@proceedings {Haller2020,
	title = {A Primal-Dual Solver for Large-Scale Tracking-by-Assignment},
	year = {2020},
	abstract = {We propose a fast approximate solver for the combinatorial problem known as tracking-by-assignment, which we apply to cell tracking. The latter plays a key role in discovery in many life sciences, especially in cell and developmental biology. So far, in the most general setting this problem was addressed by off-the-shelf solvers like Gurobi, whose run time and memory requirements rapidly grow with the size of the input. In contrast, for our method this growth is nearly linear. Our contribution consists of a new (1) de-composable compact representation of the problem; (2) dual block-coordinate ascent method for optimizing the decomposition-based dual; and (3) primal heuristics that reconstructs a feasible integer solution based on the dual information. Compared to solving the problem with Gurobi, we observe an up to 60 times speed-up, while reducing the memory footprint significantly. We demonstrate the efficacy of our method on real-world tracking problems.},
	author = {Stefan Haller and Prakash, Mangal and Hutschenreiter, Lisa and Pietzsch, Tobias and Carsten Rother and Jug, Florian and Swoboda, Paul and Savchynskyy, Bogdan}
}
@conference {Krull2017,
	title = {PoseAgent: Budget-constrained 6D object pose estimation via reinforcement learning},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	month = {dec},
	pages = {2566{\textendash}2574},
	abstract = {State-of-the-art computer vision algorithms often achieve efficiency by making discrete choices about which hypotheses to explore next. This allows allocation of computational resources to promising candidates, however, such decisions are non-differentiable. As a result, these algorithms are hard to train in an end-to-end fashion. In this work we propose to learn an efficient algorithm for the task of 6D object pose estimation. Our system optimizes the parameters of an existing state-of-the art pose estimation system using reinforcement learning, where the pose estimation system now becomes the stochastic policy, parametrized by a CNN. Additionally, we present an efficient training algorithm that dramatically reduces computation time. We show empirically that our learned pose estimation procedure makes better use of limited resources and improves upon the state-of-the-art on a challenging dataset. Our approach enables differentiable end-to-end training of complex algorithmic pipelines and learns to make optimal use of a given computational budget.},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.275},
	url = {http://arxiv.org/abs/1612.03779},
	author = {Krull, Alexander and Brachmann, Eric and Nowozin, Sebastian and Michel, Frank and Shotton, Jamie and Carsten Rother}
}
@article {Mitra2015,
	title = {Pacific Graphics 2015 DenseCut: Densely Connected CRFs for Realtime GrabCut},
	volume = {34},
	number = {7},
	year = {2015},
	abstract = {Figure-ground segmentation from bounding box input, provided either automatically or manually, has been ex-tremely popular in the last decade and influenced various applications. A lot of research has focused on high-quality segmentation, using complex formulations which often lead to slow techniques, and often hamper practi-cal usage. In this paper we demonstrate a very fast segmentation technique which still achieves very high quality results. We propose to replace the time consuming iterative refinement of global colour models in traditional GrabCut formulation by a densely connected CRF. To motivate this decision, we show that a dense CRF implicitly models unnormalized global colour models for foreground and background. Such relationship provides insightful analysis to bridge between dense CRF and GrabCut functional. We extensively evaluate our algorithm using two famous benchmarks. Our experimental results demonstrated that the proposed algorithm achieves an order of magnitude (10{\texttimes}) speed-up with respect to the closest competitor, and at the same time achieves a considerably higher accuracy.},
	keywords = {I46 [IMAGE PROCESSING AND COMPUTER VISION], partitioning, Segmentation{\textemdash}Region growing},
	url = {http://mftp.mmcheng.net/Papers/DenseCut.pdf},
	author = {Mitra, N J and Stam, J and Xu, K and Cheng, Ming-Ming and Prisacariu, Victor Adrian and Zheng, Shuai and Torr, Philip H S and Carsten Rother}
}
@conference {Mund2015a,
	title = {Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons},
	booktitle = {Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems},
	year = {2015},
	pages = {85{\textendash}94},
