Publications
Journal Article
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kröger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2014).
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems.
CoRR.
abs/1404.0533.
http://hci.iwr.uni-heidelberg.de/opengm2/ Technical Report (3.32 MB) Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kröger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2015).
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems.
International Journal of Computer Vision.
115 155–184.
http://hci.iwr.uni-heidelberg.de/opengm2/ Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kröger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2015).
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems.
International Journal of Computer Vision.
115 155–184
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kröger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2015).
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems.
International Journal of Computer Vision.
115 155–184
Conference Paper
Jancsary, J, Nowozin, S, Sharp, T and Rother, C (2012).
Regression Tree Fields An efficient, non-parametric approach to image labeling problems.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2376–2383
Jancsary, J, Nowozin, S, Sharp, T and Rother, C (2012).
Regression Tree Fields An efficient, non-parametric approach to image labeling problems.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2376–2383
Pletscher, P, Nowozin, S, Kohli, P and Rother, C (2011).
Putting MAP back on the map.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
6835 LNCS 111–121
Pletscher, P, Nowozin, S, Kohli, P and Rother, C (2011).
Putting MAP back on the map.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
6835 LNCS 111–121
Krull, A, Brachmann, E, Nowozin, S, Michel, F, Shotton, J and Rother, C (2017).
PoseAgent: Budget-constrained 6D object pose estimation via reinforcement learning.
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017.
2017-Janua 2566–2574.
http://arxiv.org/abs/1612.03779 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
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
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 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
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)