Abstract
Particle Image Velocimetry (PIV) is fundamental to fluid dynamics, yet deep learning applications face significant hurdles. A critical gap exists: the lack of comprehensive evaluation of how diverse optical flow models perform specifically on PIV data, largely due to limitations in available datasets and the absence of a standardized benchmark. This prevents fair comparison and hinders progress. To address this, our primary contribution is a novel, large-scale synthetic PIV benchmark dataset generated from diverse CFD simulations (JHTDB and Blasius). It features unprecedented variety in particle densities, flow velocities, and continuous motion, enabling, for the first time, a standardized and rigorous evaluation of various optical flow and PIV algorithms. Complementing this, we propose Multi Cost Volume PIV (MCFormer), a new deep network architecture leveraging multi-frame temporal information and multiple cost volumes, specifically designed for PIV's sparse nature. Our comprehensive benchmark evaluation, the first of its kind, reveals significant performance variations among adapted optical flow models and demonstrates that MCFormer significantly outperforms existing methods, achieving the lowest overall normalized endpoint error (NEPE). This work provides both a foundational benchmark resource essential for future PIV research and a state-of-the-art method tailored for PIV challenges. We make our benchmark dataset and code publicly available to foster future research in this area.
Abstract (translated)
粒子图像测速法（PIV）在流体动力学中至关重要，然而深度学习的应用却面临重大挑战。一个关键的缺口在于：缺乏对各种光流模型在PIV数据上性能进行综合评估的方法，这主要是由于可用数据集有限以及缺乏标准化基准造成的。这种现状阻碍了公平比较和进步。为了解决这一问题，我们的主要贡献是一个新颖的大规模合成PIV基准数据集，该数据集由多种计算流体动力学（CFD）模拟生成（包括JHTDB和Blasius）。该数据集中包含了前所未有的颗粒密度、流动速度以及连续运动的多样性，首次使得各种光流算法和PIV方法能够进行标准化且严格的评估。此外，我们提出了多成本体积粒子图像测速法（MCFormer），这是一种新的深度网络架构，专门利用多帧时间信息和多个成本体积来应对PIV数据稀疏性的问题。 我们的全面基准测试是同类中的首次尝试，揭示了适应性光流模型之间存在显著的性能差异，并证明了MCFormer在总体归一化端点误差（NEPE）上大幅超越现有方法。这项工作不仅提供了未来PIV研究所需的基础基准资源，还提供了一种针对PIV挑战量身定制的最先进的方法。我们将我们的基准数据集和代码公开发布，以促进该领域的进一步研究。
URL
https://arxiv.org/abs/2507.04750
