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2017
Hosseini Jafari, O, Groth, O, Kirillov, A, Yang, M Ying and Rother, C (2017). Analyzing modular CNN architectures for joint depth prediction and semantic segmentation. Proceedings - IEEE International Conference on Robotics and Automation. 4620–4627. http://arxiv.org/abs/1702.08009 http://dx.doi.org/10.1109/ICRA.2017.7989537
Abu Alhaija, H, Mustikovela, S Karthik, Mescheder, L, Geiger, A and Rother, C (2017). Augmented reality meets deep learning for car instance segmentation in urban scenes. British Machine Vision Conference 2017, BMVC 2017
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
Schlesinger, D, Jug, F, Myers, G, Rother, C and Kainmueller, D (2017). Crowd sourcing image segmentation with iaSTAPLE. Proceedings - International Symposium on Biomedical Imaging. 401–405
Ramos, S, Gehrig, S, Pinggera, P, Franke, U and Rother, C (2017). Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling. IEEE Intelligent Vehicles Symposium, Proceedings. 1025–1032. http://arxiv.org/abs/1612.06573
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
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
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
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
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
Kruse, J, Rother, C, Schmidt, U and Dresden, T U (2017). Learning To Push The Limits Of Efficient Fft-Based Image Deconvolution - Supplemental Material
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
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
Massiceti, D, Krull, A, Brachmann, E, Rother, C and Torr, P H S (2017). Random Forests versus Neural Networks − What's best for camera location
Hullin, M, Klein, R, Schultz, T, Yao, A, Li, W, Hosseini Jafari, O and Rother, C (2017). Semantic-Aware Image Smoothing. Vision, Modeling, and Visualization. https://hci.iwr.uni-heidelberg.de/vislearn/wp-content/uploads/2014/08/paper1024_CRC.pdf
2018
Abu Alhaija, H, Mustikovela, S Karthik, Mescheder, L, Geiger, A and Rother, C (2018). Augmented Reality Meets Computer Vision. International Journal of Computer Vision. In press 1–13
Abu Alhaija, H, Mustikovela, S K, Mescheder, A, Geiger, C and Rother, C (2018). Augmented Reality Meets Computer Vision Efficient Data Generation for Urban Driving Scenes. IJCV. 1-12PDF icon Technical Report (3.83 MB)
Abu Alhaija, H, Mustikovela, S Karthik, Mescheder, L, Geiger, A and Rother, C (2018). Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes. International Journal of Computer Vision. 126 961–972. http://arxiv.org/abs/1708.01566
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
Arnab, A, Zheng, S, Jayasumana, S, Romera-paredes, B, Kirillov, A, Savchynskyy, B, Rother, C, Kahl, F and Torr, P (2018). Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation. Cvpr. XX 1–15. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.8889&rep=rep1&type=pdf%0Ahttp://dx.doi.org/10.1109/CVPR.2012.6248050
Abu Alhaija, H, Mustikovela, S K, Geiger, A and Rother, C (2018). Geometric Image Synthesis. ACCV. Proceedings, in pressPDF icon Technical Report (1.83 MB)
Hosseini Jafari, O, Mustikovela, S K, Pertsch, K, Brachmann, E and Rother, C (2018). iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects. ACCV. Proceedings, in pressPDF icon Technical Report (3.28 MB)
Brachmann, E and Rother, C (2018). Learning Less is More - 6D Camera Localization via 3D Surface Regression. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 4654–4662. http://arxiv.org/abs/1711.10228
Tourani, S, Shekhovtsov, A, Rother, C and Savchynskyy, B (2018). MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11208 LNCS 264–281
Schilling, H, Diebold, M, Rother, C and Jähne, B (2018). Trust your Model: Light Field Depth Estimation with inline Occlusion Handling. CVPR. ProceedingsPDF icon Technical Report (5.46 MB)
Schilling, H, Diebold, M, Rother, C and Jähne, B (2018). Trust your Model: Light Field Depth Estimation with Inline Occlusion Handling. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 4530–4538
2019
Kruse, J, Ardizzone, L, Rother, C and Köthe, U (2019). Benchmarking Invertible Architectures On Inverse Problems
Kamann, C and Rother, C (2019). Benchmarking the Robustness of Semantic Segmentation Models. http://arxiv.org/abs/1908.05005
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
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
Brachmann, E and Rother, C (2019). Expert sample consensus applied to camera re-localization. Proceedings of the IEEE International Conference on Computer Vision. 2019-Octob 7524–7533. http://arxiv.org/abs/1908.02484
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
Ardizzone, L, Lüth, C, Kruse, J, Rother, C and Köthe, U (2019). Guided Image Generation with Conditional Invertible Neural Networks. http://arxiv.org/abs/1907.02392
Ardizzone, L, Lüth, C, Kruse, J, Rother, C and Köthe, U (2019). Guided Image Generation with Conditional Invertible Neural Networks. http://arxiv.org/abs/1907.02392

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