@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}
}
@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}
}
@conference {Hornacek2013,
	title = {Depth super resolution by rigid body self-similarity in 3D},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2013},
	pages = {1123{\textendash}1130},
	abstract = {We tackle the problem of jointly increasing the spatial resolution and apparent measurement accuracy of an input low-resolution, noisy, and perhaps heavily quantized depth map. In stark contrast to earlier work, we make no use of ancillary data like a color image at the target resolution, multiple aligned depth maps, or a database of high-resolution depth exemplars. Instead, we proceed by identifying and merging patch correspondences within the input depth map itself, exploiting patch wise scene self-similarity across depth such as repetition of geometric primitives or object symmetry. While the notion of {\textquoteright}single-image{\textquoteright} super resolution has successfully been applied in the context of color and intensity images, we are to our knowledge the first to present a tailored analogue for depth images. Rather than reason in terms of patches of 2D pixels as others have before us, our key contribution is to proceed by reasoning in terms of patches of 3D points, with matched patch pairs related by a respective 6 DoF rigid body motion in 3D. In support of obtaining a dense correspondence field in reasonable time, we introduce a new 3D variant of Patch Match. A third contribution is a simple, yet effective patch up scaling and merging technique, which predicts sharp object boundaries at the target resolution. We show that our results are highly competitive with those of alternative techniques leveraging even a color image at the target resolution or a database of high-resolution depth exemplars. {\textcopyright} 2013 IEEE.},
	keywords = {dense matching, depth super resolution, optimization},
	issn = {10636919},
	doi = {10.1109/CVPR.2013.149},
	author = {Horn{\'a}{\v c}ek, Michael and Rhemann, Christoph and Gelautz, Margrit and Carsten Rother}
}
