Conference Paper
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.
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.
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.
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.
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.
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.
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.
F. Michel, Kirillov, A., Brachmann, E., Krull, A., Gumhold, S., Savchynskyy, B., and Rother, C.,
“Global hypothesis generation for 6D object pose estimation”, in
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017, vol. 2017-Janua, pp. 115–124.
K. He, Rhemann, C., Rother, C., Tang, X., and Sun, J.,
“A global sampling method for alpha matting”, in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, pp. 2049–2056.
H. Abu Alhaija, Sellent, A., Kondermann, D., and Rother, C.,
“Graphflow—6D large displacement scene flow via graph matching”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9358, pp. 285–296.
C. Rhemann, Rother, C., Rav-Acha, A., and Sharp, T.,
“High resolution matting via interactive trimap segmentation”, in
26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2008.
C. Rhemann, Rother, C., Rav-Acha, A., and Sharp, T.,
“High resolution matting via interactive trimap segmentation”, in
26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2008.
C. Rhemann, Rother, C., Rav-Acha, A., and Sharp, T.,
“High resolution matting via interactive trimap segmentation”, in
26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2008.
M. Hornáček, Besse, F., Kautz, J., Fitzgibbon, A., and Rother, C.,
“Highly overparameterized optical flow using PatchMatch belief propagation”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8691 LNCS, pp. 220–234.
V. Lempitsky, Blake, A., and Rother, C.,
“Image segmentation by branch-and-mincut”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, vol. 5305 LNCS, pp. 15–29.
V. Lempitsky, Blake, A., and Rother, C.,
“Image segmentation by branch-and-mincut”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, vol. 5305 LNCS, pp. 15–29.
V. Lempitsky, Kohli, P., Rother, C., and Sharp, T.,
“Image segmentation with a bounding box prior”, in
Proceedings of the IEEE International Conference on Computer Vision, 2009, pp. 277–284.
E. Töppe, Oswald, M. R., Cremers, D., and Rother, C.,
“Image-based 3D modeling via cheeger sets”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 6492 LNCS, pp. 53–64.
C. Rhemann, Rother, C., and Gelautz, M.,
“Improving color modeling for alpha matting”, in
BMVC 2008 - Proceedings of the British Machine Vision Conference 2008, 2008.
A. Kirillov, Savchynskyy, B., Schlesinger, D., Vetrov, D., and Rother, C.,
“Inferring M-best diverse labelings in a single one”, in
Proceedings of the IEEE International Conference on Computer Vision, 2015, vol. 2015 Inter, pp. 1814–1822.
A. Kirillov, Levinkov, E., Andres, B., Savchynskyy, B., and Rother, C.,
“InstanceCut: From edges to instances with MultiCut”, in
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017, vol. 2017-Janua, pp. 7322–7331.
K. Schelten, Nowozin, S., Jancsary, J., Rother, C., and Roth, S.,
“Interleaved regression tree field cascades for blind image deconvolution”, in
Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015, 2015, pp. 494–501.
J. Mund, Michel, F., Dieke-Meier, F., Fricke, H., Meyer, L., and Rother, C.,
“Introducing LiDAR Point Cloud-based Object Classification for Safer Apron Operations”, in
International Symposium on Enhanced Solutions for Aircraft and Vehicle Surveillance Applications, 2016.
O. Hosseini Jafari, Mustikovela, S. Karthik, Pertsch, K., Brachmann, E., and Rother, C.,
“iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, vol. 11363 LNCS, pp. 477–492.
E. Levinkov, Uhrig, J., Tang, S., Omran, M., Insafutdinov, E., Kirillov, A., Rother, C., Brox, T., Schiele, B., and Andres, B.,
“Joint graph decomposition & node labeling: Problem, algorithms, applications”, in
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017, vol. 2017-Janua, pp. 1904–1912.
S. Vicente, Kolmogorov, V., and Rother, C.,
“Joint optimization of segmentation and appearance models”, in
Proceedings of the IEEE International Conference on Computer Vision, 2009, pp. 755–762.
A. Kirillov, Schlesinger, D., Zheng, S., Savchynskyy, B., Torr, P. H. S., and Rother, C.,
“Joint training of generic CNN-CRF models with stochastic optimization”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, vol. 10112 LNCS, pp. 221–236.
A. Krull, Brachmann, E., Michel, F., Yang, M. Ying, Gumhold, S., and Rother, C.,
“Learning analysis-by-synthesis for 6d pose estimation in RGB-D images”, in
Proceedings of the IEEE International Conference on Computer Vision, 2015, vol. 2015 Inter, pp. 954–962.
J. Jancsary, Nowozin, S., and Rother, C.,
“Learning convex QP relaxations for structured prediction”, in
30th International Conference on Machine Learning, ICML 2013, 2013, pp. 1952–1960.
]