  Particle Image Velocimetry (PIV) is a classical flow estimation problem which
is widely considered and utilised, especially as a diagnostic tool in
experimental fluid dynamics and the remote sensing of environmental flows.
Recently, the development of deep learning based methods has inspired new
approaches to tackle the PIV problem. These supervised learning based methods
are driven by large volumes of data with ground truth training information.
However, it is difficult to collect reliable ground truth data in large-scale,
real-world scenarios. Although synthetic datasets can be used as alternatives,
the gap between the training set-ups and real-world scenarios limits
applicability. We present here what we believe to be the first work which takes
an unsupervised learning based approach to tackle PIV problems. The proposed
approach is inspired by classic optical flow methods. Instead of using ground
truth data, we make use of photometric loss between two consecutive image
frames, consistency loss in bidirectional flow estimates and spatial smoothness
loss to construct the total unsupervised loss function. The approach shows
significant potential and advantages for fluid flow estimation. Results
presented here demonstrate that our method outputs competitive results compared
with classical PIV methods as well as supervised learning based methods for a
broad PIV dataset, and even outperforms these existing approaches in some
difficult flow cases. Codes and trained models are available at
https://github.com/erizmr/UnLiteFlowNet-PIV.
