@conference {Nair2015,
	title = {Reflection modeling for passive stereo},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2015 Inter},
	year = {2015},
	pages = {2291{\textendash}2299},
	abstract = {Stereo reconstruction in presence of reality faces many challenges that still need to be addressed. This paper considers reflections, which introduce incorrect matches due to the observation violating the diffuse-world assumption underlying the majority of stereo techniques. Unlike most existing work, which employ regularization or robust data terms to suppress such errors, we derive two least squares models from first principles that generalize diffuse world stereo and explicitly take reflections into account. These models are parametrized by depth, orientation and material properties, resulting in a total of up to 5 parameters per pixel that have to be estimated. Additionally large non-local interactions between viewed and reflected surface have to be taken into account. These two properties make inference of the model appear prohibitive, but we present evidence that inference is actually possible using a variant of patch match stereo.},
	isbn = {9781467383912},
	issn = {15505499},
	doi = {10.1109/ICCV.2015.264},
	author = {Nair, Rahul and Fitzgibbon, Andrew and Kondermann, Daniel and Carsten Rother}
}
@conference {Hornacek2014,
	title = {Highly overparameterized optical flow using PatchMatch belief propagation},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8691 LNCS},
	number = {PART 3},
	year = {2014},
	pages = {220{\textendash}234},
	abstract = {Motion in the image plane is ultimately a function of 3D motion in space. We propose to compute optical flow using what is ostensibly an extreme overparameterization: depth, surface normal, and frame-to-frame 3D rigid body motion at every pixel, giving a total of 9 DoF. The advantages of such an overparameterization are twofold: first, geometrically meaningful reasoning can be called upon in the optimization, reflecting possible 3D motion in the underlying scene; second, the {\textquoteright}fronto-parallel{\textquoteright} assumption implicit in the use of traditional matching pixel windows is ameliorated because the parameterization determines a plane-induced homography at every pixel. We show that optimization over this high-dimensional, continuous state space can be carried out using an adaptation of the recently introduced PatchMatch Belief Propagation (PMBP) energy minimization algorithm, and that the resulting flow fields compare favorably to the state of the art on a number of small- and large-displacement datasets. {\textcopyright} 2014 Springer International Publishing.},
	keywords = {9 DoF, large displacement, optical flow, PatchMatch, PMBP},
	isbn = {9783319105772},
	issn = {16113349},
	doi = {10.1007/978-3-319-10578-9_15},
	author = {Horn{\'a}{\v c}ek, Michael and Besse, Frederic and Kautz, Jan and Fitzgibbon, Andrew and Carsten Rother}
}
@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 {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 {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 {Hornacek2014a,
	title = {SphereFlow: 6 DoF scene flow from RGB-D pairs},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2014},
	pages = {3526{\textendash}3533},
	abstract = {We take a new approach to computing dense scene flow between a pair of consecutive RGB-D frames. We exploit the availability of depth data by seeking correspondences with respect to patches specified not as the pixels inside square windows, but as the 3D points that are the inliers of spheres in world space. Our primary contribution is to show that by reasoning in terms of such patches under 6 DoF rigid body motions in 3D, we succeed in obtaining compelling results at displacements large and small without relying on either of two simplifying assumptions that pervade much of the earlier literature: brightness constancy or local surface planarity. As a consequence of our approach, our output is a dense field of 3D rigid body motions, in contrast to the 3D translations that are the norm in scene flow. Reasoning in our manner additionally allows us to carry out occlusion handling using a 6 DoF consistency check for the flow computed in both directions and a patchwise silhouette check to help reason about alignments in occlusion areas, and to promote smoothness of the flow fields using an intuitive local rigidity prior. We carry out our optimization in two steps, obtaining a first correspondence field using an adaptation of PatchMatch, and subsequently using alpha-expansion to jointly handle occlusions and perform regularization. We show attractive flow results on challenging synthetic and real-world scenes that push the practical limits of the aforementioned assumptions.},
	isbn = {9781479951178},
	issn = {10636919},
	doi = {10.1109/CVPR.2014.451},
	author = {Horn{\'a}{\v c}ek, Michael and Fitzgibbon, Andrew and Carsten Rother}
}
