A calibration-informed deep learning model for three-dimensional particle reconstruction of volumetric particle image velocimetry

Accurately reconstructing three-dimensional particle fields is essential in fluid velocity measurement research. This study addresses the limitations of current three-dimensional (3D) particle reconstruction methods, such as computational efficiency, precision at high particle density, and particle morphology issues, by introducing a calibration-informed deep learning model named the calibrated pixel to voxel convolutional neural network (CPV-CNN) for 3D Particle Reconstruction. This innovative neural network framework employs a unique Gaussian attention mechanism that bridges pixels and voxels, enabling the mapping of pixel features from two-dimensional (2D) particle images to 3D voxel features. This approach eliminates the need for an initial particle field for particle reconstruction, while significantly enhancing reconstruction efficiency. Additionally, the neural network incorporates camera calibration parameters and the physical coordinates of the reconstructed domain, thereby improving the model's generalization capability and flexibility. Numerical experiments demonstrate that CPV-CNN delivers superior results in terms of accuracy, generalization, and robustness in 3D particle reconstruction. The reconstructed particles exhibit favorable morphology, without the elongation issues commonly observed with conventional methods. This achievement illustrates a practical particle reconstruction algorithm based on artificial intelligence (AI) techniques and represents an important step toward developing an end-to-end AI-based particle reconstruction method in the future.