@conference {Leistner2019,
	title = {Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift},
	booktitle = {Proceedings - 2019 International Conference on 3D Vision, 3DV 2019},
	year = {2019},
	month = {sep},
	pages = {249{\textendash}257},
	abstract = {We propose a method for depth estimation from light field data, based on a fully convolutional neural network architecture. Our goal is to design a pipeline which achieves highly accurate results for small-and wide-baseline light fields. Since light field training data is scarce, all learning-based approaches use a small receptive field and operate on small disparity ranges. In order to work with wide-baseline light fields, we introduce the idea of EPI-Shift: To virtually shift the light field stack which enables to retain a small receptive field, independent of the disparity range. In this way, our approach {\textquoteright}learns to think outside the box of the receptive field". Our network performs joint classification of integer disparities and regression of disparity-offsets. A U-Net component provides excellent long-range smoothing. EPI-Shift considerably outperforms the state-of-the-art learning-based approaches and is on par with hand-crafted methods. We demonstrate this on a publicly available, synthetic, small-baseline benchmark and on large-baseline real-world recordings.},
	keywords = {Computer vision, deep learning, depth estimation, light fields, Stereo},
	isbn = {9781728131313},
	doi = {10.1109/3DV.2019.00036},
	url = {http://arxiv.org/abs/1909.09059 http://dx.doi.org/10.1109/3DV.2019.00036},
	author = {Titus Leistner and Schilling, Hendrik and Mackowiak, Radek and Gumhold, Stefan and Carsten Rother}
}
@techreport {Li,
	title = {Localizing Common Objects Using Common Component Activation Map},
	year = {2019},
	abstract = {In this work, we propose an approach to localize common objects from novel object categories in a set of images. We solve this problem using a new common component activation map (CCAM) in which we treat the class-specific activation maps (CAM) as components to discover the common components in the image set. We show that our approach can generalize on novel object categories in our experiments .},
	author = {Weihao Li and Omid Hosseini Jafari and Carsten Rother}
}
@conference {Brachmann2018,
	title = {Learning Less is More - 6D Camera Localization via 3D Surface Regression},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2018},
	month = {nov},
	pages = {4654{\textendash}4662},
	abstract = {Popular research areas like autonomous driving and augmented reality have renewed the interest in image-based camera localization. In this work, we address the task of predicting the 6D camera pose from a single RGB image in a given 3D environment. With the advent of neural networks, previous works have either learned the entire camera localization process, or multiple components of a camera localization pipeline. Our key contribution is to demonstrate and explain that learning a single component of this pipeline is sufficient. This component is a fully convolutional neural network for densely regressing so-called scene coordinates, defining the correspondence between the input image and the 3D scene space. The neural network is prepended to a new end-to-end trainable pipeline. Our system is efficient, highly accurate, robust in training, and exhibits outstanding generalization capabilities. It exceeds state-of-the-art consistently on indoor and outdoor datasets. Interestingly, our approach surpasses existing techniques even without utilizing a 3D model of the scene during training, since the network is able to discover 3D scene geometry automatically, solely from single-view constraints.},
	isbn = {9781538664209},
	issn = {10636919},
	doi = {10.1109/CVPR.2018.00489},
	url = {http://arxiv.org/abs/1711.10228},
	author = {Brachmann, Eric and Carsten Rother}
}
@techreport {Kruse,
	title = {Learning to Push the Limits of Efficient FFT-based Image Deconvolution - Supplemental Material},
	year = {2017},
	abstract = {1. Details about boundary adjustment comparison Section 4.3 of the main paper compares our proposed boundary adjustment (BA) strategy (Our BA, cf. Eq. 17 and Fig. 2 of the main paper) to the traditional edgetapering method (ET once, cf. Eq. 11 of the main paper) and the BA approach (ET each) of CSF [3]; these BA strategies are compared within our FDN model, the CSF model, and a standard Wiener filter [5]. Specifically, we use the publicly available code to train different variants of the CSF model on a dataset of the same size as ours, and only adjust the BA strategy. Furthermore, we apply the Wiener filter as defined in Eq. 2 of the main paper, which we can use iteratively with our BA approach by replacing y with ϕ t (y, k, x t); we estimate the expected image spectrum n from 3000 clean image patches. While our BA comparison is depicted visually in Fig. 5 of the main paper, Table 1 also provides the numeric results and additionally includes stages 6-10 of our FDN model. As compared to the BA approach of CSF (ET each), the results suggest that CSF would also benefit from further stages if used with our BA strategy (cf. 6 th column). Remarkably, the performance of the Wiener filter is not even fully saturated after 50 iterations (Wiener 50) when applied with our BA approach (cf. 3 rd column, only every 5 th step shown after iteration 10). Fig. 1 shows an example where our proposed BA strategy yields a substantial improvement in image quality compared to standard edgetapering (ET once).},
