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arXiv · 2507.06247

FED-PV: A Large-Scale Synthetic Frame/Event Dataset for Particle-Based Velocimetry

Fan Wu·Xiang Feng·Aoyu Zhang·Yong Lee
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Abstract

Particle-based velocimetry (PV) is a widely used technique for non-invasive flow field measurements in fluid mechanics. Existing PV measurements typically rely on a single type of particle recording. With advancements in deep learning and information fusion, incorporating multiple different particle recordings presents a promising avenue for next-generation PV measurement techniques. However, we argue that the lack of cross-modal datasets -- combining frame-based recordings and event-based recordings -- represents a significant bottleneck in the development of fusion measurement algorithms. To address this critical gap, we developed a dual-modal data generator FED-PV to synthesize frame-based images and event-based recordings of moving particles, resulting in a 350GB dataset generated using our approach. This generator and dataset will facilitate advancements in novel PV algorithms.

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Fan Wu, Xiang Feng, Aoyu Zhang, Yong Lee. 2025-07-01. FED-PV: A Large-Scale Synthetic Frame/Event Dataset for Particle-Based Velocimetry. https://arxiv.org/abs/2507.06247

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