# FTGM: Fast Transformer-Based Global Matching for Particle Image Velocimetry
## 4 Experiments
### Qualitative Evaluations
We evaluated our model on real-world experimental PIV turbulent wave channel flow and compared it with prediction results from a high-performance PIV post-processing method, PascalIPIV, and the RAFT256-PIV method. The flow estimation results for PascalIPIV were obtained from [26]. The image size is 2560 \(\times\) 2160 pixels. When processing the full-resolution image using the same patch algorithm, the FTGM method took 43 s and 79 s (once refinement) on an NVIDIA A100, while the RAFT256-PIV took 113 s. Figure 6 illustrates the horizontal and vertical optical flow components estimated by different methods. FTGM showed consistent results with other methods in terms of flow-field structure estimation, whereas FTGM-PIV produced larger errors in magnitude. In contrast, FTGM-PIV-Ref achieved competitive performance after incorporating 1/4 resolution features for refinement, with its estimated results closely aligning with those of RAFT256-PIV and PascalIPV in both horizontal and vertical components. This demonstrates that the introduction of high-resolution features is effective for optimizing the estimation results of FTGM. Figure 4: Visual comparison of vortex pair flow case. The background color represents the motion magnitude, and black arrows indicate the flow direction. The reference result is derived from the ensemble multipass FFT window deformation method in PIVLab and is obtained through bicubic interpolation. The arrows point to the nozzle area. The red rectangular boxes indicate areas with significant differences, and for ease of reference, we have assigned numerical indices to each box. Figure 5: Visual comparison of Karman vortex street case. The reference image is generated using the ensemble multipass FFT window deformation method in PIVLab to produce a sparse flow field, followed by bicubic upsampling to obtain the result. The area enclosed by the red rectangle exhibits significant differences, and for ease of reference, we have assigned numerical indices to each box. The ability of the PIV method to extract the motion of small-scale fluid structures from images, that is, their effective spatial resolution in predicting velocity fields, is one of the important metrics for evaluating PIV algorithms. We conducted tests on the PIV turbulence structure spatial resolution test image. We first present the global prediction results of the velocity magnitude for the full-resolution image (2000 \(\times\) 2000 pixels) in Figure 7, processed using the same patch algorithm. It is evident that the FTGM method produces clear flow-field structures in the resolution image, whereas RAFT256-PIV captures more details within the flow field, missing the overall structure and also exhibiting more high estimates (as indicated by red rectangular 2 frames). Furthermore, we highlight the estimation results in the lower left corner of the image (1000 \(\times\) 1000 pixels) shown in Figure 8, which shows the magnitude of the vertical velocity component. The results demonstrate that our method possesses clear structural representation capabilities. Figure 6: Visual comparison on turbulent wavy channel flow case. A visual comparison between FTGM methods and state-of-the-art PIV method, as well as RAFT256-PIV. The middle column represents the results of the horizontal optical flow component, whereas the right column depicts the predictions of the displacement in the vertical direction. Figure 7: Spatial resolution visualization. The full-size image spatial resolution test visualization results are presented, with the test image size being 2000 \(\times\) 2000 pixels. For ease of comparison, the test cases have been adjusted for brightness and contrast. Both FTGM and RAFT256-PIV (as detailed in [26]) are trained based on PIV Dataset I. The FTGM methods demonstrate clear and sharp structural features. The red rectangular boxes indicate areas with significant differences, and for ease of reference, we have assigned numerical indices to each box.