	author = {Kruse, Jakob and Carsten Rother and Schmidt, Uwe and Dresden, T U}
}
@conference {Kruse2017,
	title = {Learning to Push the Limits of Efficient FFT-Based Image Deconvolution},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2017-Octob},
	year = {2017},
	pages = {4596{\textendash}4604},
	abstract = {This work addresses the task of non-blind image deconvolution. Motivated to keep up with the constant increase in image size, with megapixel images becoming the norm, we aim at pushing the limits of efficient FFT-based techniques. Based on an analysis of traditional and more recent learning-based methods, we generalize existing discriminative approaches by using more powerful regularization, based on convolutional neural networks. Additionally, we propose a simple, yet effective, boundary adjustment method that alleviates the problematic circular convolution assumption, which is necessary for FFT-based deconvolution. We evaluate our approach on two common non-blind deconvolution benchmarks and achieve state-of-the-art results even when including methods which are computationally considerably more expensive.},
	isbn = {9781538610329},
	issn = {15505499},
	doi = {10.1109/ICCV.2017.491},
	author = {Kruse, Jakob and Carsten Rother and Schmidt, Uwe}
}
@conference {Pinggera2016,
	title = {Lost and found: Detecting small road hazards for self-driving vehicles},
	booktitle = {IEEE International Conference on Intelligent Robots and Systems},
	volume = {2016-Novem},
	year = {2016},
	pages = {1099{\textendash}1106},
	abstract = {Detecting small obstacles on the road ahead is a critical part of the driving task which has to be mastered by fully autonomous cars. In this paper, we present a method based on stereo vision to reliably detect such obstacles from a moving vehicle. The proposed algorithm performs statistical hypothesis tests in disparity space directly on stereo image data, assessing freespace and obstacle hypotheses on independent local patches. This detection approach does not depend on a global road model and handles both static and moving obstacles. For evaluation, we employ a novel lost-cargo image sequence dataset comprising more than two thousand frames with pixelwise annotations of obstacle and free-space and provide a thorough comparison to several stereo-based baseline methods. The dataset will be made available to the community to foster further research on this important topic4. The proposed approach outperforms all considered baselines in our evaluations on both pixel and object level and runs at frame rates of up to 20 Hz on 2 mega-pixel stereo imagery. Small obstacles down to the height of 5 cm can successfully be detected at 20 m distance at low false positive rates.},
	isbn = {9781509037629},
	issn = {21530866},
	doi = {10.1109/IROS.2016.7759186},
	url = {http://www.6d-vision.com/lostandfounddataset},
	author = {Pinggera, Peter and Ramos, Sebastian and Gehrig, Stefan and Franke, Uwe and Carsten Rother and Mester, Rudolf}
}
@conference {Krull2015,
	title = {Learning analysis-by-synthesis for 6d pose estimation in RGB-D images},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2015 Inter},
	year = {2015},
	pages = {954{\textendash}962},
	abstract = {Analysis-by-synthesis has been a successful approach for many tasks in computer vision, such as 6D pose estimation of an object in an RGB-D image which is the topic of this work. The idea is to compare the observation with the output of a forward process, such as a rendered image of the object of interest in a particular pose. Due to occlusion or complicated sensor noise, it can be difficult to perform this comparison in a meaningful way. We propose an approach that "learns to compare", while taking these difficulties into account. This is done by describing the posterior density of a particular object pose with a convolutional neural network (CNN) that compares observed and rendered images. The network is trained with the maximum likelihood paradigm. We observe empirically that the CNN does not specialize to the geometry or appearance of specific objects. It can be used with objects of vastly different shapes and appearances, and in different backgrounds. Compared to state-of-the-art, we demonstrate a significant improvement on two different datasets which include a total of eleven objects, cluttered background, and heavy occlusion.},
	isbn = {9781467383912},
	issn = {15505499},
	doi = {10.1109/ICCV.2015.115},
	author = {Krull, Alexander and Brachmann, Eric and Michel, Frank and Yang, Michael Ying and Gumhold, Stefan and Carsten Rother}
