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3D Fluid Flow Estimation with Integrated Particle Reconstruction

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Book cover Pattern Recognition (GCPR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11269))

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Abstract

The standard approach to densely reconstruct the motion in a volume of fluid is to inject high-contrast tracer particles and record their motion with multiple high-speed cameras. Almost all existing work processes the acquired multi-view video in two separate steps: first, a per-frame reconstruction of the particles, usually in the form of soft occupancy likelihoods in a voxel representation; followed by 3D motion estimation, with some form of dense matching between the precomputed voxel grids from different time steps. In this sequential procedure, the first step cannot use temporal consistency considerations to support the reconstruction, while the second step has no access to the original, high-resolution image data. We show, for the first time, how to jointly reconstruct both the individual tracer particles and a dense 3D fluid motion field from the image data, using an integrated energy minimization. Our hybrid Lagrangian/Eulerian model explicitly reconstructs individual particles, and at the same time recovers a dense 3D motion field in the entire domain. Making particles explicit greatly reduces the memory consumption and allows one to use the high-resolution input images for matching. Whereas the dense motion field makes it possible to include physical a-priori constraints and account for the incompressibility and viscosity of the fluid. The method exhibits greatly (\({\approx }70\%\)) improved results over a recent baseline with two separate steps for 3D reconstruction and motion estimation. Our results with only two time steps are comparable to those of state-of-the-art tracking-based methods that require much longer sequences.

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Acknowledgements

This work was supported by ETH grant 29 14-1. Christoph Vogel acknowledges support from the ERC starting grant 640156, ‘HOMOVIS’.

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Authors and Affiliations

  1. Photogrammetry and Remote Sensing, ETH Zurich, Zürich, Switzerland

    Katrin Lasinger & Konrad Schindler

  2. Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria

    Christoph Vogel & Thomas Pock

  3. AIT Austrian Institute of Technology, Vienna, Austria

    Thomas Pock

Authors
  1. Katrin Lasinger
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  2. Christoph Vogel
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  3. Thomas Pock
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  4. Konrad Schindler
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Corresponding author

Correspondence to Katrin Lasinger .

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Editors and Affiliations

  1. University of Freiburg, Freiburg im Breisgau, Baden-Württemberg, Germany

    Thomas Brox

  2. University of Stuttgart, Stuttgart, Baden-Württemberg, Germany

    Andrés Bruhn

  3. CISPA Helmholtz Center for Information Security, Saarbrücken, Germany

    Mario Fritz

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Lasinger, K., Vogel, C., Pock, T., Schindler, K. (2019). 3D Fluid Flow Estimation with Integrated Particle Reconstruction. In: Brox, T., Bruhn, A., Fritz, M. (eds) Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science(), vol 11269. Springer, Cham. https://doi.org/10.1007/978-3-030-12939-2_22

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  • DOI: https://doi.org/10.1007/978-3-030-12939-2_22

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  • Online ISBN: 978-3-030-12939-2

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