	abstract = {Both the current system of airport ground control and the continuous implementation efforts of A-SMGCS and Remote Tower concepts require complete and independent surveillance coverage in real-time. We believe that 3D point clouds generated by an actively scanning LiDAR system available at TU Dresden may satisfy these high standards. Nonetheless, the utilization of LiDAR sensing for airport ground surveillance purposes is extremely challenging due to the unique requirement profile in this domain. This is also the reason why existing solutions in other domains such as autonomous driving and robotics are not directly applicable for airport ground surveillance. In a first step, we developed point cloud object detection and segmentation techniques to present that new data comprehensively to the airport apron controller. In this paper, we focused on the timely detection of dislocated objects (foreign object debris, forgotten equipment etc.) as a serious cause to hazardous situations on airport movement areas. The results are promising for various reference targets. However, the detection of very small objects (e.g. socket wrench) requires more elaborate algorithms to take full advantage of the current LiDAR technology. In the future we will assess the strength of LiDAR-based surveillance in terms of the number of hazardous situations that could be avoided or safely managed by the apron controller.},
	keywords = {Airport surveillance, apron control, Apron management service, FOD, Foreign object debris, laser scanning, LiDAR, Object detection, Point cloud},
	isbn = {9781450335621},
	doi = {10.1145/2899361.2899370},
	author = {Mund, Johannes and Zouhar, Alexander and Meyer, Lothar and Fricke, Hartmut and Carsten Rother}
}
@conference {Mund2015,
	title = {Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons},
	booktitle = {Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems},
	year = {2015},
	pages = {85{\textendash}94},
	abstract = {Both the current system of airport ground control and the continuous implementation efforts of A-SMGCS and Remote Tower concepts require complete and independent surveillance coverage in real-time. We believe that 3D point clouds generated by an actively scanning LiDAR system available at TU Dresden may satisfy these high standards. Nonetheless, the utilization of LiDAR sensing for airport ground surveillance purposes is extremely challenging due to the unique requirement profile in this domain. This is also the reason why existing solutions in other domains such as autonomous driving and robotics are not directly applicable for airport ground surveillance. In a first step, we developed point cloud object detection and segmentation techniques to present that new data comprehensively to the airport apron controller. In this paper, we focused on the timely detection of dislocated objects (foreign object debris, forgotten equipment etc.) as a serious cause to hazardous situations on airport movement areas. The results are promising for various reference targets. However, the detection of very small objects (e.g. socket wrench) requires more elaborate algorithms to take full advantage of the current LiDAR technology. In the future we will assess the strength of LiDAR-based surveillance in terms of the number of hazardous situations that could be avoided or safely managed by the apron controller.},
	keywords = {Airport surveillance, apron control, Apron management service, FOD, Foreign object debris, laser scanning, LiDAR, Object detection, Point cloud},
	isbn = {9781450335621},
	doi = {10.1145/2899361.2899370},
	author = {Mund, Johannes and Zouhar, Alexander and Meyer, Lothar and Fricke, Hartmut and Carsten Rother}
}
@conference {Michel2015,
	title = {Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression},
	year = {2015},
	pages = {181.1{\textendash}181.11},
	abstract = {In this paper, we address the problem of one shot pose estimation of articulated ob-jects from an RGB-D image. In particular, we consider object instances with the topol-ogy of a kinematic chain, i.e. assemblies of rigid parts connected by prismatic or revolute joints. This object type occurs often in daily live, for instance in the form of furniture or electronic devices. Instead of treating each object part separately we are using the rela-tionship between parts of the kinematic chain and propose a new minimal pose sampling approach. This enables us to create a pose hypothesis for a kinematic chain consist-ing of K parts by sampling K 3D-3D point correspondences. To asses the quality of our method, we gathered a large dataset containing four objects and 7000+ annotated RGB-D frames 1 . On this dataset we achieve considerably better results than a modified state-of-the-art pose estimation system for rigid objects.},
	doi = {10.5244/c.29.181},
