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B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”. 2014.

B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”, J. Math. Imag. Vision, vol. 52, pp. 436–458, 2015.

J. H. Kappes, Speth, M., Andres, B., Reinelt, G., and Schnörr, C., “Globally Optimal Image Partitioning by Multicuts”, in EMMCVPR, 2011, pp. 31-44.

Technical Report (7.3 MB)

J. H. Kappes, Speth, M., Andres, B., Reinelt, G., and Schnörr, C., “Globally Optimal Image Partitioning by Multicuts”, in EMMCVPR, 2011.

Technical Report (7.47 MB)

J. Hendrik Kappes, Speth, M., Andres, B., Reinelt, G., and Schnörr, C., “Globally Optimal Image Partitioning by Multicuts”, in EMMCVPR, 2011.

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)

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.

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

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.

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.

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

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.

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.

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

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.

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.

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

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.

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. 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.

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

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

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

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)

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.

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)

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