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
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.
BibTeX
@inproceedings{cvpr2020_tomofluidreconst,
title = {TomoFluid: Reconstructing Dynamic Fluid From Sparse View Videos},
author = {Guangming Zang and Ramzi Idoughi and Congli Wang and Anthony Bennett and Jianguo Du and Scott Skeen and William L. Roberts and Peter Wonka and Wolfgang Heidrich},
booktitle = {CVPR 2020},
year = {2020}
}