}
@conference {Hoai2014,
	title = {Learning discriminative localization from weakly labeled data},
	booktitle = {Pattern Recognition},
	volume = {47},
	number = {3},
	year = {2014},
	month = {mar},
	pages = {1523{\textendash}1534},
	abstract = {Visual categorization problems, such as object classification or action recognition, are increasingly often approached using a detection strategy: a classifier function is first applied to candidate subwindows of the image or the video, and then the maximum classifier score is used for class decision. Traditionally, the subwindow classifiers are trained on a large collection of examples manually annotated with masks or bounding boxes. The reliance on time-consuming human labeling effectively limits the application of these methods to problems involving very few categories. Furthermore, the human selection of the masks introduces arbitrary biases (e.g., in terms of window size and location) which may be suboptimal for classification. We propose a novel method for learning a discriminative subwindow classifier from examples annotated with binary labels indicating the presence of an object or action of interest, but not its location. During training, our approach simultaneously localizes the instances of the positive class and learns a subwindow SVM to recognize them. We extend our method to classification of time series by presenting an algorithm that localizes the most discriminative set of temporal segments in the signal. We evaluate our approach on several datasets for object and action recognition and show that it achieves results similar and in many cases superior to those obtained with full supervision. {\textcopyright} 2013 Elsevier Ltd. All rights reserved.},
	keywords = {Discriminative discovery, Event detection, Image classification, Object detection, Time series classification, Weakly supervised learning},
	issn = {00313203},
	doi = {10.1016/j.patcog.2013.09.028},
	author = {Hoai, Minh and Torresani, Lorenzo and De La Torre, Fernando and Carsten Rother}
}
@conference {Jancsary2013,
	title = {Learning convex QP relaxations for structured prediction},
	booktitle = {30th International Conference on Machine Learning, ICML 2013},
	number = {PART 3},
	year = {2013},
	pages = {1952{\textendash}1960},
	abstract = {We introduce a new large margin approach to discriminative training of intractable discrete graphical models. Our approach builds on a convex quadratic programming relaxation of the MAP inference problem. The model parameters are trained directly within this restricted class of energy functions so as to optimize the predictions on the training data. We address the issue of how to parameterize the resulting model and point out its relation to existing approaches. The primary motivation behind our use of the QP relaxation is its computational efficiency; yet, empirically, its predictive accuracy compares favorably to more expensive approaches. This makes it an appealing choice for many practical tasks. Copyright 2013 by the author(s).},
	author = {Jancsary, Jeremy and Nowozin, Sebastian and Carsten Rother}
}
@conference {Jancsary2012,
	title = {Loss-specific training of non-parametric image restoration models: A new state of the art},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {7578 LNCS},
	number = {PART 7},
	year = {2012},
	pages = {112{\textendash}125},
	abstract = {After a decade of rapid progress in image denoising, recent methods seem to have reached a performance limit. Nonetheless, we find that state-of-the-art denoising methods are visually clearly distinguishable and possess complementary strengths and failure modes. Motivated by this observation, we introduce a powerful non-parametric image restoration framework based on Regression Tree Fields (RTF). Our restoration model is a densely-connected tractable conditional random field that leverages existing methods to produce an image-dependent, globally consistent prediction. We estimate the conditional structure and parameters of our model from training data so as to directly optimize for popular performance measures. In terms of peak signal-to-noise-ratio (PSNR), our model improves on the best published denoising method by at least 0.26dB across a range of noise levels. Our most practical variant still yields statistically significant improvements, yet is over 20x faster than the strongest competitor. Our approach is well-suited for many more image restoration and low-level vision problems, as evidenced by substantial gains in tasks such as removal of JPEG blocking artefacts. {\textcopyright} 2012 Springer-Verlag.},
	isbn = {9783642337857},
	issn = {03029743},
	doi = {10.1007/978-3-642-33786-4_9},
	author = {Jancsary, Jeremy and Nowozin, Sebastian and Carsten Rother}
}
@conference {Jancsary2012a,
	title = {Loss-specific training of non-parametric image restoration models: A new state of the art},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {7578 LNCS},
	number = {PART 7},