	author = {Michel, Frank and Krull, Alexander and Brachmann, Eric and Yang, Michael Ying and Gumhold, Stefan and Carsten Rother}
}
@article {Besse2014b,
	title = {PMBP: PatchMatch Belief Propagation for correspondence field estimation},
	journal = {International Journal of Computer Vision},
	volume = {110},
	number = {1},
	year = {2014},
	pages = {2{\textendash}13},
	abstract = {PatchMatch (PM) is a simple, yet very powerful and successful method for optimizing continuous labelling problems. The algorithm has two main ingredients: the update of the solution space by sampling and the use of the spatial neighbourhood to propagate samples. We show how these ingredients are related to steps in a specific form of belief propagation (BP) in the continuous space, called max-product particle BP (MP-PBP). However, MP-PBP has thus far been too slow to allow complex state spaces. In the case where all nodes share a common state space and the smoothness prior favours equal values, we show that unifying the two approaches yields a new algorithm, PMBP, which is more accurate than PM and orders of magnitude faster than MP-PBP. To illustrate the benefits of our PMBP method we have built a new stereo matching algorithm with unary terms which are borrowed from the recent PM Stereo work and novel realistic pairwise terms that provide smoothness. We have experimentally verified that our method is an improvement over state-of-the-art techniques at sub-pixel accuracy level.},
	keywords = {Belief propagation, Correspondence fields, PatchMatch},
	issn = {15731405},
	doi = {10.1007/s11263-013-0653-9},
	author = {Besse, Frederic and Carsten Rother and Fitzgibbon, Andrew and Kautz, Jan}
}
@article {Besse2014,
	title = {PMBP: PatchMatch Belief Propagation for correspondence field estimation},
	journal = {International Journal of Computer Vision},
	volume = {110},
	number = {1},
	year = {2014},
	month = {oct},
	pages = {2{\textendash}13},
	publisher = {Kluwer Academic Publishers},
	abstract = {PatchMatch (PM) is a simple, yet very powerful and successful method for optimizing continuous labelling problems. The algorithm has two main ingredients: the update of the solution space by sampling and the use of the spatial neighbourhood to propagate samples. We show how these ingredients are related to steps in a specific form of belief propagation (BP) in the continuous space, called max-product particle BP (MP-PBP). However, MP-PBP has thus far been too slow to allow complex state spaces. In the case where all nodes share a common state space and the smoothness prior favours equal values, we show that unifying the two approaches yields a new algorithm, PMBP, which is more accurate than PM and orders of magnitude faster than MP-PBP. To illustrate the benefits of our PMBP method we have built a new stereo matching algorithm with unary terms which are borrowed from the recent PM Stereo work and novel realistic pairwise terms that provide smoothness. We have experimentally verified that our method is an improvement over state-of-the-art techniques at sub-pixel accuracy level.},
	keywords = {Belief propagation, Correspondence fields, PatchMatch},
	issn = {15731405},
	doi = {10.1007/s11263-013-0653-9},
	author = {Besse, Frederic and Carsten Rother and Fitzgibbon, Andrew and Kautz, Jan}
}
@article {Besse2014a,
	title = {PMBP: PatchMatch Belief Propagation for correspondence field estimation},
	journal = {International Journal of Computer Vision},
	volume = {110},
	number = {1},
	year = {2014},
	pages = {2{\textendash}13},
	abstract = {PatchMatch (PM) is a simple, yet very powerful and successful method for optimizing continuous labelling problems. The algorithm has two main ingredients: the update of the solution space by sampling and the use of the spatial neighbourhood to propagate samples. We show how these ingredients are related to steps in a specific form of belief propagation (BP) in the continuous space, called max-product particle BP (MP-PBP). However, MP-PBP has thus far been too slow to allow complex state spaces. In the case where all nodes share a common state space and the smoothness prior favours equal values, we show that unifying the two approaches yields a new algorithm, PMBP, which is more accurate than PM and orders of magnitude faster than MP-PBP. To illustrate the benefits of our PMBP method we have built a new stereo matching algorithm with unary terms which are borrowed from the recent PM Stereo work and novel realistic pairwise terms that provide smoothness. We have experimentally verified that our method is an improvement over state-of-the-art techniques at sub-pixel accuracy level.},
	keywords = {Belief propagation, Correspondence fields, PatchMatch},
	issn = {15731405},
	doi = {10.1007/s11263-013-0653-9},
