  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.
