Publications
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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 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
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
I
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
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
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
Rhemann, C, Rother, C and Gelautz, M (2008).
Improving color modeling for alpha matting.
BMVC 2008 - Proceedings of the British Machine Vision Conference 2008 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
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
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 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
J
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
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
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 L
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
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
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
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
Pages
Technical Report (3.28 MB)
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