Streamline Selection Algorithm for Three-Dimensional Flow Fields

This paper addresses the well known occlusion and cluttering issue effectively in the visualization of 3D flow fields using streamlines.A streamline selection algorithm based on iterative closest point(ICP) and K-means clustering is presented to show a clear flow field pattern and the important flow features.The ICP method registers shape and ranks streamlines,and then the ranking values are used to cluster the initial streamlines.Finally,the similar streamlines in the same cluster are pruned according to the density defined arbitrarily by the user.An effectiveness estimation function is also introduced to evaluate the accuracy of the presented algorithm with the reconstruction of the vector field.Compared with previous work in uniform streamline placement,comprehensive experiments conducted on multiple datasets show that the proposed algorithm can better reflect the underlying properties of the flow field and greatly improve the readability of 3D flow field datasets.