@techreport {Kruse2019,
	title = {Benchmarking Invertible Architectures on Inverse Problems},
	number = {i},
	year = {2019},
	abstract = {Recent work demonstrated that flow-based invert-ible neural networks are promising tools for solving ambiguous inverse problems. Following up on this, we investigate how ten invertible archi-tectures and related models fare on two intuitive, low-dimensional benchmark problems, obtaining the best results with coupling layers and simple autoencoders. We hope that our initial efforts inspire other researchers to evaluate their invertible architectures in the same setting and put forth additional benchmarks, so our evaluation may eventually grow into an official community challenge.},
	author = {Kruse, Jakob and Lynton Ardizzone and Carsten Rother and Ullrich K{\"o}the}
}
@article {Kamann2019,
	title = {Benchmarking the Robustness of Semantic Segmentation Models},
	year = {2019},
	abstract = {When designing a semantic segmentation module for a practical application, such as autonomous driving, it is crucial to understand the robustness of the module with respect to a wide range of image corruptions. While there are recent robustness studies for full-image classification, we are the first to present an exhaustive study for semantic segmentation, based on the state-of-the-art model DeepLabv3\$+\$. To increase the realism of our study, we utilize almost 200,000 images generated from Cityscapes and PASCAL VOC 2012, and we furthermore present a realistic noise model, imitating HDR camera noise. Based on the benchmark study we gain several new insights. Firstly, model robustness increases with model performance, in most cases. Secondly, some architecture properties affect robustness significantly, such as a Dense Prediction Cell which was designed to maximize performance on clean data only. Thirdly, to achieve good generalization with respect to various types of image noise, it is recommended to train DeepLabv3+ with our realistic noise model.},
	url = {http://arxiv.org/abs/1908.05005},
	author = {Kamann, Christoph and Carsten Rother}
}
@conference {Hodan2018,
	title = {BOP: Benchmark for 6D object pose estimation},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {11214 LNCS},
	year = {2018},
	month = {aug},
	pages = {19{\textendash}35},
	abstract = {We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: (i) eight datasets in a unified format that cover different practical scenarios, including two new datasets focusing on varying lighting conditions, (ii) an evaluation methodology with a pose-error function that deals with pose ambiguities, (iii) a comprehensive evaluation of 15 diverse recent methods that captures the status quo of the field, and (iv) an online evaluation system that is open for continuous submission of new results. The evaluation shows that methods based on point-pair features currently perform best, outperforming template matching methods, learning-based methods and methods based on 3D local features. The project website is available at bop.felk.cvut.cz.},
	isbn = {9783030012489},
	issn = {16113349},
	doi = {10.1007/978-3-030-01249-6_2},
	url = {http://arxiv.org/abs/1808.08319},
	author = {Hoda{\v n}, Tom{\'a}{\v s} and Michel, Frank and Brachmann, Eric and Kehl, Wadim and Buch, Anders Glent and Kraft, Dirk and Drost, Bertram and Vidal, Joel and Ihrke, Stephan and Zabulis, Xenophon and Sahin, Caner and Manhardt, Fabian and Tombari, Federico and Kim, Tae Kyun and Matas, Ji{\v r}{\'\i} and Carsten Rother}
}
@conference {Behl2017,
	title = {Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2017-Octob},
	year = {2017},
	pages = {2593{\textendash}2602},
	abstract = {Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e.g., at texture-less or reflective surfaces. However, these challenges are omnipresent in dynamic road scenes, which is the focus of this work. Our main contribution is to overcome these 3D motion estimation problems by exploiting recognition. In particular, we investigate the importance of recognition granularity, from coarse 2D bounding box estimates over 2D instance segmentations to fine-grained 3D object part predictions. We compute these cues using CNNs trained on a newly annotated dataset of stereo images and integrate them into a CRF-based model for robust 3D scene flow estimation - an approach we term Instance Scene Flow. We analyze the importance of each recognition cue in an ablation study and observe that the instance segmentation cue is by far strongest, in our setting. We demonstrate the effectiveness of our method on the challenging KITTI 2015 scene flow benchmark where we achieve state-of-the-art performance at the time of submission.},
	isbn = {9781538610329},
	issn = {15505499},
	doi = {10.1109/ICCV.2017.281},
	author = {Behl, Aseem and Omid Hosseini Jafari and Mustikovela, Siva Karthik and Hassan Abu Alhaija and Carsten Rother and Geiger, Andreas}
}
@conference {Behl2017a,
	title = {Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2017-Octob},
	year = {2017},
	pages = {2593{\textendash}2602},
	abstract = {Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e.g., at texture-less or reflective surfaces. However, these challenges are omnipresent in dynamic road scenes, which is the focus of this work. Our main contribution is to overcome these 3D motion estimation problems by exploiting recognition. In particular, we investigate the importance of recognition granularity, from coarse 2D bounding box estimates over 2D instance segmentations to fine-grained 3D object part predictions. We compute these cues using CNNs trained on a newly annotated dataset of stereo images and integrate them into a CRF-based model for robust 3D scene flow estimation - an approach we term Instance Scene Flow. We analyze the importance of each recognition cue in an ablation study and observe that the instance segmentation cue is by far strongest, in our setting. We demonstrate the effectiveness of our method on the challenging KITTI 2015 scene flow benchmark where we achieve state-of-the-art performance at the time of submission.},
