Particle Image Velocimetry (PIV) is a widely adopted non-invasive imaging technique that tracks the motion of tracer particles across image sequences to capture the velocity distribution of fluid flows. It is commonly employed to analyze complex flow structures and validate numerical simulations. This study explores the untapped potential of spike cameras--ultra-high-speed, high-dynamic-range vision sensors--in high-speed fluid velocimetry. We propose a deep learning framework, Spike Imaging Velocimetry (SIV), tailored for high-resolution fluid motion estimation. To enhance the network's performance, we design three novel modules specifically adapted to the characteristics of fluid dynamics and spike streams: the Detail-Preserving Hierarchical Transform (DPHT), the Graph Encoder (GE), and the Multi-scale Velocity Refinement (MSVR). Furthermore, we introduce a spike-based PIV dataset, Particle Scenes with Spike and Displacement (PSSD), which contains labeled samples from three representative fluid-dynamics scenarios: steady turbulence, high-speed flow, and high-dynamic-range conditions. Our proposed method outperforms existing baselines across all these scenarios, demonstrating its effectiveness.

翻译：粒子图像测速法（PIV）是一种广泛采用的非侵入式成像技术，通过追踪示踪粒子在图像序列中的运动来捕捉流体流动的速度分布。该方法常用于分析复杂流动结构及验证数值模拟。本研究探索了脉冲相机——一种超高速、高动态范围的视觉传感器——在高速流体测速领域的未开发潜力。我们提出了一种深度学习框架：脉冲成像测速法（SIV），专为高分辨率流体运动估计而设计。为提升网络性能，我们针对流体动力学与脉冲流特性设计了三个创新模块：细节保持分层变换（DPHT）、图编码器（GE）和多尺度速度优化（MSVR）。此外，我们构建了基于脉冲的PIV数据集——含脉冲与位移的粒子场景（PSSD），其中包含三种典型流体动力学场景的标注样本：稳态湍流、高速流动和高动态范围条件。我们提出的方法在所有场景中均优于现有基线模型，验证了其有效性。