Abstract:

This paper presents a seed placement strategy for streamlines based on flow features in the data set. The primary goal of our seeding strategy is to capture flow patterns in the vicinity of critical points in the flow field, even as the density of streamlines is reduced. Secondary goals are to place streamlines such that there is sufficient coverage in non-critical regions, and to vary the streamline placements and lengths so that the overall presentation is aesthetically pleasing (avoid clustering of streamlines, avoid sharp discontinuities across several streamlines, etc.). The procedure is straight forward and non-iterative. First, critical points are identified. Next, the flow field is segmented into regions, each containing a single critical point. The critical point in each region is then seeded with a template depending on the type of critical point. Finally, additional seed points are randomly distributed around the field using a Poisson disk distribution to minimize closely spaced seed points. The main advantage of this approach is that it does not miss the features around critical points. Since the strategy is not image-guided, and hence not view dependent, significant savings are possible when examining flow fields from different viewpoints, especially for 3D flow fields.

Paper:

Images:

Comparison of flow-based streamline seeding with regular seeding and image based streamline placement method. Left: regular seeding; Middle: image-based streamline placement; Right: flow-based seeding.


Different types of critical points possible in 2D.


Seed templates for various critical points. The bold dots represent the seed template and the dashed lines are the streamlines traced from the template. Left: center, spiral; Middle: source, sink; Right: saddle.


A flow field can be partitioned using Voronoi diagram of the critical point locations. Each Voronoi region contains only one critical point that is representative of the flow in that region.


Seeds placed using templates for various critical points. Left: dynamic vortices. Right: 5-cp.


Regions of influence for each critical point are shown as yellow disks.




Comparison of our flow-based streamline seeding method with Turk and Banks's image based streamline placement strategy. Left: images generated using image-guided streamline placement. Right: images generated using our flow-based seed placement strategy. The streamline separation is 1% for images in the first row, 1.67% for images in the the second row, and 3% for images in the third row.
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