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

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 proceeding › Conference contribution

25 Scopus citations

Abstract

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 language	English (US)
Title of host publication	Conference on Computer Vision and Pattern Recognition
Publisher	IEEE
State	Published - 2020

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Cite this

@inproceedings{d3735ec6ad124ed8b8562054324e780e,

title = "TomoFluid: Reconstructing Dynamic Fluid from Sparse View Videos",

abstract = "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.",

author = "Guangming Zang and Ramzi Idoughi and Congli Wang and Anthony Bennett and Jianguo Du and Scott Skeen and Roberts, {William L.} and Peter Wonka and Wolfgang Heidrich",

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.",

year = "2020",

language = "English (US)",

booktitle = "Conference on Computer Vision and Pattern Recognition",

publisher = "IEEE",

}

TY - GEN

T1 - TomoFluid: Reconstructing Dynamic Fluid from Sparse View Videos

AU - Zang, Guangming

AU - Idoughi, Ramzi

AU - Wang, Congli

AU - Bennett, Anthony

AU - Du, Jianguo

AU - Skeen, Scott

AU - Roberts, William L.

AU - Wonka, Peter

AU - Heidrich, Wolfgang

N1 - 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.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/662380

M3 - Conference contribution

BT - Conference on Computer Vision and Pattern Recognition

PB - IEEE

ER -

TomoFluid: Reconstructing Dynamic Fluid from Sparse View Videos

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