Publications

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S. Meister, Izadi, S., Kohli, P., Hämmerle, M., Rother, C., and Kondermann, D., When Can We Use KinectFusion for Ground Truth Acquisition?, in Workshop on Color-Depth Camera Fusion in Robotics, IEEE International Conference on Intelligent Robots and Systems, 2012.
M. Hoai Nguyen, Torresani, L., De La Torre, F., and Rother, C., Weakly supervised discriminative localization and classification: A joint learning process, in Proceedings of the IEEE International Conference on Computer Vision, 2009, pp. 1925–1932.
M. Hoai Nguyen, Torresani, L., De La Torre, F., and Rother, C., Weakly supervised discriminative localization and classification: A joint learning process, in Proceedings of the IEEE International Conference on Computer Vision, 2009, pp. 1925–1932.
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A. Mansfield, Gehler, P., Van Gool, L., and Rother, C., Visibility maps for improving seam carving, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 6554 LNCS, pp. 131–144.
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P. Kohli, Nickisch, H., Rother, C., and Rhemann, C., User-centric learning and evaluation of interactive segmentation systems, International Journal of Computer Vision, vol. 100, pp. 261–274, 2012.
D. S. Kirk, Sellen, A. J., Rother, C., and Wood, K. R., Understanding photowork, in Conference on Human Factors in Computing Systems - Proceedings, 2006, vol. 2, pp. 761–770.
D. Richmond, Kainmueller, D., Glocker, B., Rother, C., and Myers, G., Uncertainty-driven forest predictors for vertebra localization and segmentation, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9349. pp. 653–660, 2015.
E. Brachmann, Michel, F., Krull, A., Yang, M. Ying, Gumhold, S., and Rother, C., Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 3364–3372.
E. Brachmann, Michel, F., Krull, A., Yang, M. Ying, Gumhold, S., and Rother, C., Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 3364–3372.
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H. Schilling, Diebold, M., Rother, C., and Jähne, B., Trust your Model: Light Field Depth Estimation with inline Occlusion Handling, CVPR. Proceedings. 2018.PDF icon Technical Report (5.46 MB)
H. Schilling, Diebold, M., Rother, C., and Jähne, B., Trust your Model: Light Field Depth Estimation with Inline Occlusion Handling, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2018, pp. 4530–4538.
B. Glocker, T. Heibel, H., Navab, N., Kohli, P., and Rother, C., TriangleFlow: Optical flow with triangulation-based higher-order likelihoods, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, vol. 6313 LNCS, pp. 272–285.
J. Shotton, Winn, J., Rother, C., and Criminisi, A., TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, vol. 3951 LNCS, pp. 1–15.
J. Shotton, Winn, J., Rother, C., and Criminisi, A., TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context, International Journal of Computer Vision, vol. 81, pp. 2–23, 2009.
S. Tourani, Shekhovtsov, A., Rother, C., and Savchynskyy, B., Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization, in AISTATS 2020, 2020.PDF icon PDF (2.58 MB)
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M. Bleyer, Rother, C., and Kohli, P., Surface stereo with soft segmentation, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 1570–1577.
D. Singaraju, Rother, C., and Rhemann, C., Supplementary material for New Appearance Models for Image Matting, 2009.
A. Sellent, Rother, C., and Roth, S., Stereo Video Deblurring-Supplemental Material, 2016.
A. Sellent, Rother, C., and Roth, S., Stereo video deblurring, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, vol. 9906 LNCS, pp. 558–575.
M. Bleyer, Gelautz, M., Rother, C., and Rhemann, C., A stereo approach that handles the matting problem via imagewarping, in 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, 2009, vol. 2009 IEEE, pp. 501–508.
M. Hornáček, Fitzgibbon, A., and Rother, C., SphereFlow: 6 DoF scene flow from RGB-D pairs, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2014, pp. 3526–3533.
C. Rhemann, Rother, C., Kohli, P., and Gelautz, M., A spatially varying PSF-based prior for alpha matting, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 2149–2156.
C. Rother, Sparse Higher Order Functions of Discrete Variables–-Representation and Optimization, research.microsoft.com, vol. 45, 2011.
M. Hullin, Klein, R., Schultz, T., Yao, A., Li, W., Hosseini Jafari, O., and Rother, C., Semantic-Aware Image Smoothing, Vision, Modeling, and Visualization, 2017.
A. Zouhar, Rother, C., and Fuchs, S., Semantic 3-D labeling of ear implants using a global parametric transition prior, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9350, pp. 177–184.
A. Mansfield, Gehler, P., Van Gool, L., and Rother, C., Scene carving: Scene consistent image retargeting, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, vol. 6311 LNCS, pp. 143–156.
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A. Bhowmik, Gumhold, S., Rother, C., and Brachmann, E., Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task, 2019.
J. Jancsary, Nowozin, S., Sharp, T., and Rother, C., Regression Tree Fields An efficient, non-parametric approach to image labeling problems, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012, pp. 2376–2383.
J. Jancsary, Nowozin, S., Sharp, T., and Rother, C., Regression Tree Fields An efficient, non-parametric approach to image labeling problems, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012, pp. 2376–2383.
R. Nair, Fitzgibbon, A., Kondermann, D., and Rother, C., Reflection modeling for passive stereo, in Proceedings of the IEEE International Conference on Computer Vision, 2015, vol. 2015 Inter, pp. 2291–2299.
P. Vincent Gehler, Rother, C., Kiefel, M., Zhang, L., and Schölkopf, B., Recovering intrinsic images with a global sparsity prior on reflectance, in Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011, 2011.
D. Massiceti, Krull, A., Brachmann, E., Rother, C., and Torr, P. H. S., Random Forests versus Neural Networks − What's best for camera location. 2017.
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P. Pletscher, Nowozin, S., Kohli, P., and Rother, C., Putting MAP back on the map, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 6835 LNCS, pp. 111–121.
P. Pletscher, Nowozin, S., Kohli, P., and Rother, C., Putting MAP back on the map, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 6835 LNCS, pp. 111–121.
C. Rother, Carlsson, S., and Tell, D., Projective factorization of planes and cameras in multiple views, in Proceedings - International Conference on Pattern Recognition, 2002, vol. 16, pp. 737–740.

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