@conference {Alhaija2015,
	title = {Graphflow{\textemdash}6D large displacement scene flow via graph matching},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {9358},
	year = {2015},
	pages = {285{\textendash}296},
	abstract = {We present an approach for computing dense scene flow from two large displacement RGB-D images. When dealing with large displacements the crucial step is to estimate the overall motion correctly. While state-of-the-art approaches focus on RGB information to establish guiding correspondences, we explore the power of depth edges. To achieve this, we present a new graph matching technique that brings sparse depth edges into correspondence. An additional contribution is the formulation of a continuous-label energy which is used to densify the sparse graph matching output. We present results on challenging Kinect images, for which we outperform state-of-the-art techniques.},
	isbn = {9783319249469},
	issn = {16113349},
	doi = {10.1007/978-3-319-24947-6_23},
	author = {Hassan Abu Alhaija and Sellent, Anita and Kondermann, Daniel and Carsten Rother}
}
