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    Stereo Event-Based Particle Tracking Velocimetry for 3D Fluid Flow Reconstruction

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    Name:
    Wang2020StereoEventPTV.pdf
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    6.505Mb
    Format:
    PDF
    Description:
    Accepted Manuscript
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    Type
    Conference Paper
    Authors
    Wang, Yuanhao cc
    Idoughi, Ramzi
    Heidrich, Wolfgang cc
    KAUST Department
    Computational Imaging Group
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Visual Computing Center (VCC)
    Date
    2020-10-07
    Online Publication Date
    2020-10-07
    Print Publication Date
    2020
    Permanent link to this record
    http://hdl.handle.net/10754/665631
    
    Metadata
    Show full item record
    Abstract
    Existing Particle Imaging Velocimetry techniques require the use of high-speed cameras to reconstruct time-resolved fluid flows. These cameras provide high-resolution images at high frame rates, which generates bandwidth and memory issues. By capturing only changes in the brightness with a very low latency and at low data rate, event-based cameras have the ability to tackle such issues. In this paper, we present a new framework that retrieves dense 3D measurements of the fluid velocity field using a pair of event-based cameras. First, we track particles inside the two event sequences in order to estimate their 2D velocity in the two sequences of images. A stereo-matching step is then performed to retrieve their 3D positions. These intermediate outputs are incorporated into an optimization framework that also includes physically plausible regularizers, in order to retrieve the 3D velocity field. Extensive experiments on both simulated and real data demonstrate the efficacy of our approach.
    Citation
    Wang, Y., Idoughi, R., & Heidrich, W. (2020). Stereo Event-Based Particle Tracking Velocimetry for 3D Fluid Flow Reconstruction. Lecture Notes in Computer Science, 36–53. doi:10.1007/978-3-030-58526-6_3
    Sponsors
    This work was supported by King Abdullah University of Science and Technology as part of VCC Center Competitive Funding. The authors would like to thank the anonymous reviewers for their valuable comments. We thank Hadi Amata for his help in the design of the hexagonal tank and the camera extension tubes. We also thank Congli Wang for helping in the use of the event cameras.
    Publisher
    Springer Nature
    Conference/Event name
    ECCV: European Conference on Computer Vision
    ISBN
    9783030585259
    9783030585266
    DOI
    10.1007/978-3-030-58526-6_3
    Additional Links
    http://link.springer.com/10.1007/978-3-030-58526-6_3
    Relations
    Is Supplemented By:
    • [Software]
      Title: vccimaging/StereoEventPTV: Source Code for the Event PTV. Publication Date: 2020-08-19. github: vccimaging/StereoEventPTV Handle: 10754/667142
    • [Dataset]
      Wang, Y. (2020). Stereo-Event flow dataset [Data set]. KAUST Research Repository. https://doi.org/10.25781/KAUST-MSG02. DOI: 10.25781/KAUST-MSG02 Handle: 10754/664999
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-58526-6_3
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Electrical and Computer Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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