	isbn = {9781538610329},
	issn = {15505499},
	doi = {10.1109/ICCV.2017.281},
	author = {Behl, Aseem and Omid Hosseini Jafari and Mustikovela, Siva Karthik and Hassan Abu Alhaija and Carsten Rother and Geiger, Andreas}
}
@article {Lempitsky2012,
	title = {Branch-and-mincut: Global optimization for image segmentation with high-level priors},
	journal = {Journal of Mathematical Imaging and Vision},
	volume = {44},
	number = {3},
	year = {2012},
	pages = {315{\textendash}329},
	abstract = {Efficient global optimization techniques such as graph cut exist for energies corresponding to binary image segmentation from low-level cues. However, introducing a high-level prior such as a shape prior or a color-distribution prior into the segmentation process typically results in an energy that is much harder to optimize. The main contribution of the paper is a new global optimization framework for a wide class of such energies. The framework is built upon two powerful techniques: graph cut and branch-and-bound. These techniques are unified through the derivation of lower bounds on the energies. Being computable via graph cut, these bounds are used to prune branches within a branchand-bound search. We demonstrate that the new framework can compute globally optimal segmentations for a variety of segmentation scenarios in a reasonable time on a modern CPU. These scenarios include unsupervised segmentation of an object undergoing 3D pose change, category-specific shape segmentation, and the segmentation under intensity/color priors defined by Chan-Vese and GrabCut functionals. {\textcopyright} Springer Science+Business Media, LLC 2011.},
	keywords = {Branch-and-bound, Global optimization, Graph Cuts, Image segmentation, Shape priors},
	issn = {09249907},
	doi = {10.1007/s10851-012-0328-0},
	author = {Lempitsky, Victor and Blake, Andrew and Carsten Rother}
}
@conference {Gehler2008,
	title = {Bayesian color constancy revisited},
	booktitle = {26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR},
	year = {2008},
	abstract = {Computational color constancy is the task of estimating the true reflectances of visible surfaces in an image. In this paper we follow a line of research that assumes uniform illumination of a scene, and that the principal step in estimating reflectances is the estimation of the scene illuminant. We review recent approaches to illuminant estimation, firstly those based on formulae for normalisation of the reflectance distribution in an image - so-called grey-world algorithms, and those based on a Bayesian formulation of image formation. In evaluating these previous approaches we introduce a new tool in the form of a database of 568 high-quality, indoor and outdoor images, accurately labelled with illuminant, and preserved in their raw form, free of correction or normalisation. This has enabled us to establish several properties experimentally. Firstly automatic selection of grey-world algorithms according to image properties is not nearly so effective as has been thought. Secondly, it is shown that Bayesian illuminant estimation is significantly improved by the improved accuracy of priors for illuminant and reflectance that are obtained from the new dataset. {\textcopyright}2008 IEEE.},
	isbn = {9781424422432},
	doi = {10.1109/CVPR.2008.4587765},
	author = {Gehler, Peter Vincent and Carsten Rother and Blake, Andrew and Minka, Tom and Sharp, Toby}
}
@conference {Kolmogorov2005,
	title = {Bi-layer segmentation of binocular stereo video},
	booktitle = {Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005},
	volume = {II},
	year = {2005},
	pages = {407{\textendash}414},
	abstract = {This paper describes two algorithms capable of real-time segmentation of foreground from background layers in stereo video sequences. Automatic separation of layers from colour/contrast or from stereo alone is known to be error-prone. Here, colour, contrast and stereo matching information are fused to infer layers accurately and efficiently. The first algorithm, Layered Dynamic Programming (LDP), solves stereo in an extended 6-state space that represents both foreground/background layers and occluded regions. The stereo-match likelihood is then fused with a contrast-sensitive colour model that is learned on the fly, and stereo disparities are obtained by dynamic programming. The second algorithm, Layered Graph Cut (LGC), does not directly solve stereo. Instead the stereo match likelihood is marginalised over foreground and background hypotheses, and fused with a contrast-sensitive colour model like the one used in LDP. Segmentation is solved efficiently by ternary graph cut. Both algorithms are evaluated with respect to ground truth data and found to have similar perfomance, substantially better than stereo or colour/contrast alone. However, their characteristics with respect to computational efficiency are rather different. The algorithms are demonstrated in the application of background substitution and shown to give good quality composite video output.},
	isbn = {0769523722},
	doi = {10.1109/CVPR.2005.91},
	url = {http://research.microsoft.com/vision/cambridge},
	author = {Kolmogorov, V and Criminisi, A and Blake, A and Cross, G and Carsten Rother}
}
