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Mund, J, Zouhar, A, Meyer, L, Fricke, H and Rother, C (2015). Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons. Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems. 85–94
Mund, J, Zouhar, A, Meyer, L, Fricke, H and Rother, C (2015). Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons. Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems. 85–94
Lalonde, J François, Hoiem, D, Efros, A A, Rother, C, Winn, J and Criminisi, A (2007). Photo clip art. Proceedings of the ACM SIGGRAPH Conference on Computer Graphics. http://graphics.cs.cmu.edu/projects/photoclipart/
Besse, F, Rother, C, Fitzgibbon, A and Kautz, J (2014). PMBP: PatchMatch Belief Propagation for correspondence field estimation. International Journal of Computer Vision. Kluwer Academic Publishers. 110 2–13
Besse, F, Rother, C, Fitzgibbon, A and Kautz, J (2014). PMBP: PatchMatch Belief Propagation for correspondence field estimation. International Journal of Computer Vision. 110 2–13
Besse, F, Rother, C, Fitzgibbon, A and Kautz, J (2014). PMBP: PatchMatch Belief Propagation for correspondence field estimation. International Journal of Computer Vision. 110 2–13
Michel, F, Krull, A, Brachmann, E, Yang, M Ying, Gumhold, S and Rother, C (2015). Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression. 181.1–181.11
Krull, A, Brachmann, E, Nowozin, S, Michel, F, Shotton, J and Rother, C (2017). PoseAgent: Budget-constrained 6D object pose estimation via reinforcement learning. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua 2566–2574. http://arxiv.org/abs/1612.03779
Haller, S, Prakash, M, Hutschenreiter, L, Pietzsch, T, Rother, C, Jug, F, Swoboda, P and Savchynskyy, B (2020). A Primal-Dual Solver for Large-Scale Tracking-by-Assignment. AISTATS 2020PDF icon PDF (1.04 MB)
Kolmogorov, V, Criminisi, A, Blake, A, Cross, G and Rother, C (2006). Probabilistic fusion of stereo with color and contrast for bilayer segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28 1480–1492. http://research.microsoft.com/vision/cambridge
Rother, C, Carlsson, S and Tell, D (2002). Projective factorization of planes and cameras in multiple views. Proceedings - International Conference on Pattern Recognition. 16 737–740
Pletscher, P, Nowozin, S, Kohli, P and Rother, C (2011). Putting MAP back on the map. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6835 LNCS 111–121
Pletscher, P, Nowozin, S, Kohli, P and Rother, C (2011). Putting MAP back on the map. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6835 LNCS 111–121
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Massiceti, D, Krull, A, Brachmann, E, Rother, C and Torr, P H S (2017). Random Forests versus Neural Networks − What's best for camera location
Gehler, P Vincent, Rother, C, Kiefel, M, Zhang, L and Schölkopf, B (2011). Recovering intrinsic images with a global sparsity prior on reflectance. Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011
Nair, R, Fitzgibbon, A, Kondermann, D and Rother, C (2015). Reflection modeling for passive stereo. Proceedings of the IEEE International Conference on Computer Vision. 2015 Inter 2291–2299
Jancsary, J, Nowozin, S, Sharp, T and Rother, C (2012). Regression Tree Fields An efficient, non-parametric approach to image labeling problems. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2376–2383
Jancsary, J, Nowozin, S, Sharp, T and Rother, C (2012). Regression Tree Fields An efficient, non-parametric approach to image labeling problems. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2376–2383
Bhowmik, A, Gumhold, S, Rother, C and Brachmann, E (2019). Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task. http://arxiv.org/abs/1912.00623
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Mansfield, A, Gehler, P, Van Gool, L and Rother, C (2010). Scene carving: Scene consistent image retargeting. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6311 LNCS 143–156. www.fujifilm.com/products/3d/camera/finepix_
Zouhar, A, Rother, C and Fuchs, S (2015). Semantic 3-D labeling of ear implants using a global parametric transition prior. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9350 177–184
Hullin, M, Klein, R, Schultz, T, Yao, A, Li, W, Hosseini Jafari, O and Rother, C (2017). Semantic-Aware Image Smoothing. Vision, Modeling, and Visualization. https://hci.iwr.uni-heidelberg.de/vislearn/wp-content/uploads/2014/08/paper1024_CRC.pdf
Rother, C (2011). Sparse Higher Order Functions of Discrete Variables–-Representation and Optimization. research.microsoft.com. 45. http://research.microsoft.com/pubs/147370/RotherKohli-SparseHigherOrder.pdf
Rhemann, C, Rother, C, Kohli, P and Gelautz, M (2010). A spatially varying PSF-based prior for alpha matting. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2149–2156
Hornáček, M, Fitzgibbon, A and Rother, C (2014). SphereFlow: 6 DoF scene flow from RGB-D pairs. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 3526–3533
Bleyer, M, Gelautz, M, Rother, C and Rhemann, C (2009). A stereo approach that handles the matting problem via imagewarping. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. 2009 IEEE 501–508
Sellent, A, Rother, C and Roth, S (2016). Stereo video deblurring. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9906 LNCS 558–575
Sellent, A, Rother, C and Roth, S (2016). Stereo Video Deblurring-Supplemental Material
Singaraju, D, Rother, C and Rhemann, C (2009). Supplementary Material For New Appearance Models For Image Matting
Bleyer, M, Rother, C and Kohli, P (2010). Surface stereo with soft segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1570–1577
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Tourani, S, Shekhovtsov, A, Rother, C and Savchynskyy, B (2020). Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization. AISTATS 2020. https://gitlab.com/PDF icon PDF (2.58 MB)
Shotton, J, Winn, J, Rother, C and Criminisi, A (2009). TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. International Journal of Computer Vision. 81 2–23. http://jamie.shotton.org/work/code.html
Shotton, J, Winn, J, Rother, C and Criminisi, A (2006). TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3951 LNCS 1–15. http://research.microsoft.com/vision/cambridge/recognition/.
Glocker, B, T. Heibel, H, Navab, N, Kohli, P and Rother, C (2010). TriangleFlow: Optical flow with triangulation-based higher-order likelihoods. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6313 LNCS 272–285. http://vision.middlebury.edu/flow/
Schilling, H, Diebold, M, Rother, C and Jähne, B (2018). Trust your Model: Light Field Depth Estimation with inline Occlusion Handling. CVPR. ProceedingsPDF icon Technical Report (5.46 MB)

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