The need for accurate and non-intrusive flow measurement methods has led to the widespread adoption of Particle Image Velocimetry (PIV), a powerful diagnostic tool in fluid motion estimation. This study investigates the tremendous potential of spike cameras (a type of ultra-high-speed, high-dynamic-range camera) in PIV. We propose a deep learning framework, Spike Imaging Velocimetry (SIV), designed specifically for highly turbulent and intricate flow fields. To aggregate motion features from the spike stream while minimizing information loss, we incorporate a Detail-Preserving Hierarchical Transform (DPHT) module. Additionally, we introduce a Graph Encoder (GE) to extract contextual features from highly complex fluid flows. Furthermore, we present a spike-based PIV dataset, Particle Scenes with Spike and Displacement (PSSD), which provides labeled data for three challenging fluid dynamics scenarios. Our proposed method achieves superior performance compared to existing baseline methods on PSSD. The datasets and our implementation of SIV are open-sourced in the supplementary materials.
View on arXiv@article{zhang2025_2504.18864,
title={ Spike Imaging Velocimetry: Dense Motion Estimation of Fluids Using Spike Cameras },
author={ Yunzhong Zhang and Bo Xiong and You Zhou and Changqing Su and Zhen Cheng and Zhaofei Yu and Xun Cao and Tiejun Huang },
journal={arXiv preprint arXiv:2504.18864},
year={ 2025 }
}