Conference Paper
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., 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 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,
“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.
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.
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.
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.
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.
A. Kirillov, Schlesinger, D., Vetrov, D., Rother, C., and Savchynskyy, B.,
“M-best-diverse labelings for submodular energies and beyond”, in
Advances in Neural Information Processing Systems, 2015, vol. 2015-Janua, pp. 613–621.
S. Tourani, Shekhovtsov, A., Rother, C., and Savchynskyy, B.,
“MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11208 LNCS, pp. 264–281.
E. Brachmann and Rother, C.,
“Neural-guided RANSAC: Learning where to sample model hypotheses”, in
Proceedings of the IEEE International Conference on Computer Vision, 2019, vol. 2019-Octob, pp. 4321–4330.
PDF (8.02 MB) D. Singaraju, Rother, C., and Rhemann, C.,
“New appearance models for natural image matting”, in
2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, 2009, vol. 2009 IEEE, pp. 659–666.
J. Jancsary, Nowozin, S., and Rother, C.,
“Non-parametric crfs for image labeling”, in
NIPS Workshop Modern Nonparametric Methods in Machine Learning, 2012, pp. 1–5.
P. Márquez-Neila, Kohli, P., Rother, C., and Baumela, L.,
“Non-parametric higher-order random fields for image segmentation”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8694 LNCS, pp. 269–284.
S. Vicente, Rother, C., and Kolmogorov, V.,
“Object cosegmentation”, in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, pp. 2217–2224.
S. Zheng, Prisacariu, V. Adrian, Averkiou, M., Cheng, M. Ming, Mitra, N. J., Shotton, J., Torr, P. H. S., and Rother, C.,
“Object proposals estimation in depth image using compact 3D shape manifolds”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9358, pp. 196–208.
M. Bleyer, Rother, C., Kohli, P., Scharstein, D., and Sinha, S.,
“Object stereo Joint stereo matching and object segmentation”, in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, pp. 3081–3088.
C. Rother, Kolmogorov, V., Lempitsky, V., and Szummer, M.,
“Optimizing binary MRFs via extended roof duality”, in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007.
T. J. Adler, Ayala, L., Ardizzone, L., Kenngott, H. G., Vemuri, A., Müller-Stich, B. P., Rother, C., Köthe, U., and Maier-Hein, L.,
“Out of Distribution Detection for Intra-operative Functional Imaging”, in
MICCAI UNSURE Workshop 2019, 2019, vol. 11840 LNCS, pp. 75–82.
PDF (3.1 MB) P. Kohli, Shekhovtsov, A., Rother, C., Kolmogorov, V., and Torr, P.,
“On partial optimality in multi-label MRFs”, in
Proceedings of the 25th International Conference on Machine Learning, 2008, pp. 480–487.
C. Rhemann, Rother, C., Wang, J., Gelautz, M., Kohli, P., and Rott, P.,
“A perceptually motivated online benchmark for image matting”, in
2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, 2009, vol. 2009 IEEE, pp. 1826–1833.
C. Rhemann, Rother, C., Wang, J., Gelautz, M., Kohli, P., and Rott, P.,
“A perceptually motivated online benchmark for image matting”, in
2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, 2009, vol. 2009 IEEE, pp. 1826–1833.
C. Rhemann, Rother, C., Wang, J., Gelautz, M., Kohli, P., and Rott, P.,
“A perceptually motivated online benchmark for image matting”, in
2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, 2009, vol. 2009 IEEE, pp. 1826–1833.
J. Mund, Zouhar, A., Meyer, L., Fricke, H., and Rother, C.,
“Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons”, in
Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems, 2015, pp. 85–94.
J. Mund, Zouhar, A., Meyer, L., Fricke, H., and Rother, C.,
“Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons”, in
Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems, 2015, pp. 85–94.
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