Publications
Conference Paper
Abu Alhaija, H, Mustikovela, S Karthik, Geiger, A and Rother, C (2019).
Geometric Image Synthesis.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
11366 LNCS 85–100.
https://youtu.be/W2tFCz9xJoU Gulshan, V, Rother, C, Criminisi, A, Blake, A and Zisserman, A (2010).
Geodesic star convexity for interactive image segmentation.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 3129–3136
Torresani, L, Kolmogorov, V and Rother, C (2008).
Feature correspondence via graph matching: Models and global optimization.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
5303 LNCS 596–609
Torresani, L, Kolmogorov, V and Rother, C (2008).
Feature correspondence via graph matching: Models and global optimization.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
5303 LNCS 596–609
Bleyer, M, Rhemann, C and Rother, C (2012).
Extracting 3D scene-consistent object proposals and depth from stereo images.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
7576 LNCS 467–481.
http://vision.middlebury.edu/stereo/ Bleyer, M, Rhemann, C and Rother, C (2012).
Extracting 3D scene-consistent object proposals and depth from stereo images.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
7576 LNCS 467–481
Brachmann, E, Krull, A, Nowozin, S, Shotton, J, Michel, F, Gumhold, S and Rother, C (2017).
DSAC - Differentiable RANSAC for camera localization.
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017.
2017-Janua 2492–2500.
http://arxiv.org/abs/1611.05705 Sellen, A, Fogg, A, Aitken, M, Hodges, S, Rother, C and Wood, K (2007).
Do life-logging technologies support memory for the past? An experimental study using sensecam.
Conference on Human Factors in Computing Systems - Proceedings. 81–90
Hornáček, M, Rhemann, C, Gelautz, M and Rother, C (2013).
Depth super resolution by rigid body self-similarity in 3D.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1123–1130
Zheng, S, Cheng, M Ming, Warrell, J, Sturgess, P, Vineet, V, Rother, C and Torr, P H S (2014).
Dense semantic image segmentation with objects and attributes.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 3214–3221.
http://www.robots.ox.ac.uk/˜tvg/http://tu-dresden.de/inf/cvld Li, W, Hosseini Jafari, O and Rother, C (2019).
Deep Object Co-segmentation.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
11363 LNCS 638–653
Nowozin, S, Rother, C, Bagon, S, Sharp, T, Yao, B and Kohli, P (2011).
Decision tree fields.
Proceedings of the IEEE International Conference on Computer Vision. 1668–1675
Shekhovtsov, A, Kohli, P and Rother, C (2012).
Curvature prior for MRF-based segmentation and shape inpainting.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
7476 LNCS 41–51
Shekhovtsov, A, Kohli, P and Rother, C (2012).
Curvature prior for MRF-based segmentation and shape inpainting.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
7476 LNCS 41–51.
http://arxiv.org/abs/1109.1480 Vicente, S, Kolmogorov, V and Rother, C (2010).
Cosegmentation revisited: Models and optimization.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
6312 LNCS 465–479
Royer, L A, Richmond, D L, Rother, C, Andres, B and Kainmueller, D (2016).
Convexity shape constraints for image segmentation.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
2016-Decem 402–410.
http://arxiv.org/abs/1509.02122 Kluger, F, Brachmann, E, Ackermann, H, Rother, C, Yang, M Ying and Rosenhahn, B (2020).
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus.
CVPR 2020.
http://arxiv.org/abs/2001.02643
PDF (9.95 MB) Kolmogorov, V and Rother, C (2006).
Comparison of energy minimization algorithms for highly connected graphs.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
3952 LNCS 1–15
Kolmogorov, V and Rother, C (2006).
Comparison of energy minimization algorithms for highly connected graphs.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
3952 LNCS 1–15
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Sungwoong, K, Kausler, B X, Lellmann, J, Komodakis, N and Rother, C (2013).
A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems.
CVPR 2013. Proceedings Technical Report (1.35 MB) Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Lellmann, J, Komodakis, N and Rother, C (2013).
A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem.
CVPR Technical Report (1.35 MB) Kannan, A, Winn, J and Rother, C (2007).
Clustering appearance and shape by learning jigsaws.
Advances in Neural Information Processing Systems. 657–664
Kannan, A, Winn, J and Rother, C (2007).
Clustering appearance and shape by learning jigsaws.
Advances in Neural Information Processing Systems. 657–664
Mackowiak, R, Lenz, P, Ghori, O, Diego, F, Lange, O and Rother, C (2019).
CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation.
British Machine Vision Conference 2018, BMVC 2018 Mustikovela, S Karthik, Yang, M Ying and Rother, C (2016).
Can ground truth label propagation from video help semantic segmentation?.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
9915 LNCS 804–820
Behl, A, Hosseini Jafari, O, Mustikovela, S Karthik, Abu Alhaija, H, Rother, C and Geiger, A (2017).
Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?.
Proceedings of the IEEE International Conference on Computer Vision.
2017-Octob 2593–2602
Behl, A, Hosseini Jafari, O, Mustikovela, S Karthik, Abu Alhaija, H, Rother, C and Geiger, A (2017).
Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?.
Proceedings of the IEEE International Conference on Computer Vision.
2017-Octob 2593–2602
Hodaň, T, Michel, F, Brachmann, E, Kehl, W, Buch, A Glent, Kraft, D, Drost, B, Vidal, J, Ihrke, S, Zabulis, X, Sahin, C, Manhardt, F, Tombari, F, Kim, T Kyun, Matas, J and Rother, C (2018).
BOP: Benchmark for 6D object pose estimation.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
11214 LNCS 19–35.
http://arxiv.org/abs/1808.08319 Pages
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