	year = {2012},
	pages = {112{\textendash}125},
	abstract = {After a decade of rapid progress in image denoising, recent methods seem to have reached a performance limit. Nonetheless, we find that state-of-the-art denoising methods are visually clearly distinguishable and possess complementary strengths and failure modes. Motivated by this observation, we introduce a powerful non-parametric image restoration framework based on Regression Tree Fields (RTF). Our restoration model is a densely-connected tractable conditional random field that leverages existing methods to produce an image-dependent, globally consistent prediction. We estimate the conditional structure and parameters of our model from training data so as to directly optimize for popular performance measures. In terms of peak signal-to-noise-ratio (PSNR), our model improves on the best published denoising method by at least 0.26dB across a range of noise levels. Our most practical variant still yields statistically significant improvements, yet is over 20x faster than the strongest competitor. Our approach is well-suited for many more image restoration and low-level vision problems, as evidenced by substantial gains in tasks such as removal of JPEG blocking artefacts. {\textcopyright} 2012 Springer-Verlag.},
	isbn = {9783642337857},
	issn = {03029743},
	doi = {10.1007/978-3-642-33786-4_9},
	author = {Jancsary, Jeremy and Nowozin, Sebastian and Carsten Rother}
}
@conference {Lempitsky2007,
	title = {LogCut - Efficient graph cut optimization for markov random fields},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	year = {2007},
	abstract = {Markov Random Fields (MRFs) are ubiquitous in low-level computer vision. In this paper, we propose a new approach to the optimization of multi-labeled MRFs. Similarly to α-expansion it is based on iterative application of binary graph cut. However, the number of binary graph cuts required to compute a labelling grows only logarithmically with the size of label space, instead of linearly. We demonstrate that for applications such as optical flow, image restoration, and high resolution stereo, this gives an order of magnitude speed-up, for comparable energies. Iterations are performed by "fusion" of solutions, done with QPBO which is related to graph cut but can deal with non-submodularity. At convergence, the method achieves optima on a par with the best competitors, and sometimes even exceeds them. {\textcopyright}2007 IEEE.},
	doi = {10.1109/ICCV.2007.4408907},
	author = {Lempitsky, Victor and Carsten Rother and Blake, Andrew}
}
@article {Rother2003a,
	title = {Linear Multi-View Reconstruction for Translating Cameras},
	journal = {Nada.Kth.Se},
	year = {2003},
	abstract = {This paper presents a linear multi view reconstruction algorithm for translating cameras with fixed internal parameters. The main advantages of this method are a) points and camera centers are computed simultaneously from one linear system containing all image data b) the allowance of arbitrary missing data. We show that the key to linearize the SFM problem is the infinite homography which comprises of the cameras{\textquoteright} calibration and rotation. This insight unifies reconstruction methods for calibrated cameras, e.g. Olien-sis [9], and uncalibrated cameras, e.g. Rother-Carlsson [10]. A further contribution of this paper is the summary and comparison of different approaches to determine the infinite homography.},
	url = {http://www.nada.kth.se/ carstenr/papers/paper_ssab03.pdf},
	author = {Carsten Rother}
}
@conference {Rother2003b,
	title = {Linear multi-view reconstruction of points, lines, planes and cameras using a reference plane},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2},
	year = {2003},
	pages = {1210{\textendash}1217},
	abstract = {This paper presents a new linear method for reconstructing simultaneously 3D features (points, lines and planes) and cameras from many perspective views by solving a single linear system. It assumes that a real or virtual reference plane is visible in all views. We call it the Direct Reference Plane (DRP) method. It is well known that the projection relationship between uncalibrated cameras and 3D features is non-linear in the absence of a reference plane. With a known reference plane, points and cameras have a linear relationship, as shown in [16]. The main contribution of this paper is that lines and cameras, as well as, planes and cameras also have a linear relationship. Consequently, all 3D features and all cameras can be reconstructed simultaneously from a single linear system, which handles missing image measurements naturally. A further contribution is an extensive experimental comparison, using real data, of different reference plane and non-reference plane reconstruction methods. For difficult reference plane scenarios, with point or line features, the DRP method is superior to all compared methods. Finally, an extensive list of reference plane scenarios is presented, which shows the wide applicability of the DRP method.},
