Conference Paper
H. Abu Alhaija, Mustikovela, S. Karthik, Geiger, A., and Rother, C.,
“Geometric Image Synthesis”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, vol. 11366 LNCS, pp. 85–100.
V. Gulshan, Rother, C., Criminisi, A., Blake, A., and Zisserman, A.,
“Geodesic star convexity for interactive image segmentation”, in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 3129–3136.
L. Torresani, Kolmogorov, V., and Rother, C.,
“Feature correspondence via graph matching: Models and global optimization”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, vol. 5303 LNCS, pp. 596–609.
L. Torresani, Kolmogorov, V., and Rother, C.,
“Feature correspondence via graph matching: Models and global optimization”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, vol. 5303 LNCS, pp. 596–609.
M. Bleyer, Rhemann, C., and Rother, C.,
“Extracting 3D scene-consistent object proposals and depth from stereo images”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7576 LNCS, pp. 467–481.
M. Bleyer, Rhemann, C., and Rother, C.,
“Extracting 3D scene-consistent object proposals and depth from stereo images”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7576 LNCS, pp. 467–481.
E. Brachmann and Rother, C.,
“Expert sample consensus applied to camera re-localization”, in
Proceedings of the IEEE International Conference on Computer Vision, 2019, vol. 2019-Octob, pp. 7524–7533.
E. Brachmann, Krull, A., Nowozin, S., Shotton, J., Michel, F., Gumhold, S., and Rother, C.,
“DSAC - Differentiable RANSAC for camera localization”, in
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017, vol. 2017-Janua, pp. 2492–2500.
A. Sellen, Fogg, A., Aitken, M., Hodges, S., Rother, C., and Wood, K.,
“Do life-logging technologies support memory for the past? An experimental study using sensecam”, in
Conference on Human Factors in Computing Systems - Proceedings, 2007, pp. 81–90.
C. Rother, Kumar, S., Kolmogorov, V., and Blake, A.,
“Digital tapestry [automatic image synthesis]”, in
Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 2005, vol. 1, pp. 589–596.
S. Ramos, Gehrig, S., Pinggera, P., Franke, U., and Rother, C.,
“Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling”, in
IEEE Intelligent Vehicles Symposium, Proceedings, 2017, pp. 1025–1032.
M. Hornáček, Rhemann, C., Gelautz, M., and Rother, C.,
“Depth super resolution by rigid body self-similarity in 3D”, in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2013, pp. 1123–1130.
S. Zheng, Cheng, M. Ming, Warrell, J., Sturgess, P., Vineet, V., Rother, C., and Torr, P. H. S.,
“Dense semantic image segmentation with objects and attributes”, in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2014, pp. 3214–3221.
W. Li, Hosseini Jafari, O., and Rother, C.,
“Deep Object Co-segmentation”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, vol. 11363 LNCS, pp. 638–653.
S. Nowozin, Rother, C., Bagon, S., Sharp, T., Yao, B., and Kohli, P.,
“Decision tree fields”, in
Proceedings of the IEEE International Conference on Computer Vision, 2011, pp. 1668–1675.
A. Shekhovtsov, Kohli, P., and Rother, C.,
“Curvature prior for MRF-based segmentation and shape inpainting”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7476 LNCS, pp. 41–51.
A. Shekhovtsov, Kohli, P., and Rother, C.,
“Curvature prior for MRF-based segmentation and shape inpainting”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7476 LNCS, pp. 41–51.
A. Shekhovtsov, Kohli, P., and Rother, C.,
“Curvature prior for MRF-based segmentation and shape inpainting”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7476 LNCS, pp. 41–51.
D. Schlesinger, Jug, F., Myers, G., Rother, C., and Kainmueller, D.,
“Crowd sourcing image segmentation with iaSTAPLE”, in
Proceedings - International Symposium on Biomedical Imaging, 2017, pp. 401–405.
S. Vicente, Kolmogorov, V., and Rother, C.,
“Cosegmentation revisited: Models and optimization”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, vol. 6312 LNCS, pp. 465–479.
C. Rother, Kolmogorov, V., Minka, T., and Blake, A.,
“Cosegmentation of image pairs by histogram matching - Incorporating a global constraint into MRFs”, in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, vol. 1, pp. 994–1000.
L. A. Royer, Richmond, D. L., Rother, C., Andres, B., and Kainmueller, D.,
“Convexity shape constraints for image segmentation”, in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 402–410.
F. Kluger, Brachmann, E., Ackermann, H., Rother, C., Yang, M. Ying, and Rosenhahn, B.,
“CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus”, in
CVPR 2020, 2020.
PDF (9.95 MB) V. Kolmogorov and Rother, C.,
“Comparison of energy minimization algorithms for highly connected graphs”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, vol. 3952 LNCS, pp. 1–15.
V. Kolmogorov and Rother, C.,
“Comparison of energy minimization algorithms for highly connected graphs”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, vol. 3952 LNCS, pp. 1–15.
J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Sungwoong, K., Kausler, B. X., Lellmann, J., Komodakis, N., and Rother, C.,
“A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems”, in
CVPR 2013. Proceedings, 2013.
Technical Report (1.35 MB) J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Lellmann, J., Komodakis, N., and Rother, C.,
“A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem”, in
CVPR, 2013.
Technical Report (1.35 MB) A. Kannan, Winn, J., and Rother, C.,
“Clustering appearance and shape by learning jigsaws”, in
Advances in Neural Information Processing Systems, 2007, pp. 657–664.
A. Kannan, Winn, J., and Rother, C.,
“Clustering appearance and shape by learning jigsaws”, in
Advances in Neural Information Processing Systems, 2007, pp. 657–664.
R. Mackowiak, Lenz, P., Ghori, O., Diego, F., Lange, O., and Rother, C.,
“CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation”, in
British Machine Vision Conference 2018, BMVC 2018, 2019.
S. Karthik Mustikovela, Yang, M. Ying, and Rother, C.,
“Can ground truth label propagation from video help semantic segmentation?”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, vol. 9915 LNCS, pp. 804–820.
A. Behl, Hosseini Jafari, O., Mustikovela, S. Karthik, Abu Alhaija, H., Rother, C., and Geiger, A.,
“Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?”, in
Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 2593–2602.
A. Behl, Hosseini Jafari, O., Mustikovela, S. Karthik, Abu Alhaija, H., Rother, C., and Geiger, A.,
“Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?”, in
Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 2593–2602.
T. Hodaň, 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.,
“BOP: Benchmark for 6D object pose estimation”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11214 LNCS, pp. 19–35.
]