Flow visualization technologies such as particle tracking velocimetry (PTV) are broadly used in understanding the all-pervasiveness three-dimensional (3D) turbulent flow from nature and industrial processes. Despite the advances in 3D acquisition techniques, the developed motion estimation algorithms in particle tracking remain great challenges of large particle displacements, dense particle distributions and high computational cost. By introducing a novel deep neural network based on recurrent Graph Optimal Transport, called GotFlow3D, we present an end-to-end solution to learn the 3D fluid flow motion from double-frame particle sets. The proposed network constructs two graphs in the geometric and feature space and further enriches the original particle representations with the fused intrinsic and extrinsic features learnt from a graph neural network. The extracted deep features are subsequently utilized to make optimal transport plans indicating the correspondences of particle pairs, which are then iteratively and adaptively retrieved to guide the recurrent flow learning. Experimental evaluations, including assessments on numerical experiments and validations on real-world experiments, demonstrate that the proposed GotFlow3D achieves state-of-the-art performance against both recently-developed scene flow learners and particle tracking algorithms, with impressive accuracy, robustness and generalization ability, which can provide deeper insight into the complex dynamics of broad physical and biological systems.
翻译:尽管在3D获取技术方面有所进步,但粒子跟踪的发达运动估计算法仍然是大型粒子转移、稠密粒粒分布和高计算成本的巨大挑战。通过采用基于经常的“最佳迁移”图(GotFlow3D)的新型深神经网络,我们提出了一个端到端的解决方案,以学习双框架粒子组的3D流流动。拟议的网络在几何空间和地貌空间中构造两张图表,进一步丰富原始粒子的外形图,从一个图形神经网络中学习的精密内在和外部特征。随后,挖掘的深层特征被用于制定最佳的运输计划,表明粒子配的对应关系,然后通过迭接和适应性回收来指导经常性流学。实验性评估,包括对数字实验和对现实世界实验的验证。拟议的GotFlow3D在地貌空间和地貌空间中建立了两张图图图图图图图图图图图图图图图图图,进一步丰富了原始粒子颗粒图的图示,并用从一个图形神经网络网络网络网状的内在和外外线外外外外图,从而提供最新的精确的精确的跟踪和深层次分析。