J. Xiong, Q. Fu, R. Idoughi, W. Heidrich
ICCP, (2018)
Camera image, the 3D particle positions
and the dense fluid vector field can be reconstructed using an optimization-based approach.
Despite significant recent progress, dense, time-resolved imaging of complex,
non-stationary 3D flow velocities remains an elusive goal. In this work we
tackle this problem by extending an established 2D method, Particle Imaging
Velocimetry, to three dimensions by encoding depth into color. The encoding
is achieved by illuminating the flow volume with a continuum of light planes
(a “rainbow”), such that each depth corresponds to a specific wavelength of
light. A diffractive component in the camera optics ensures that all planes are
in focus simultaneously. With this setup, a single color camera is sufficient
for tracking 3D trajectories of particles by combining 2D spatial and 1D
color information.
For reconstruction, we derive an image formation model for recovering
stationary 3D particle positions. 3D velocity estimation is achieved with a
variant of 3D optical flow that accounts for both physical constraints as well
as the rainbow image formation model. We evaluate our method with both
simulations and an experimental prototype setup.
