3D Particle Field Reconstruction for Tomographic Particle Image Velocimetry Based on a Single Light-Field Camera: A Survey

Three-dimensional (3D) particle field reconstruction is a core procedure of tomographic particle image velocimetry (Tomo-PIV). Its reconstruction accuracy and efficiency directly determine the ability of the PIV system to characterize various complex flow fields. Compared with traditional multicamera Tomo-PIV, a single light-field camera offers a compact layout, simple calibration, and strong adaptability, making it widely applicable for 3D flow measurement in confined space. This paper systematically reviews recent advances in 3D particle field reconstruction algorithms that use a single light-field camera, including both traditional iterative reconstruction methods and deep learning techniques. First, the imaging mechanism of different light-field cameras, the fundamental theory of light-field Tomo-PIV, and the mathematical foundation of tomographic reconstruction are elaborated to establish a theoretical framework for subsequent algorithm analysis. Next, the advantages, disadvantages, and limitations of traditional iterative reconstruction methods and deep learning techniques are comprehensively analyzed from key dimensions, including reconstruction quality, computational efficiency, inherent defects such as particle elongation and ghost particles, and applicable scenarios. On this basis, the current technical bottlenecks are concluded, including low computational efficiency under high particle concentration, insufficient research on velocity uncertainty quantification, domain mismatch between simulated and experimental datasets, and poor interpretability of deep learning models. Finally, several promising future research directions are discussed, such as the optimization of multiframe correlation-based high-precision reconstruction algorithms, the development of standardized open-source datasets, the interpretability of deep neural networks, and time-resolved flow measurement. This study aims to provide a comprehensive algorithmic reference for researchers in the field and facilitate the practical application of light-field Tomo-PIV in engineering fluid mechanics and related disciplines.