GraphFlow – 6D Large Displacement Scene Flow via Graph Matching

  • Hassan Abu Alhaija
  • Anita Sellent
  • Daniel Kondermann
  • Carsten Rother
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9358)

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.

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Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Hassan Abu Alhaija
    • 1
    • 2
  • Anita Sellent
    • 1
    • 3
  • Daniel Kondermann
    • 2
  • Carsten Rother
    • 1
  1. 1.TU DresdenDresdenGermany
  2. 2.Heidelberg UniversityHeidelbergGermany
  3. 3.TU DarmstadtDarmstadtGermany

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