Graphflow—6D large displacement scene flow via graph matching

TitleGraphflow—6D large displacement scene flow via graph matching
Publication TypeConference Paper
Year of Publication2015
AuthorsAbu Alhaija, H, Sellent, A, Kondermann, D, Rother, C
Conference NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISBN Number9783319249469

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.

Citation KeyAlhaija2015