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G

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. Rother, Kolmogorov, V., and Blake, A., “"GrabCut" - Interactive foreground extraction using iterated graph cuts”, in ACM Transactions on Graphics, 2004, vol. 23, pp. 309–314.

S. Vicente, Kolmogorov, V., and Rother, C., “Graph cut based image segmentation with connectivity priors”, in 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2008.

H. Abu Alhaija, Sellent, A., Kondermann, D., and Rother, C., “Graphflow—6D large displacement scene flow via graph matching”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9358, pp. 285–296.

L. Ardizzone, Lüth, C., Kruse, J., Rother, C., and Köthe, U., “Guided Image Generation with Conditional Invertible Neural Networks”, 2019.

L. Ardizzone, Lüth, C., Kruse, J., Rother, C., and Köthe, U., “Guided Image Generation with Conditional Invertible Neural Networks”, 2019.

H

C. Rhemann, Rother, C., Rav-Acha, A., and Sharp, T., “High resolution matting via interactive trimap segmentation”, in 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2008.

C. Rhemann, Rother, C., Rav-Acha, A., and Sharp, T., “High resolution matting via interactive trimap segmentation”, in 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2008.

C. Rhemann, Rother, C., Rav-Acha, A., and Sharp, T., “High resolution matting via interactive trimap segmentation”, in 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2008.

V. Vineet, Rother, C., and Torr, P. H. S., “Higher order priors for joint intrinsic image, objects, and attributes estimation”, in Advances in Neural Information Processing Systems, 2013.

M. Hornáček, Besse, F., Kautz, J., Fitzgibbon, A., and Rother, C., “Highly overparameterized optical flow using PatchMatch belief propagation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8691 LNCS, pp. 220–234.

I

V. Lempitsky, Blake, A., and Rother, C., “Image segmentation by branch-and-mincut”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, vol. 5305 LNCS, pp. 15–29.

V. Lempitsky, Blake, A., and Rother, C., “Image segmentation by branch-and-mincut”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, vol. 5305 LNCS, pp. 15–29.

V. Lempitsky, Kohli, P., Rother, C., and Sharp, T., “Image segmentation with a bounding box prior”, in Proceedings of the IEEE International Conference on Computer Vision, 2009, pp. 277–284.

E. Töppe, Oswald, M. R., Cremers, D., and Rother, C., “Image-based 3D modeling via cheeger sets”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 6492 LNCS, pp. 53–64.

C. Rhemann, Rother, C., and Gelautz, M., “Improving color modeling for alpha matting”, in BMVC 2008 - Proceedings of the British Machine Vision Conference 2008, 2008.

A. Kirillov, Savchynskyy, B., Schlesinger, D., Vetrov, D., and Rother, C., “Inferring M-best diverse labelings in a single one”, in Proceedings of the IEEE International Conference on Computer Vision, 2015, vol. 2015 Inter, pp. 1814–1822.

A. Kirillov, Levinkov, E., Andres, B., Savchynskyy, B., and Rother, C., “InstanceCut: From edges to instances with MultiCut”, in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017, vol. 2017-Janua, pp. 7322–7331.

C. Rother and Kolmogorov, V., “Interactive foreground extraction using graph cut”, Advances in Markov \ldots, pp. 1–20, 2011.

K. Schelten, Nowozin, S., Jancsary, J., Rother, C., and Roth, S., “Interleaved regression tree field cascades for blind image deconvolution”, in Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015, 2015, pp. 494–501.

J. Mund, Michel, F., Dieke-Meier, F., Fricke, H., Meyer, L., and Rother, C., “Introducing LiDAR Point Cloud-based Object Classification for Safer Apron Operations”, in International Symposium on Enhanced Solutions for Aircraft and Vehicle Surveillance Applications, 2016.

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.

Technical Report (3.28 MB)

O. Hosseini Jafari, Mustikovela, S. Karthik, Pertsch, K., Brachmann, E., and Rother, C., “iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, vol. 11363 LNCS, pp. 477–492.

J

E. Levinkov, Uhrig, J., Tang, S., Omran, M., Insafutdinov, E., Kirillov, A., Rother, C., Brox, T., Schiele, B., and Andres, B., “Joint graph decomposition & node labeling: Problem, algorithms, applications”, in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017, vol. 2017-Janua, pp. 1904–1912.

S. Vicente, Kolmogorov, V., and Rother, C., “Joint optimization of segmentation and appearance models”, in Proceedings of the IEEE International Conference on Computer Vision, 2009, pp. 755–762.

A. Kirillov, Schlesinger, D., Zheng, S., Savchynskyy, B., Torr, P. H. S., and Rother, C., “Joint training of generic CNN-CRF models with stochastic optimization”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, vol. 10112 LNCS, pp. 221–236.

L

A. Krull, Brachmann, E., Michel, F., Yang, M. Ying, Gumhold, S., and Rother, C., “Learning analysis-by-synthesis for 6d pose estimation in RGB-D images”, in Proceedings of the IEEE International Conference on Computer Vision, 2015, vol. 2015 Inter, pp. 954–962.

J. Jancsary, Nowozin, S., and Rother, C., “Learning convex QP relaxations for structured prediction”, in 30th International Conference on Machine Learning, ICML 2013, 2013, pp. 1952–1960.

M. Hoai, Torresani, L., De La Torre, F., and Rother, C., “Learning discriminative localization from weakly labeled data”, in Pattern Recognition, 2014, vol. 47, pp. 1523–1534.

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.

J. Kruse, Rother, C., Schmidt, U., and Dresden, T. U., “Learning to Push the Limits of Efficient FFT-based Image Deconvolution - Supplemental Material”, 2017.

J. Kruse, Rother, C., and Schmidt, U., “Learning to Push the Limits of Efficient FFT-Based Image Deconvolution”, in Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 4596–4604.

T. Leistner, Schilling, H., Mackowiak, R., Gumhold, S., and Rother, C., “Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift”, in Proceedings - 2019 International Conference on 3D Vision, 3DV 2019, 2019, pp. 249–257.

PDF (8.94 MB)

C. Rother and Carlsson, S., “Linear multi view reconstruction and camera recovery”, in Proceedings of the IEEE International Conference on Computer Vision, 2001, vol. 1, pp. 42–49.

C. Rother and Carlsson, S., “Linear multi view reconstruction and camera recovery using a reference plane”, International Journal of Computer Vision, vol. 49, pp. 117–141, 2002.

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