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The three-dimensional particle image velocimetry (3D PIV) technique, as a non-intrusive method for three-dimensional full-field velocity measurement, has garnered extensive utilization across diverse domains including biomimetic dynamics, combustion diagnostics, and the structural design of aerospace equipment. Synthetic Aperture Particle Image Velocimetry (SAPIV), based on camera arrays, digitally merges images obtained from different perspectives to simulate the imaging effects of large-aperture cameras. This approach allows for large-scale, high-resolution flow field measurements. In this study, the three-dimensional intensity characteristics of particles within sequences of refocused images are investigated. Leveraging the spatial distribution patterns of grayscale information for individual focused particles, we designed a three-dimensional convolutional neural network (3DCNN) capable of extracting focused particle positions. Throughout the particle extraction procedure, this three-dimensional CNN network systematically analyzes the sequence of refocused images and subsequently derives both particle positions and grayscale information for focused particles based on their distinctive characteristics. The tracer particle field in simulated experiments were reconstructed and the reconstruction quality was evaluated. The results demonstrate the high precision of our proposed method in reconstructing three-dimensional tracer particle information in SAPIV.
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