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    Rainbow Particle Imaging Velocimetry for Dense 3D Fluid Velocity Imaging

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    Type
    Poster
    Authors
    Xiong, Jinhui cc
    Idoughi, Ramzi
    Aguirre-Pablo, Andres A. cc
    Aljedaani, Abdulrahman Barakat cc
    Dun, Xiong
    Fu, Qiang cc
    Thoroddsen, Sigurdur T cc
    Heidrich, Wolfgang cc
    KAUST Department
    Computational Imaging Group
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    High-Speed Fluids Imaging Laboratory
    Mechanical Engineering Program
    Physical Science and Engineering (PSE) Division
    Visual Computing Center (VCC)
    Date
    2017-04-11
    Permanent link to this record
    http://hdl.handle.net/10754/623948
    
    Metadata
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    Abstract
    Despite significant recent progress, dense, time-resolved imaging of complex, non-stationary 3D flow velocities remains an elusive goal. In this work we tackle this problem by extending an established 2D method, Particle Imaging Velocimetry, to three dimensions by encoding depth into color. The encoding is achieved by illuminating the flow volume with a continuum of light planes (a “rainbow”), such that each depth corresponds to a specific wavelength of light. A diffractive component in the camera optics ensures that all planes are in focus simultaneously. For reconstruction, we derive an image formation model for recovering stationary 3D particle positions. 3D velocity estimation is achieved with a variant of 3D optical flow that accounts for both physical constraints as well as the rainbow image formation model. We evaluate our method with both simulations and an experimental prototype setup.
    Conference/Event name
    KAUST Research Conference 2017: Visual Computing – Modeling and Reconstruction
    Collections
    Posters; Physical Science and Engineering (PSE) Division; Computer Science Program; Mechanical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division; KAUST Research Conference 2017: Visual Computing – Modeling and Reconstruction

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