	author = {Besse, Frederic and Carsten Rother and Fitzgibbon, Andrew and Kautz, Jan}
}
@conference {Bleyer2011a,
	title = {PatchMatch Stereo - Stereo Matching with Slanted Support Windows},
	year = {2011},
	pages = {14.1{\textendash}14.11},
	abstract = {Common local stereo methods match support windows at integer-valued disparities. The implicit assumption that pixels within the support region have constant disparity does not hold for slanted surfaces and leads to a bias towards reconstructing frontoparallel surfaces. This work overcomes this bias by estimating an individual 3D plane at each pixel onto which the support region is projected. The major challenge of this approach is to find a pixels optimal 3D plane among all possible planes whose number is infinite. We show that an ideal algorithm to solve this problem is PatchMatch 1 that we extend to find an approximate nearest neighbor according to a plane. In addition to Patch- Matchs spatial propagation scheme, we propose (1) view propagation where planes are propagated among left and right views of the stereo pair and (2) temporal propagation where planes are propagated from preceding and consecutive frames of a video when doing temporal stereo. Adaptive support weights are used in matching cost aggregation to improve results at disparity borders. We also show that our slanted support windows can be used to compute a cost volume for global stereo methods, which allows for explicit treatment of occlusions and can handle large untextured regions. In the results we demonstrate that our method reconstructs highly slanted surfaces and achieves impressive disparity details with sub-pixel precision. In the Middlebury table, our method is currently top-performer among local methods and takes rank 2 among approximately 110 competitors if sub-pixel precision is considered.},
	doi = {10.5244/c.25.14},
	author = {Bleyer, Michael and Rhemann, Christoph and Carsten Rother}
}
@conference {Pletscher2011,
	title = {Putting MAP back on the map},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6835 LNCS},
	year = {2011},
	pages = {111{\textendash}121},
	abstract = {Conditional Random Fields (CRFs) are popular models in computer vision for solving labeling problems such as image denoising. This paper tackles the rarely addressed but important problem of learning the full form of the potential functions of pairwise CRFs. We examine two popular learning techniques, maximum likelihood estimation and maximum margin training. The main focus of the paper is on models such as pairwise CRFs, that are simplistic (misspecified) and do not fit the data well. We empirically demonstrate that for misspecified models maximum-margin training with MAP prediction is superior to maximum likelihood estimation with any other prediction method. Additionally we examine the common belief that MLE is better at producing predictions matching image statistics. {\textcopyright} 2011 Springer-Verlag.},
	isbn = {9783642231223},
	issn = {03029743},
	doi = {10.1007/978-3-642-23123-0_12},
	author = {Pletscher, Patrick and Nowozin, Sebastian and Kohli, Pushmeet and Carsten Rother}
}
@conference {Pletscher2011a,
	title = {Putting MAP back on the map},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6835 LNCS},
	year = {2011},
	pages = {111{\textendash}121},
	abstract = {Conditional Random Fields (CRFs) are popular models in computer vision for solving labeling problems such as image denoising. This paper tackles the rarely addressed but important problem of learning the full form of the potential functions of pairwise CRFs. We examine two popular learning techniques, maximum likelihood estimation and maximum margin training. The main focus of the paper is on models such as pairwise CRFs, that are simplistic (misspecified) and do not fit the data well. We empirically demonstrate that for misspecified models maximum-margin training with MAP prediction is superior to maximum likelihood estimation with any other prediction method. Additionally we examine the common belief that MLE is better at producing predictions matching image statistics. {\textcopyright} 2011 Springer-Verlag.},
	isbn = {9783642231223},
	issn = {03029743},
	doi = {10.1007/978-3-642-23123-0_12},
	author = {Pletscher, Patrick and Nowozin, Sebastian and Kohli, Pushmeet and Carsten Rother}
}
@conference {Rhemann2009b,
	title = {A perceptually motivated online benchmark for image matting},
	booktitle = {2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009},
	volume = {2009 IEEE},
	year = {2009},
	pages = {1826{\textendash}1833},
