Three-dimensional particle reconstruction method based on latent space back projection

Three-dimensional particle field reconstruction serves as the foundation for three-dimensional velocity measurements in turbulent flows, and its accuracy directly determines the measurement reliability of particle image velocimetry systems. To address the typical limitations of conventional algebraic reconstruction techniques—such as computational inefficiency, poor adaptability to high-concentration fields, limited noise resistance, and particle morphology distortion—this study proposes the latent space back-projection particle reconstruction network. The framework establishes an implicit spatial mapping mechanism, employing differentiable projection operators to construct a nonlinear relationship between two-dimensional multi-view image feature tensors and three-dimensional implicit tensor space, thereby achieving end-to-end voxel feature reconstruction. Experimental validation demonstrates that the proposed algorithm significantly outperforms traditional algebraic reconstruction techniques in reconstruction fidelity on standard test datasets, with its depth-wise intensity distribution exhibiting superior Gaussian statistical characteristics. By leveraging shared encoding and an implicit space projection mechanism, the framework achieves single-pass reconstruction of 2083 voxels, substantially reducing graphics processing unit memory usage. This provides an efficient and reliable voxel reconstruction solution for three-dimensional measurements in complex flow fields.