FED-PV: A Large-Scale Synthetic Frame/Event Dataset for Particle-Based Velocimetry
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913932649693184 |
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| author | Wu, Fan Feng, Xiang Zhang, Aoyu Lee, Yong |
| author_facet | Wu, Fan Feng, Xiang Zhang, Aoyu Lee, Yong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06247 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FED-PV: A Large-Scale Synthetic Frame/Event Dataset for Particle-Based Velocimetry Wu, Fan Feng, Xiang Zhang, Aoyu Lee, Yong Fluid Dynamics Instrumentation and Detectors 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. |
| title | FED-PV: A Large-Scale Synthetic Frame/Event Dataset for Particle-Based Velocimetry |
| topic | Fluid Dynamics Instrumentation and Detectors |
| url | https://arxiv.org/abs/2507.06247 |