On-Line Variational Estimation of Dynamical Fluid Flows with Physics-Based Spatio-temporal Regularization

  • Paul Ruhnau
  • Annette Stahl
  • Christoph Schnörr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)


We present a variational approach to motion estimation of instationary fluid flows. Our approach extends prior work along two directions: (i) The full incompressible Navier-Stokes equation is employed in order to obtain a physically consistent regularization which does not suppress turbulent flow variations. (ii) Regularization along the time-axis is employed as well, but formulated in a receding horizon manner contrary to previous approaches to spatio-temporal regularization. This allows for a recursive on-line (non-batch) implementation of our estimation framework.

Ground-truth evaluations for simulated turbulent flows demonstrate that due to imposing both physical consistency and temporal coherency, the accuracy of flow estimation compares favourably even with optical flow approaches based on higher-order div-curl regularization.


Particle Image Velocimetry Motion Estimation Temporal Coherency Saddle Point Problem Particle Image Velocimetry Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Paul Ruhnau
    • 1
  • Annette Stahl
    • 1
  • Christoph Schnörr
    • 1
  1. 1.Computer Vision, Graphics, and Pattern Recognition Group, Department of Mathematics and Computer ScienceUniversity of MannheimMannheimGermany

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