Volumetric Flow Estimation for Incompressible Fluids Using the Stationary Stokes Equations. Lasinger, K., Vogel, C., & Schindler, K. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2584-2592, 4, 2017. IEEE. ![Volumetric Flow Estimation for Incompressible Fluids Using the Stationary Stokes Equations [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 3 downloads In experimental fluid dynamics, the flow in a volume of fluid is observed by injecting high-contrast tracer particles and tracking them in multi-view video. Fluid dynamics re-searchers have developed variants of space-carving to re-construct the 3D particle distribution at a given time-step, and then use relatively simple local matching to recover the motion over time. On the contrary, estimating the opti-cal flow between two consecutive images is a long-standing standard problem in computer vision, but only little work exists about volumetric 3D flow. Here, we propose a varia-tional method for 3D fluid flow estimation from multi-view data. We start from a 3D version of the standard varia-tional flow model, and investigate different regularization schemes that ensure divergence-free flow fields, to account for the physics of incompressible fluids. Moreover, we pro-pose a semi-dense formulation, to cope with the computa-tional demands of large volumetric datasets. Flow is esti-mated and regularized at a lower spatial resolution, while the data term is evaluated at full resolution to preserve the discriminative power and geometric precision of the local particle distribution. Extensive experiments reveal that a simple sum of squared differences (SSD) is the most suit-able data term for our application. For regularization, an energy whose Euler-Lagrange equations correspond to the stationary Stokes equations leads to the best results. This strictly enforces a divergence-free flow and additionally pe-nalizes the squared gradient of the flow.
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abstract = {In experimental fluid dynamics, the flow in a volume of fluid is observed by injecting high-contrast tracer particles and tracking them in multi-view video. Fluid dynamics re-searchers have developed variants of space-carving to re-construct the 3D particle distribution at a given time-step, and then use relatively simple local matching to recover the motion over time. On the contrary, estimating the opti-cal flow between two consecutive images is a long-standing standard problem in computer vision, but only little work exists about volumetric 3D flow. Here, we propose a varia-tional method for 3D fluid flow estimation from multi-view data. We start from a 3D version of the standard varia-tional flow model, and investigate different regularization schemes that ensure divergence-free flow fields, to account for the physics of incompressible fluids. Moreover, we pro-pose a semi-dense formulation, to cope with the computa-tional demands of large volumetric datasets. Flow is esti-mated and regularized at a lower spatial resolution, while the data term is evaluated at full resolution to preserve the discriminative power and geometric precision of the local particle distribution. Extensive experiments reveal that a simple sum of squared differences (SSD) is the most suit-able data term for our application. For regularization, an energy whose Euler-Lagrange equations correspond to the stationary Stokes equations leads to the best results. This strictly enforces a divergence-free flow and additionally pe-nalizes the squared gradient of the flow.},
bibtype = {inproceedings},
author = {Lasinger, Katrin and Vogel, Christoph and Schindler, Konrad},
doi = {10.1109/ICCV.2017.280},
booktitle = {2017 IEEE International Conference on Computer Vision (ICCV)}
}
Downloads: 3
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