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; The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 1870-1879

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.

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[pdf]
[bibtex]
@InProceedings{Zang_2020_CVPR,
author = {Zang, Guangming and Idoughi, Ramzi and Wang, Congli and Bennett, Anthony and Du, Jianguo and Skeen, Scott and Roberts, William L. and Wonka, Peter and Heidrich, Wolfgang},
title = {TomoFluid: Reconstructing Dynamic Fluid From Sparse View Videos},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}