Flow Gym统一了流场量化方法开发与评测流程，提升可复现性。
Flow Gym: A framework for the development, benchmarking, training, and deployment of flow-field quantification methods
- 提供标准化接口，兼容传统与学习型算法
- 支持合成与真实数据，实现离线评测与实时部署
- 基于JAX加速，兼容OpenCV/PyTorch等工具
粒子图像测速（PIV）及类似光学流方法广泛用于流体运动量化，但其开发与评估常受限于软件碎片化、接口不一致和可复现性差。为此，我们提出Flow Gym框架，支持流场量化方法的开发、基准测试、训练与部署，重点聚焦PIV。核心贡献是标准化接口，使经典与基于学习的算法可在统一流程中集成、比较与部署。框架包含JAX实现与现有方法封装、模块化预处理与后处理组件，以及训练与基准测试工具。借助JAX，支持硬件加速执行，同时兼容OpenCV、PyTorch等外部库。可处理合成与实验数据，统一支持离线基准测试与实时部署。旨在提升可复现性，降低方法开发门槛，促进流场量化算法从研究到实验场景的转化。
原文摘要 · Abstract (English)
Particle image velocimetry (PIV) and related optical-flow methods are widely used to quantify fluid motion, but their development and evaluation are often hindered by fragmented software, inconsistent interfaces, and limited reproducibility. To address these challenges, we present Flow Gym, a framework for developing, benchmarking, training, and deploying flow-field quantification methods, with a primary focus on PIV. Its core contribution is a standardized interface that allows classical and learning-based algorithms to be integrated, compared, and deployed within a common pipeline. The framework includes JAX implementations and wrappers for existing methods, modular pre-processing and post-processing components, and utilities for training and benchmarking. By leveraging JAX, Flow Gym supports hardware-accelerated execution while remaining interoperable with external implementations from libraries such as OpenCV and PyTorch. It can operate on both synthetic and experimental data and supports the same workflow for offline benchmarking and real-time deployment. Flow Gym is designed to improve reproducibility, reduce barriers to method development, and facilitate the translation of flow-field quantification algorithms from research to experimental settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。