TomoFluid: Reconstructing Dynamic Fluid from Sparse View Videos

Guangming Zang, Ramzi Idoughi, Congli Wang, Anthony Bennett, Jianguo Du, Scott Skeen, William L. Roberts, Peter Wonka, Wolfgang Heidrich

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    19 Scopus citations


    Visible light tomography is a promising and increasingly popular technique for fluid imaging. However, the use of a sparse number of viewpoints in the capturing setups makes the reconstruction of fluid flows very challenging. In this paper, we present a state-of-the-art 4D tomographic reconstruction framework that integrates several regularizers into a multi-scale matrix free optimization algorithm. In addition to existing regularizers, we propose two new regularizers for improved results: a regularizer based on view interpolation of projected images and a regularizer to encourage reprojection consistency. We demonstrate our method with extensive experiments on both simulated and real data.
    Original languageEnglish (US)
    Title of host publicationConference on Computer Vision and Pattern Recognition
    StatePublished - 2020

    Bibliographical note

    KAUST Repository Item: Exported on 2020-10-01
    Acknowledgements: This work was supported by KAUST as part of VCC and CCRC Center Competitive Funding and KAUST Competitive Research Grants. We thank the anonymous reviewers for their insightful comments, and Yuansi Tian for helping with the data collection.


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