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.
Cite
Text
Zang et al. "TomoFluid: Reconstructing Dynamic Fluid from Sparse View Videos." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. doi:10.1109/CVPR42600.2020.00194Markdown
[Zang et al. "TomoFluid: Reconstructing Dynamic Fluid from Sparse View Videos." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.](https://mlanthology.org/cvpr/2020/zang2020cvpr-tomofluid/) doi:10.1109/CVPR42600.2020.00194BibTeX
@inproceedings{zang2020cvpr-tomofluid,
title = {{TomoFluid: Reconstructing Dynamic Fluid from Sparse View Videos}},
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},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2020},
doi = {10.1109/CVPR42600.2020.00194},
url = {https://mlanthology.org/cvpr/2020/zang2020cvpr-tomofluid/}
}