Publications
Conference Paper
Jancsary, J, Nowozin, S and Rother, C (2012).
Loss-specific training of non-parametric image restoration models: A new state of the art.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
7578 LNCS 112–125
Jancsary, J, Nowozin, S and Rother, C (2012).
Loss-specific training of non-parametric image restoration models: A new state of the art.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
7578 LNCS 112–125
Rother, C and Carlsson, S (2002).
Linear multi view reconstruction with missing data.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
2351 209–324
Rother, C and Carlsson, S (2001).
Linear multi view reconstruction and camera recovery.
Proceedings of the IEEE International Conference on Computer Vision.
1 42–49
Kruse, J, Rother, C and Schmidt, U (2017).
Learning to Push the Limits of Efficient FFT-Based Image Deconvolution.
Proceedings of the IEEE International Conference on Computer Vision.
2017-Octob 4596–4604
Jancsary, J, Nowozin, S and Rother, C (2013).
Learning convex QP relaxations for structured prediction.
30th International Conference on Machine Learning, ICML 2013. 1952–1960
Krull, A, Brachmann, E, Michel, F, Yang, M Ying, Gumhold, S and Rother, C (2015).
Learning analysis-by-synthesis for 6d pose estimation in RGB-D images.
Proceedings of the IEEE International Conference on Computer Vision.
2015 Inter 954–962
Kirillov, A, Schlesinger, D, Zheng, S, Savchynskyy, B, Torr, P H S and Rother, C (2017).
Joint training of generic CNN-CRF models with stochastic optimization.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
10112 LNCS 221–236.
http://host.robots.ox.ac.uk:8080/leaderboard Vicente, S, Kolmogorov, V and Rother, C (2009).
Joint optimization of segmentation and appearance models.
Proceedings of the IEEE International Conference on Computer Vision. 755–762
Levinkov, E, Uhrig, J, Tang, S, Omran, M, Insafutdinov, E, Kirillov, A, Rother, C, Brox, T, Schiele, B and Andres, B (2017).
Joint graph decomposition & node labeling: Problem, algorithms, applications.
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017.
2017-Janua 1904–1912
Hosseini Jafari, O, Mustikovela, S Karthik, Pertsch, K, Brachmann, E and Rother, C (2019).
iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
11363 LNCS 477–492
Mund, J, Michel, F, Dieke-Meier, F, Fricke, H, Meyer, L and Rother, C (2016).
Introducing LiDAR Point Cloud-based Object Classification for Safer Apron Operations.
International Symposium on Enhanced Solutions for Aircraft and Vehicle Surveillance Applications.
https://goo.gl/28Yoqh Kirillov, A, Levinkov, E, Andres, B, Savchynskyy, B and Rother, C (2017).
InstanceCut: From edges to instances with MultiCut.
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017.
2017-Janua 7322–7331
Kirillov, A, Savchynskyy, B, Schlesinger, D, Vetrov, D and Rother, C (2015).
Inferring M-best diverse labelings in a single one.
Proceedings of the IEEE International Conference on Computer Vision.
2015 Inter 1814–1822
Rhemann, C, Rother, C and Gelautz, M (2008).
Improving color modeling for alpha matting.
BMVC 2008 - Proceedings of the British Machine Vision Conference 2008 Töppe, E, Oswald, M R, Cremers, D and Rother, C (2011).
Image-based 3D modeling via cheeger sets.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
6492 LNCS 53–64
Lempitsky, V, Kohli, P, Rother, C and Sharp, T (2009).
Image segmentation with a bounding box prior.
Proceedings of the IEEE International Conference on Computer Vision. 277–284
Lempitsky, V, Blake, A and Rother, C (2008).
Image segmentation by branch-and-mincut.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
5305 LNCS 15–29
Lempitsky, V, Blake, A and Rother, C (2008).
Image segmentation by branch-and-mincut.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
5305 LNCS 15–29
Hornáček, M, Besse, F, Kautz, J, Fitzgibbon, A and Rother, C (2014).
Highly overparameterized optical flow using PatchMatch belief propagation.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
8691 LNCS 220–234
Abu Alhaija, H, Sellent, A, Kondermann, D and Rother, C (2015).
Graphflow—6D large displacement scene flow via graph matching.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
9358 285–296
He, K, Rhemann, C, Rother, C, Tang, X and Sun, J (2011).
A global sampling method for alpha matting.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2049–2056
Michel, F, Kirillov, A, Brachmann, E, Krull, A, Gumhold, S, Savchynskyy, B and Rother, C (2017).
Global hypothesis generation for 6D object pose estimation.
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017.
2017-Janua 115–124.
http://arxiv.org/abs/1612.02287 Pages
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