KAUST DepartmentComputational Imaging Group
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Visual Computing Center (VCC)
Online Publication Date2018-09-12
Print Publication Date2018-12
Permanent link to this recordhttp://hdl.handle.net/10754/631618
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AbstractWe present a novel method to reconstruct a fluid's 3D density and motion based on just a single sequence of images. This is rendered possible by using powerful physical priors for this strongly under-determined problem. More specifically, we propose a novel strategy to infer density updates strongly coupled to previous and current estimates of the flow motion. Additionally, we employ an accurate discretization and depth-based regularizers to compute stable solutions. Using only one view for the reconstruction reduces the complexity of the capturing setup drastically and could even allow for online video databases or smart-phone videos as inputs. The reconstructed 3D velocity can then be flexibly utilized, e.g., for re-simulation, domain modification or guiding purposes. We will demonstrate the capacity of our method with a series of synthetic test cases and the reconstruction of real smoke plumes captured with a Raspberry Pi camera.
CitationEckert M-L, Heidrich W, Thuerey N (2018) Coupled Fluid Density and Motion from Single Views. Computer Graphics Forum 37: 47–58. Available: http://dx.doi.org/10.1111/cgf.13511.
SponsorsThis work was funded by the ERC Starting Grant realFlow (StG-2015-637014) and supported by King Abdullah University of Science and Technology under Individual Baseline Funding.
JournalComputer Graphics Forum