@article {Ardizzone2020,
	title = {Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling},
	year = {2020},
	month = {jan},
	abstract = {Generative models are more informative about underlying phenomena than discriminative ones and offer superior uncertainty quantification and out-of-distribution robustness. However, these advantages often come at the expense of reduced classification accuracy. The Information Bottleneck objective (IB) formulates this trade-off in a clean information-theoretic way, but its practical application is hampered by a lack of accurate high-dimensional estimators of mutual information (MI), its main constituent. To overcome this limitation, we develop the theory and methodology of IB-INNs, which optimize the IB objective by means of Invertible Neural Networks (INNs), without the need for approximations of MI. Our experiments show that IB-INNs allow for a precise adjustment of the generative/discriminative trade-off: They learn accurate models of the class conditional likelihoods, generalize well to unseen data and reliably detect out-of-distribution examples, while at the same time exhibiting classification accuracy close to purely discriminative feed-forward networks.},
	url = {http://arxiv.org/abs/2001.06448},
	author = {Lynton Ardizzone and Mackowiak, Radek and Carsten Rother and Ullrich K{\"o}the}
}
@conference {Brachmann2019,
	title = {Expert sample consensus applied to camera re-localization},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2019-Octob},
	year = {2019},
	month = {aug},
	pages = {7524{\textendash}7533},
	abstract = {Fitting model parameters to a set of noisy data points is a common problem in computer vision. In this work, we fit the 6D camera pose to a set of noisy correspondences between the 2D input image and a known 3D environment. We estimate these correspondences from the image using a neural network. Since the correspondences often contain outliers, we utilize a robust estimator such as Random Sample Consensus (RANSAC) or Differentiable RANSAC (DSAC) to fit the pose parameters. When the problem domain, e.g. the space of all 2D-3D correspondences, is large or ambiguous, a single network does not cover the domain well. Mixture of Experts (MoE) is a popular strategy to divide a problem domain among an ensemble of specialized networks, so called experts, where a gating network decides which expert is responsible for a given input. In this work, we introduce Expert Sample Consensus (ESAC), which integrates DSAC in a MoE. Our main technical contribution is an efficient method to train ESAC jointly and end-to-end. We demonstrate experimentally that ESAC handles two real-world problems better than competing methods, i.e. scalability and ambiguity. We apply ESAC to fitting simple geometric models to synthetic images, and to camera re-localization for difficult, real datasets.},
	isbn = {9781728148038},
	issn = {15505499},
	doi = {10.1109/ICCV.2019.00762},
	url = {http://arxiv.org/abs/1908.02484},
	author = {Brachmann, Eric and Carsten Rother}
}
@conference {Bleyer2012,
	title = {Extracting 3D scene-consistent object proposals and depth from stereo images},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {7576 LNCS},
	number = {PART 5},
	year = {2012},
	pages = {467{\textendash}481},
	abstract = {This work combines two active areas of research in computer vision: unsupervised object extraction from a single image, and depth estimation from a stereo image pair. A recent, successful trend in unsupervised object extraction is to exploit so-called "3D scene-consistency", that is enforcing that objects obey underlying physical constraints of the 3D scene, such as occupancy of 3D space and gravity of objects. Our main contribution is to introduce the concept of 3D scene-consistency into stereo matching. We show that this concept is beneficial for both tasks, object extraction and depth estimation. In particular, we demonstrate that our approach is able to create a large set of 3D scene-consistent object proposals, by varying e.g. the prior on the number of objects. After automatically ranking the proposals we show experimentally that our results are considerably closer to ground truth than state-of-the-art techniques which either use stereo or monocular images. We envision that our method will build the front-end of a future object recognition system for stereo images. {\textcopyright} 2012 Springer-Verlag.},
	isbn = {9783642337147},
	issn = {03029743},
	doi = {10.1007/978-3-642-33715-4_34},
	url = {http://vision.middlebury.edu/stereo/},
	author = {Bleyer, Michael and Rhemann, Christoph and Carsten Rother}
}
@conference {Bleyer2012a,
	title = {Extracting 3D scene-consistent object proposals and depth from stereo images},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {7576 LNCS},
	number = {PART 5},
	year = {2012},
	pages = {467{\textendash}481},
	abstract = {This work combines two active areas of research in computer vision: unsupervised object extraction from a single image, and depth estimation from a stereo image pair. A recent, successful trend in unsupervised object extraction is to exploit so-called "3D scene-consistency", that is enforcing that objects obey underlying physical constraints of the 3D scene, such as occupancy of 3D space and gravity of objects. Our main contribution is to introduce the concept of 3D scene-consistency into stereo matching. We show that this concept is beneficial for both tasks, object extraction and depth estimation. In particular, we demonstrate that our approach is able to create a large set of 3D scene-consistent object proposals, by varying e.g. the prior on the number of objects. After automatically ranking the proposals we show experimentally that our results are considerably closer to ground truth than state-of-the-art techniques which either use stereo or monocular images. We envision that our method will build the front-end of a future object recognition system for stereo images. {\textcopyright} 2012 Springer-Verlag.},
	keywords = {Object Segmentation, Stereo Matching},
	isbn = {9783642337147},
	issn = {03029743},
	doi = {10.1007/978-3-642-33715-4_34},
	author = {Bleyer, Michael and Rhemann, Christoph and Carsten Rother}
}
@article {Criminisi2007,
	title = {Efficient dense stereo with occlusions for new view-synthesis by four-state dynamic programming},
	journal = {International Journal of Computer Vision},
	volume = {71},
	number = {1},
	year = {2007},
	pages = {89{\textendash}110},
	publisher = {Kluwer Academic Publishers},
	abstract = {A new algorithm is proposed for efficient stereo and novel view synthesis. Given the video streams acquired by two synchronized cameras the proposed algorithm synthesises images from a virtual camera in arbitrary position near the physical cameras. The new technique is based on an improved, dynamic-programming, stereo algorithm for efficient novel view generation. The two main contributions of this paper are: (i) a new four state matching graph for dense stereo dynamic programming, that supports accurate occlusion labelling; (ii) a compact geometric derivation for novel view synthesis by direct projection of the minimum cost surface. Furthermore, the paper presents an algorithm for the temporal maintenance of a background model to enhance the rendering of occlusions and reduce temporal artefacts (flicker); and a cost aggregation algorithm that acts directly in the three-dimensional matching cost space. The proposed algorithm has been designed to work with input images with large disparity range, a common practical situation. The enhanced occlusion handling capabilities of the new dynamic programming algorithm are evaluated against those of the most powerful state-of-the-art dynamic programming and graph-cut techniques. Four-state DP is also evaluated against the disparity-based Middlebury error metrics and its performance found to be amongst the best of the efficient algorithms. A number of examples demonstrate the robustness of four-state DP to artefacts in stereo video streams. This includes demonstrations of cyclopean view synthesis in extended conversational sequences, synthesis from a freely translating virtual camera and, finally, basic 3D scene editing. {\textcopyright} 2006 Springer Science + Business Media, LLC.},
	keywords = {Dense stereo, Gaze correction, Image-based rendering, Video-conferencing},
	issn = {09205691},
	doi = {10.1007/s11263-006-8525-1},
	author = {Criminisi, A and Blake, A and Carsten Rother and Shotton, J and Torr, P. H.S.}
}
