A Highly Efficient GPU Implementation for Variational Optic Flow Based on the Euler-Lagrange Framework

  • Pascal Gwosdek
  • Henning Zimmer
  • Sven Grewenig
  • Andrés Bruhn
  • Joachim Weickert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6554)


The Euler-Lagrange (EL) framework is the most widely-used strategy for solving variational optic flow methods. We present the first approach that solves the EL equations of state-of-the-art methods on sequences with \(640 \!\times\! 480\) pixels in near-realtime on GPUs. This performance is achieved by combining two ideas: (i) We extend the recently proposed Fast Explicit Diffusion (FED) scheme to optic flow, and additionally embed it into a coarse-to-fine strategy. (ii) We parallelise our complete algorithm on a GPU, where a careful optimisation of global memory operations and an efficient use of on-chip memory guarantee a good performance. Applying our approach to the variational ‘Complementary Optic Flow’ method (Zimmer et al. (2009)), we obtain highly accurate flow fields in less than a second. This currently constitutes the fastest method in the top 10 of the widely used Middlebury benchmark.


Global Memory Data Term Smoothness Term British Machine Vision Constancy Assumption 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pascal Gwosdek
    • 1
  • Henning Zimmer
    • 1
  • Sven Grewenig
    • 1
  • Andrés Bruhn
    • 2
  • Joachim Weickert
    • 1
  1. 1.Mathematical Image Analysis Group, Faculty of Mathematics and Computer ScienceSaarland UniversitySaarbrückenGermany
  2. 2.Vision and Image Processing Group,Cluster of Excellence Multimodal Computing and InteractionSaarland UniversitySaarbrückenGermany

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