	abstract = {The availability of quantitative online benchmarks for low-level vision tasks such as stereo and optical flow has led to significant progress in the respective fields. This paper introduces such a benchmark for image matting. There are three key factors for a successful benchmarking system: (a) a challenging, high-quality ground truth test set; (b) an online evaluation repository that is dynamically updated with new results; (c) perceptually motivated error functions. Our new benchmark strives to meet all three criteria. We evaluated several matting methods with our benchmark and show that their performance varies depending on the error function. Also, our challenging test set reveals problems of existing algorithms, not reflected in previously reported results. We hope that our effort will lead to considerable progress in the field of image matting, and welcome the reader to visit our benchmark at www.alphamatting.com. {\textcopyright} 2009 IEEE.},
	isbn = {9781424439935},
	doi = {10.1109/CVPRW.2009.5206503},
	author = {Rhemann, Christoph and Carsten Rother and Wang, Jue and Gelautz, Margrit and Kohli, Pushmeet and Rott, Pamela}
}
@conference {Rhemann2009a,
	title = {A perceptually motivated online benchmark for image matting},
	booktitle = {2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009},
	volume = {2009 IEEE},
	year = {2009},
	pages = {1826{\textendash}1833},
	abstract = {The availability of quantitative online benchmarks for low-level vision tasks such as stereo and optical flow has led to significant progress in the respective fields. This paper introduces such a benchmark for image matting. There are three key factors for a successful benchmarking system: (a) a challenging, high-quality ground truth test set; (b) an online evaluation repository that is dynamically updated with new results; (c) perceptually motivated error functions. Our new benchmark strives to meet all three criteria. We evaluated several matting methods with our benchmark and show that their performance varies depending on the error function. Also, our challenging test set reveals problems of existing algorithms, not reflected in previously reported results. We hope that our effort will lead to considerable progress in the field of image matting, and welcome the reader to visit our benchmark at www.alphamatting.com. {\textcopyright} 2009 IEEE.},
	keywords = {Benchmark, Evaluation, Matting},
	isbn = {9781424439935},
	doi = {10.1109/CVPRW.2009.5206503},
	url = {www.alphamatting.com.},
	author = {Rhemann, Christoph and Carsten Rother and Wang, Jue and Gelautz, Margrit and Kohli, Pushmeet and Rott, Pamela}
}
@conference {Rhemann2009,
	title = {A perceptually motivated online benchmark for image matting},
	booktitle = {2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009},
	volume = {2009 IEEE},
	year = {2009},
	pages = {1826{\textendash}1833},
	abstract = {The availability of quantitative online benchmarks for low-level vision tasks such as stereo and optical flow has led to significant progress in the respective fields. This paper introduces such a benchmark for image matting. There are three key factors for a successful benchmarking system: (a) a challenging, high-quality ground truth test set; (b) an online evaluation repository that is dynamically updated with new results; (c) perceptually motivated error functions. Our new benchmark strives to meet all three criteria. We evaluated several matting methods with our benchmark and show that their performance varies depending on the error function. Also, our challenging test set reveals problems of existing algorithms, not reflected in previously reported results. We hope that our effort will lead to considerable progress in the field of image matting, and welcome the reader to visit our benchmark at www.alphamatting.com. {\textcopyright} 2009 IEEE.},
	isbn = {9781424439935},
	doi = {10.1109/CVPRW.2009.5206503},
	url = {www.alphamatting.com.},
	author = {Rhemann, Christoph and Carsten Rother and Wang, Jue and Gelautz, Margrit and Kohli, Pushmeet and Rott, Pamela}
}
@conference {Kohli2008,
	title = {On partial optimality in multi-label MRFs},
	booktitle = {Proceedings of the 25th International Conference on Machine Learning},
	year = {2008},
	pages = {480{\textendash}487},
	abstract = {We consider the problem of optimizing multilabel MRFs, which is in general NP-hard and ubiquitous in low-level computer vision. One approach for its solution is to formulate it as an integer linear programming and relax the integrality constraints. The approach we consider in this paper is to first convert the multi-label MRF into an equivalent binary-label MRF and then to relax it. The resulting relaxation can be efficiently solved using a maximum flow algorithm. Its solution provides us with a partially optimal labelling of the binary variables. This partial labelling is then easily transferred to the multi-label problem. We study the theoretical properties of the new relaxation and compare it with the standard one. Specifically, we compare tightness, and characterize a subclass of problems where the two relaxations coincide. We propose several combined algorithms based on the technique and demonstrate their performance on challenging computer vision problems. Copyright 2008 by the author(s)/owner(s).},
