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D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C., “Mapping auto-context decision forests to deep convnets for semantic segmentation”, in British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.

D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C., “Mapping auto-context decision forests to deep convnets for semantic segmentation”, in British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.

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P. Pinggera, Ramos, S., Gehrig, S., Franke, U., Rother, C., and Mester, R., “Lost and found: Detecting small road hazards for self-driving vehicles”, in IEEE International Conference on Intelligent Robots and Systems, 2016, vol. 2016-Novem, pp. 1099–1106.

J. Jancsary, Nowozin, S., and Rother, C., “Loss-specific training of non-parametric image restoration models: A new state of the art”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7578 LNCS, pp. 112–125.

J. Jancsary, Nowozin, S., and Rother, C., “Loss-specific training of non-parametric image restoration models: A new state of the art”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7578 LNCS, pp. 112–125.

V. Lempitsky, Rother, C., and Blake, A., “LogCut - Efficient graph cut optimization for markov random fields”, in Proceedings of the IEEE International Conference on Computer Vision, 2007.

W. Li, Hosseini Jafari, O., and Rother, C., “Localizing Common Objects Using Common Component Activation Map”, 2019.

C. Rother, “Linear multi-view reconstruction of points, lines, planes and cameras using a reference plane”, in Proceedings of the IEEE International Conference on Computer Vision, 2003, vol. 2, pp. 1210–1217.

C. Rother, “Linear Multi-View Reconstruction for Translating Cameras”, Nada.Kth.Se, 2003.

C. Rother and Carlsson, S., “Linear multi view reconstruction with missing data”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2002, vol. 2351, pp. 209–324.

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.

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.

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)

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.

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. 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.

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.

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.

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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.

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.

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.

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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. 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.

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.

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

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.

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.

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.

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.

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.

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.

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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.

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