@article {Hosni2013,
	title = {Fast cost-volume filtering for visual correspondence and beyond},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {35},
	number = {2},
	year = {2013},
	pages = {504{\textendash}511},
	abstract = {Many computer vision tasks can be formulated as labeling problems. The desired solution is often a spatially smooth labeling where label transitions are aligned with color edges of the input image. We show that such solutions can be efficiently achieved by smoothing the label costs with a very fast edge-preserving filter. In this paper, we propose a generic and simple framework comprising three steps: 1) constructing a cost volume, 2) fast cost volume filtering, and 3) Winner-Takes-All label selection. Our main contribution is to show that with such a simple framework state-of-the-art results can be achieved for several computer vision applications. In particular, we achieve 1) disparity maps in real time whose quality exceeds those of all other fast (local) approaches on the Middlebury stereo benchmark, and 2) optical flow fields which contain very fine structures as well as large displacements. To demonstrate robustness, the few parameters of our framework are set to nearly identical values for both applications. Also, competitive results for interactive image segmentation are presented. With this work, we hope to inspire other researchers to leverage this framework to other application areas. {\textcopyright} 2012 IEEE.},
	keywords = {Interactive Image Segmentation, optical flow, Stereo Matching},
	issn = {01628828},
	doi = {10.1109/TPAMI.2012.156},
	author = {Hosni, Asmaa and Rhemann, Christoph and Bleyer, Michael and Carsten Rother and Gelautz, Margrit}
}
@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}
}
@conference {Bleyer2011,
	title = {Object stereo Joint stereo matching and object segmentation},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2011},
	pages = {3081{\textendash}3088},
	abstract = {This paper presents a method for joint stereo matching and object segmentation. In our approach a 3D scene is represented as a collection of visually distinct and spatially coherent objects. Each object is characterized by three different aspects: a color model, a 3D plane that approximates the object{\textquoteright}s disparity distribution, and a novel 3D connectivity property. Inspired by Markov Random Field models of image segmentation, we employ object-level color models as a soft constraint, which can aid depth estimation in powerful ways. In particular, our method is able to recover the depth of regions that are fully occluded in one input view, which to our knowledge is new for stereo matching. Our model is formulated as an energy function that is optimized via fusion moves. We show high-quality disparity and object segmentation results on challenging image pairs as well as standard benchmarks. We believe our work not only demonstrates a novel synergy between the areas of image segmentation and stereo matching, but may also inspire new work in the domain of automatic and interactive object-level scene manipulation. {\textcopyright} 2011 IEEE.},
	isbn = {9781457703942},
	issn = {10636919},
	doi = {10.1109/CVPR.2011.5995581},
	author = {Bleyer, Michael and Carsten Rother and Kohli, Pushmeet and Scharstein, Daniel and Sinha, Sudipta}
}
@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 {Bleyer2010,
	title = {Surface stereo with soft segmentation},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2010},
	pages = {1570{\textendash}1577},
	abstract = {This paper proposes a new stereo model which encodes the simple assumption that the scene is composed of a few, smooth surfaces. A key feature of our model is the surface-based representation, where each pixel is assigned to a 3D surface (planes or B-splines). This representation enables several important contributions: Firstly, we formulate a higher-order prior which states that pixels of similar appearance are likely to belong to the same 3D surface. This enables to incorporate the very popular color segmentation constraint in a soft and principled way. Secondly, we use a global MDL prior to penalize the number of surfaces. Thirdly, we are able to incorporate, in a simple way, a prior which favors low curvature surfaces. Fourthly, we improve the asymmetric occlusion model by disallowing pixels of the same surface to occlude each other. Finally, we use the known fusion move approach which enables a powerful optimization of our model, despite the infinite number of possible labelings (surfaces). {\textcopyright}2010 IEEE.},
	isbn = {9781424469840},
	issn = {10636919},
	doi = {10.1109/CVPR.2010.5539783},
	author = {Bleyer, Michael and Carsten Rother and Kohli, Pushmeet}
}
@conference {Bleyer2009,
	title = {A stereo approach that handles the matting problem via imagewarping},
	booktitle = {2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009},
	volume = {2009 IEEE},
	year = {2009},
	pages = {501{\textendash}508},
	abstract = {We propose an algorithm that simultaneously extracts disparities and alpha matting information given a stereo image pair. Our method divides the reference image into a set of overlapping, partially transparent color segments. Each segment pixel is assigned an alpha value and color. The disparity inside the segment is modeled via a plane. The goodness of alphas, colors and disparity planes is measured by a new energy function. Its basic idea is to use the three parameters for generating artificial views representing the left and right images. If alphas, colors and disparity planes are correct, these artificial images should be very similar to the real ones. For generating the artificial right view, we warp all pixels of the left into the geometry of the right image using the disparity planes. We introduce the assumption of constant solidity in order to correctly model how pixels{\textquoteright} alpha values are affected by the warping operation. Experimental results on the Middlebury set show that our algorithm gives good results in comparison to the state-of-the-art in stereo matching. {\textcopyright}2009 IEEE.},
	keywords = {Alpha Matting, Stereo Matching, Stereo Matting},
	isbn = {9781424439935},
	doi = {10.1109/CVPRW.2009.5206656},
	author = {Bleyer, Michael and Gelautz, Margrit and Carsten Rother and Rhemann, Christoph}
}