	doi = {10.1109/iccv.2003.1238629},
	url = {http://www.nada.kth.se/carstenr},
	author = {Carsten Rother}
}
@article {Rother2002a,
	title = {Linear multi view reconstruction and camera recovery using a reference plane},
	journal = {International Journal of Computer Vision},
	volume = {49},
	number = {2-3},
	year = {2002},
	pages = {117{\textendash}141},
	abstract = {This paper presents a linear algorithm for simultaneous computation of 3D points and camera positions from multiple perspective views based on having a reference plane visible in all views. The reconstruction and camera recovery is achieved in a single step by finding the null-space of a matrix built from image data using Singular Value Decomposition. Contrary to factorization algorithms this approach does not need to have all points visible in all views. This paper investigates two reference plane configurations: Finite reference planes defined by four coplanar points and infinite reference planes defined by vanishing points. A further contribution of this paper is the study of critical configurations for configurations with four coplanar points. By simultaneously reconstructing points and views we can exploit the numerical stabilizing effect of having wide spread cameras with large mutual baselines. This is demonstrated by reconstructing the outside and inside (courtyard) of a building on the basis of 35 views in one single Singular Value Decomposition.},
	keywords = {Critical configurations, Duality, Missing data, Multiple views, Planar parallax, Projective reconstruction, Reference plane, Structure from motion},
	issn = {09205691},
	doi = {10.1023/A:1020189404787},
	author = {Carsten Rother and Carlsson, Stefan}
}
@conference {Rother2002b,
	title = {Linear multi view reconstruction with missing data},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {2351},
	year = {2002},
	pages = {209{\textendash}324},
	abstract = {General multi view reconstruction from affine or projective cameras has so far been solved most efficiently using methods of factorizing image data matrices into camera and scene parameters. This can be done directly for affine cameras [18] and after computing epipolar geometry for projective cameras [17]. A notorious problem has been the fact that these factorization methods require all points to be visible in all views. This paper presents alternative algorithms for general affine and projective views of multiple points where a) points and camera centers are computed as the nullspace of one linear system constructed from all the image data b) only three points have to be visible in all views. The latter requirement increases the flexibility and usefulness of 3D reconstruction from multiple views. In the case of projective views and unknown epipolar geometry, an additional algorithm is presented which initially assumes affine views and compensates iteratively for the perspective effects. In this paper affine cameras are represented in a projective framework which is novel and leads to a unified treatment of parallel and perspective projection in a single framework. The experiments cover a wide range of different camera motions and compare the presented algorithms to factorization methods, including approaches which handle missing data.},
	keywords = {Affine and Projective Cameras, Linear Multiple View Reconstruction, Missing data, Structure from motion},
	isbn = {9783540437444},
	issn = {16113349},
	doi = {10.1007/3-540-47967-8_21},
	author = {Carsten Rother and Carlsson, Stefan}
}
@conference {Rother2001,
	title = {Linear multi view reconstruction and camera recovery},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {1},
	number = {3},
	year = {2001},
	pages = {42{\textendash}49},
	abstract = {This paper presents a linear algorithm for the simultaneous computation of 3D points and camera positions from multiple perspective views, based on having four points on a reference plane visible in all views. The reconstruction and camera recovery is achieved, in a single step, by finding the null-space of a matrix using singular value decomposition. Unlike factorization algorithms, the presented algorithm does not require all points to be visible in all views. By simultaneously reconstructing points and views the numerically stabilizing effect of having wide spread cameras with large mutual baselines is exploited. Experimental results are presented for both finite and infinite reference planes. An especially interesting application of this method is the reconstruction of architectural scenes with the reference plane taken as the plane at infinity which is visible via three orthogonal vanishing points. This is demonstrated by reconstructing the outside and inside (courtyard) of a building on the basis of 35 views in one single SVD.},
	keywords = {Critical configurations, Duality, Missing data, Multiple views, Planar parallax, Projective reconstruction, Reference plane, Structure from motion},
	doi = {10.1109/iccv.2001.937497},
	author = {Carsten Rother and Carlsson, Stefan}
}
