.

B. Savchynskyy, Kappes, J. H., Swoboda, P., and Schnörr, C., “Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation”, in NIPS, 2013.

Technical Report (499.17 KB)

B. Savchynskyy, Kappes, J. H., Swoboda, P., and Schnörr, C., “Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation”, in NIPS. Proceedings, 2013, pp. 1950-1958.

B. Savchynskyy, Kappes, J. Hendrik, Swoboda, P., and Schnörr, C., “Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation”, in NIPS, 2013.

O. J. Woodford, “A Global Perspective on MAP Inference for Low-Level Vision Supplementary material to ICCV submission \# 1536”, Optimization, 2009.

K. He, Rhemann, C., Rother, C., Tang, X., and Sun, J., “A global sampling method for alpha matting”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, pp. 2049–2056.

C. Schnörr, Stiehl, H. - S., and Grigat, R. - R., “On Globally Asymptotically Stable Continuous-Time CNNs for Adaptive Smoothing of Multidimensional Signals”, in Proc. 4th IEEE Int. Workshop on Cellular Neural Networks and their Applications, Seville, Spain, 1996.

S. Wanner and Goldlücke, B., “Globally Consistent Depth Labeling of 4D Light Fields”, in CVPR. Proceedings, 2012, pp. 41-48.

S. Wanner and Goldlücke, B., “Globally Consistent Depth Labeling of 4D Lightfields”, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.

S. Wanner, Straehle, C. N., and Goldlücke, B., “Globally Consistent Multi-Label Assignment on the Ray Space of 4D Light Fields”, CVPR 2013. Proceedings, pp. 1011-1018, 2013.

S. Wanner, Straehle, C. N., and Goldlücke, B., “Globally consistent multi-label assignment on the ray space of 4D light fields”, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.

B. Andres, Kröger, T., Briggmann, K. L., Denk, W., Norogod, N., Knott, G. W., Köthe, U., and Hamprecht, F. A., “Globally Optimal Closed-Surface Segmentation for Connectomics”, in ECCV 2012. Proceedings, Part 3, 2012, pp. 778-791.

Technical Report (2.72 MB)

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