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D. Hoiem, Rother, C., and Winn, J., “3D LayoutCRF for multi-view object class recognition and segmentation”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007.

A. Shesh, Criminisi, A., Rother, C., and Smyth, G., “3D-aware image editing for out of bounds photography”, in Proceedings - Graphics Interface, 2009, pp. 47–54.

A

D. Kainmueller, Jug, F., Rother, C., and Myers, G., “Active graph matching for automatic joint segmentation and annotation of C. elegans”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8673 LNCS, pp. 81–88.

M. Sindeev, Konushin, A., and Rother, C., “Alpha-flow for video matting”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, vol. 7726 LNCS, pp. 438–452.

O. Hosseini Jafari, Groth, O., Kirillov, A., Yang, M. Ying, and Rother, C., “Analyzing modular CNN architectures for joint depth prediction and semantic segmentation”, in Proceedings - IEEE International Conference on Robotics and Automation, 2017, pp. 4620–4627.

V. Kolmogorov, Boykov, Y., and Rother, C., “Applications of parametric maxflow in computer vision”, in Proceedings of the IEEE International Conference on Computer Vision, 2007.

V. Kolmogorov, Boykov, Y., and Rother, C., “Applications of parametric maxflow in computer vision”, in Proceedings of the IEEE International Conference on Computer Vision, 2007.

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.

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.

H. Abu Alhaija, Mustikovela, S. Karthik, Mescheder, L., Geiger, A., and Rother, C., “Augmented reality meets deep learning for car instance segmentation in urban scenes”, in British Machine Vision Conference 2017, BMVC 2017, 2017.

B

P. Vincent Gehler, Rother, C., Blake, A., Minka, T., and Sharp, T., “Bayesian color constancy revisited”, in 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2008.

J. Kruse, Ardizzone, L., Rother, C., and Köthe, U., “Benchmarking Invertible Architectures on Inverse Problems”, i, 2019.

C. Kamann and Rother, C., “Benchmarking the Robustness of Semantic Segmentation Models”, 2019.

V. Kolmogorov, Criminisi, A., Blake, A., Cross, G., and Rother, C., “Bi-layer segmentation of binocular stereo video”, in Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, 2005, vol. II, pp. 407–414.

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.

A. Behl, Hosseini Jafari, O., Mustikovela, S. Karthik, Abu Alhaija, H., Rother, C., and Geiger, A., “Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?”, in Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 2593–2602.

A. Behl, Hosseini Jafari, O., Mustikovela, S. Karthik, Abu Alhaija, H., Rother, C., and Geiger, A., “Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?”, in Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 2593–2602.

V. Lempitsky, Blake, A., and Rother, C., “Branch-and-mincut: Global optimization for image segmentation with high-level priors”, Journal of Mathematical Imaging and Vision, vol. 44, pp. 315–329, 2012.

C

S. Karthik Mustikovela, Yang, M. Ying, and Rother, C., “Can ground truth label propagation from video help semantic segmentation?”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, vol. 9915 LNCS, pp. 804–820.

R. Mackowiak, Lenz, P., Ghori, O., Diego, F., Lange, O., and Rother, C., “CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation”, in British Machine Vision Conference 2018, BMVC 2018, 2019.

A. Kannan, Winn, J., and Rother, C., “Clustering appearance and shape by learning jigsaws”, in Advances in Neural Information Processing Systems, 2007, pp. 657–664.

A. Kannan, Winn, J., and Rother, C., “Clustering appearance and shape by learning jigsaws”, in Advances in Neural Information Processing Systems, 2007, pp. 657–664.

R. Szeliski, Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., and Rother, C., “A comparative study of energy minimization methods for Markov random fields with smoothness-based priors”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, pp. 1068–1080, 2008.

R. Szeliski, Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., and Rother, C., “A comparative study of energy minimization methods for Markov random fields with smoothness-based priors”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, pp. 1068–1080, 2008.

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Sungwoong, K., Kausler, B. X., Lellmann, J., Komodakis, N., and Rother, C., “A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems”, in CVPR 2013. Proceedings, 2013.

Technical Report (1.35 MB)

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Lellmann, J., Komodakis, N., and Rother, C., “A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem”, in CVPR, 2013.

Technical Report (1.35 MB)

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, pp. 1-30, 2015.

Technical Report (1.5 MB)

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, Int.~J.~Comp.~Vision, 2015.

Technical Report (5.12 MB)

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, CoRR, vol. abs/1404.0533, 2014.

Technical Report (3.32 MB)

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, vol. 115, pp. 155–184, 2015.

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, CoRR, 2014.

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, vol. 115, pp. 155–184, 2015.

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, vol. 115, pp. 155–184, 2015.

V. Kolmogorov and Rother, C., “Comparison of energy minimization algorithms for highly connected graphs”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, vol. 3952 LNCS, pp. 1–15.

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