  We introduce Recurrent All-Pairs Field Transforms for Stereoscopic Particle
Image Velocimetry (RAFT-StereoPIV). Our approach leverages deep optical flow
learning to analyze time-resolved and double-frame particle images from on-site
measurements, particularly from the 'Ring of Fire,' as well as from wind tunnel
measurements for real-time aerodynamic analysis. A multi-fidelity dataset
comprising both Reynolds-Averaged Navier-Stokes (RANS) and Direct Numerical
Simulation (DNS) was used to train our model. RAFT-StereoPIV outperforms all
PIV state-of-the-art deep learning models on benchmark datasets, with a 68$\%$
error reduction on the validation dataset, Problem Class 2, and a 47$\%$ error
reduction on the unseen test dataset, Problem Class 1, demonstrating its
robustness and generalizability. In comparison to the most recent works in the
field of deep learning for PIV, where the main focus was the methodology
development and the application was limited to either 2D flow cases or simple
experimental data, we extend deep learning-based PIV for industrial
applications and 3D flow field estimation. As we apply the trained network to
three-dimensional highly turbulent PIV data, we are able to obtain flow
estimates that maintain spatial resolution of the input image sequence. In
contrast, the traditional methods produce the flow field of $\sim$16$\times$
lower resolution. We believe that this study brings the field of experimental
fluid dynamics one step closer to the long-term goal of having experimental
measurement systems that can be used for real-time flow field estimation.
