Coupled Fluid Density and Motion from Single Views
This method reduces setup complexity for fluid reconstruction, potentially enabling applications with online videos or smartphones, but it appears incremental as it builds on prior work with physical priors.
The authors tackled the problem of reconstructing a fluid's 3D density and motion from a single sequence of images, using physical priors and a novel coupling strategy, and demonstrated results with synthetic tests and real smoke plumes captured by a Raspberry Pi camera.
We 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.