Next message: generate contour from point data.generate contour from point data Eduardo Schott Verdugo maschotte at This Node editor is only available in the nightly version of ParaView and needs to be enabled using the Manage Plugins… menu that we saw earlier.Generate contour from point data We may have a look at the recently added Node editor to replace / complement the original Pipeline browser. Figure 9: TTK Manifold checker, non manifold vertices, edges and faces are shown in red.Īs a side note, using TTK often involves large pipelines. Its use is highlighted in this example, and illustrated in Figure 9. This can be done using the TTK Manifold checker filter. For the moment, you also need to check if your data is manifold. For the simplicial complex part, the Thetrahedralize and Clean to Grid filters should be enough to ensure you only have valid cells. When using TTK, as explained in Section 2, you need to check that your data forms a manifold simplicial complex. Figure 8 presents another example where we can distinguish phalanges. If you want to change the coarseness of the extracted feature, you can play with the “Lower Threshold” of the Persistence Threshold filter in the Pipeline. You can find a more detailed version of the pipeline in the tutorial that was given at IEEE VIS 2020: (time-code 01:38:45) and in the example website. Finally, we use this segmentation to extract areas attached to maxima. With the TTK FTM Tree filter, we compute the contour tree along with the corresponding segmentation (cf. Then, the filtered diagram is used to configure a topological simplification of the initial domain, resulting in a new version where the noise has been removed. With a threshold on the persistence, we filter this diagram to only keep the most prominent features (see the Properties panel on the left of Figure 7). On this diagram, each bar corresponds to one feature and the persistence (the height) of the bar is a measure of the robustness of this feature. In the first part of the pipeline, we use a topological abstraction named the persistence diagram to extract the data set features. Due to small fluctuations, especially in the air, this dataset is noisy. Points with the highest density correspond to bones, and the lowest density is the air around the foot. This state file reads the dataset ctBones.vti, containing a human foot scan with the density of matter represented as point scalars. For a more complete introduction to Topological Data Analysis, you can find this IEEE Vis 2020 Tutorial: (Introduction starts at 00:06:55).įigure 7: ctBones.pvsm, segmentation based on region of maximum density (bones) on a CT scan using TTK. It is for example possible to hierarchically simplify the scalar field using the relationship emphasized in Figure 6. The toolkit also comes with filters to work with these abstractions. the relationship between various scalar fields (Continuous scatter-plot).the temporal evolution of features (Planar Graph Layout).feature robustness and noise amount (Persistence Diagram / Chart).the slope/gradient (Morse-Smale Complex). ![]() TTK contains other kinds of topological abstractions that may be used when studying: Due to its hierarchical nature, this tree also embeds a relationship between features, which can be used, for example, for noise removal. These regions merge together at internal nodes in the tree. ![]() ![]() In this example, we can see how each minima / maxima at the end of the fingers correspond to a leaf arc and its corresponding region. ![]() Figure 6: (left) 2-simplicial complex of a hand with an elevation scalar field and (right) the corresponding contour tree with its segmentation
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