	isbn = {9781605582054},
	doi = {10.1145/1390156.1390217},
	author = {Kohli, Pushmeet and Shekhovtsov, Alexander and Carsten Rother and Kolmogorov, Vladimir and Torr, Philip}
}
@conference {Lalonde2007,
	title = {Photo clip art},
	booktitle = {Proceedings of the ACM SIGGRAPH Conference on Computer Graphics},
	year = {2007},
	abstract = {We present a system for inserting new objects into existing photographs by querying a vast image-based object library, pre-computed using a publicly available Internet object database. The central goal is to shield the user from all of the arduous tasks typically involved in image compositing. The user is only asked to do two simple things: 1) pick a 3D location in the scene to place a new object; 2) select an object to insert using a hierarchical menu. We pose the problem of object insertion as a data-driven, 3D-based, context-sensitive object retrieval task. Instead of trying to manipulate the object to change its orientation, color distribution, etc. to fit the new image, we simply retrieve an object of a specified class that has all the required properties (camera pose, lighting, resolution, etc) from our large object library. We present new automatic algorithms for improving object segmentation and blending, estimating true 3D object size and orientation, and estimating scene lighting conditions. We also present an intuitive user interface that makes object insertion fast and simple even for the artistically challenged. {\textcopyright} 2007 ACM.},
	keywords = {3D scene reasoning, Blending and compositing, Computational photography, Image databases, Object insertion},
	doi = {10.1145/1275808.1276381},
	url = {http://graphics.cs.cmu.edu/projects/photoclipart/},
	author = {Lalonde, Jean Fran{\c c}ois and Hoiem, Derek and Efros, Alexei A and Carsten Rother and Winn, John and Criminisi, Antonio}
}
@article {Kolmogorov2006,
	title = {Probabilistic fusion of stereo with color and contrast for bilayer segmentation},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {28},
	number = {9},
	year = {2006},
	pages = {1480{\textendash}1492},
	abstract = {This paper describes models and algorithms for the real-time segmentation of foreground from background layers in stereo video sequences. Automatic separation of layers from color/contrast or from stereo alone is known to be error-prone. Here, color, 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 six-state space that represents both foreground/background layers and occluded regions. The stereo-match likelihood is then fused with a contrast-sensitive color 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 marginalized over disparities to evaluate foreground and background hypotheses and then fused with a contrast-sensitive color 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 performance, substantially better than either stereo or color/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. {\textcopyright} 2006 IEEE.},
	keywords = {3D/stereo scene analysis, Computer vision, Dynamic programming, Image processing and computer vision, Parameter learning},
	issn = {01628828},
	doi = {10.1109/TPAMI.2006.193},
	url = {http://research.microsoft.com/vision/cambridge},
	author = {Kolmogorov, Vladimir and Criminisi, Antonio and Blake, Andrew and Cross, Geoffrey and Carsten Rother}
}
@conference {Rother2002c,
	title = {Projective factorization of planes and cameras in multiple views},
	booktitle = {Proceedings - International Conference on Pattern Recognition},
	volume = {16},
	number = {2},
	year = {2002},
	pages = {737{\textendash}740},
	abstract = {This paper proposes a novel method for the projective reconstruction of planes and cameras from multiple images by factorizing a matrix containing all planar homographies between a reference view and all other views. If some planes are not visible in all views an alternative method is presented which solves the problem in two steps: a) all camera centers are recovered simultaneously b) all planes are reconstructed. The key idea of both methods is to specify one of the planes, which is visible in all views, as the plane at infinity. The methods were applied to synthetic and real data, where VRML models can be created with a small amount of user interaction.},
	issn = {10514651},
	doi = {10.1109/icpr.2002.1048408},
	author = {Carsten Rother and Carlsson, Stefan and Tell, Dennis}
}
