We present a framework that captures sparse Lagrangian flow information from a volume of real liquid and reconstructs its detailed kinematic information in real time. Our framework can perform flow reconstruction even when the liquid is disturbed by an object of unknown movement and shape. Through a large dataset of liquid moving under external disturbance, an agent is trained using reinforcement learning to reproduce the target flow kinematics with only the captured sparse information as inputs while remaining oblivious to the movement and the shape of the disturbance sources. To ensure that the underlying simulation model faithfully obeys physical reality, we also optimize the viscosity parameters in Smoothed Particle Hydrodynamics (SPH) using classical fluid dynamics knowledge and gradient-based optimization. By quantitatively comparing the reconstruction results against real-world and simulated ground truth, we verified that our reconstruction method is resilient to different agitation patterns.

我们提出了一个框架，可以从大量真实液体中捕获稀疏拉格朗日流信息，并实时重建其详细的运动学信息。即使液体受到未知运动和形状的物体的干扰，我们的框架也可以执行流动重建。通过外部干扰下液体移动的大型数据集，使用强化学习来训练代理，以仅使用捕获的稀疏信息作为输入来重现目标流动运动学，同时保持对干扰源的运动和形状的忽视。为了确保底层模拟模型忠实地遵循物理现实，我们还使用经典流体动力学知识和基于梯度的优化来优化平滑粒子流体动力学（SPH）中的粘度参数。通过将重建结果与现实世界和模拟地面实况进行定量比较，我们验证了我们的重建方法对不同的搅拌模式具有弹性。