  In the past decades, great progress has been made in the field of optical and
particle-based measurement techniques for experimental analysis of fluid flows.
Particle Image Velocimetry (PIV) technique is widely used to identify flow
parameters from time-consecutive snapshots of particles injected into the
fluid. The computation is performed as post-processing of the experimental data
via proximity measure between particles in frames of reference. However, the
post-processing step becomes problematic as the motility and density of the
particles increases, since the data emerges in extreme rates and volumes.
Moreover, existing algorithms for PIV either provide sparse estimations of the
flow or require large computational time frame preventing from on-line use. The
goal of this manuscript is therefore to develop an accurate on-line algorithm
for estimation of the fine-grained velocity field from PIV data. As the data
constitutes a pair of images, we employ computer vision methods to solve the
problem. In this work, we introduce a convolutional neural network adapted to
the problem, namely Volumetric Correspondence Network (VCN) which was recently
proposed for the end-to-end optical flow estimation in computer vision. The
network is thoroughly trained and tested on a dataset containing both synthetic
and real flow data. Experimental results are analyzed and compared to that of
conventional methods as well as other recently introduced methods based on
neural networks. Our analysis indicates that the proposed approach provides
improved efficiency also keeping accuracy on par with other state-of-the-art
methods in the field. We also verify through a-posteriori tests that our newly
constructed VCN schemes are reproducing well physically relevant statistics of
velocity and velocity gradients.
