  A novel method performing 3D PTV from double frame multi-camera images is
introduced. Particle velocities are estimated by following three steps.
Firstly, separate particle reconstructions with a sparsity-based algorithm are
performed on a fine grid. Secondly, they are expanded on a coarser grid on
which 3D correlation is performed, yielding a predictor displacement field that
allows to efficiently match particles at the two time instants. As these
particles are still located on a voxel grid, the third, final step achieves
particle position refinement to their actual subvoxel position by a global
optimization process, also accounting for their intensities. As it strongly
leverages on principles from tomographic reconstruction, the technique is
termed Double-Frame Tomo-PTV (DF-TPTV). Synthetic tests on a complex turbulent
flow show that the method achieves high particle and vector detection
efficiency, up to seeding densities of around 0.08 particles per pixel (ppp),
while its root-mean-square error of velocity estimation is lower to that of
state-of-the-art similar methods. Results from an experimental campaign on a
transitional round air jet at Reynolds number 4600 are also presented. During
the tests, seeding density varies from 0.06 to 0.03 ppp on average. Associated
to an outlier rejection scheme based on temporal statistics, DF-TPTV vector
fields truthfully correspond to the instantaneous jet dynamics. Quantitative
performance assessment is provided by introducing statistics performed by bin
averaging, upon assuming statistical axisymmetry of the jet. Mean and
fluctuating axial velocity components in the jet near-field are compared with
reference results obtained from planar PIV at higher seeding density.
