# FTGM: Fast Transformer-Based Global Matching for Particle Image Velocimetry
## 4 Experiments  
### Overview of Datasets  
PIV Dataset I--involves extensive public PIV data. These datasets originate from a publicly accessible database that serves as a benchmark for PIV algorithms [22]. The dataset includes five both well-known and realistic flow scenarios, such as backward stepping flow (backstep), vortex shedding over a circular cylinder (cylinder), 2D turbulent flow motion from direct numerical simulation (termed DNS turbulence), surface quasi-geostrophic (SQG) model of sea flow, and different flows from the Johns Hopkins Turbulence Database (JHTDB). The particle images are artificially synthesized with a displacement range of approximately \(\pm 10\) pixels. The dataset is distinguished by a high seeding density and a high signal-to-noise ratio (SNR), indicating nearly ideal experimental conditions.  
PIV Dataset II--encompassing a vast array of flow conditions to bolster model training and parameter optimization--enhances model generalization through its rich and extensive compilation of scenarios. The dataset cited in [25] features a displacement range of \(\pm 10\) pixels, with new particle images synthesized to include vortices, point sources, point sinks, dipole flow fields, and square cavity flows. To emulate real-world fluid dynamics, these images incorporate Gaussian noise of diverse magnitudes and random spatial distributions of light intensity.  
PIV Dataset III--featuring datasets with real-world constraints--reflects the practical challenges of PIV equipment [26], where both internal and external noise sources make it nearly impossible to obtain high-quality images as seen in PIV Datasets I and II. This dataset simulates image acquisition from actual devices, similar to Dataset I, but with a particle displacement range of \(\pm 24\) pixels, reduced particle density, lowered signal-to-noise ratio, and added camera noise.  
PIV Dataset IV--advancing to real-world test scenarios--further evaluates generalization capabilities through predictions on synthetic data and experimental test cases beyond the confines of the training set, which include the following:  
* Realistic vortex pair flow images [39], used to study the interaction between flow vortex structures. The double-helical vortices are generated by the nozzle, which has a relatively stable flow field with fine structures near the nozzle position, making it highly suitable for PIV analysis with particle images of good imaging quality.
* Actual Karman vortex street images [40], which form a series of alternating vortices downstream of an obstacle (such as a cylinder) when a fluid flows past it. This flow field is a classic phenomenon in fluid dynamics, often used to study fluid separation, vortex shedding, and turbulence characteristics. The image, sourced from PIVLab [41], is used to demonstrate how the PIV method can be employed to analyze and visualize the flow field of the Karman vortex street.  
* Spatial resolution images. The test case originates from Group A-4 of the public data of the Third International PIV Fluid Measurement Conference [42], specifically for evaluating the spatial resolution and the fine measurement performance of vortex structures using test algorithms.
* Turbulent wave channel flow--characterized by the presence of favorable and adverse pressure gradients [26], which are crucial for understanding the evolution of turbulent scales under the influence of local pressure gradients. By evaluating PIV optical flow models within this flow field, we can assess the performance of the methods in handling complex flow patterns, such as flow separation, reattachment, and periodic vortex shedding.