vtkDelaunay3D - create 3D Delaunay triangulation of input points

Super Class: vtkPointSetFilter

Description:

vtkDelaunay3D is a filter that constructs a 3D Delaunay triangulation from a list of input points. These points may be represented by any dataset of type vtkPointSet and subclasses. The output of the filter is an unstructured grid dataset. Usually the output is a tetrahedral mesh, but if a non-zero alpha distance value is specified (called the "alpha" value), then only tetrahedra, triangles, edges, and vertices lying within the alpha radius are output. In other words, non-zero alpha values may result in arbitrary combinations of tetrahedra, triangles, lines, and vertices. (The notion of alpha value is derived from Edelsbrunner's work on "alpha shapes".) The 3D Delaunay triangulation is defined as the triangulation that satisfies the Delaunay criterion for n-dimensional simplexes (in this case n=3 and the simplexes are tetrahedra). This criterion states that a circumsphere of each simplex in a triangulation contains only the n+1 defining points of the simplex. (See text for more information.) While in two dimensions this translates into an "optimal" triangulation, this is not true in 3D, since a measurement for optimality in 3D is not agreed on. Delaunay triangulations are used to build topological structures from unorganized (or unstructured) points. The input to this filter is a list of points specified in 3D. (If you wish to create 2D triangulations see vtkDelaunay2D.) The output is an unstructured grid. The Delaunay triangulation can be numerically sensitive. To prevent problems, try to avoid injecting points that will result in triangles with bad aspect ratios (1000:1 or greater). In practice this means inserting points that are "widely dispersed", and enables smooth transition of triangle sizes throughout the mesh. (You may even want to add extra points to create a better point distribution.) If numerical problems are present, you will see a warning message to this effect at the end of the triangulation process.

Caveats:

Points arranged on a regular lattice (termed degenerate cases) can be triangulated in more than one way (at least according to the Delaunay criterion). The choice of triangulation (as implemented by this algorithm) depends on the order of the input points. The first four points will form a tetrahedron; other degenerate points (relative to this initial tetrahedron) will not break it. Points that are coincident (or nearly so) may be discarded by the algorithm. This is because the Delaunay triangulation requires unique input points. You can control the definition of coincidence with the "Tolerance" instance variable. The output of the Delaunay triangulation is supposedly a convex hull. In certain cases this implementation may not generate the convex hull. This behavior can be controlled by the Offset instance variable. Offset is a multiplier used to control the size of the initial triangulation. The larger the offset value, the more likely you will generate a convex hull; and the more likely you are to see numerical problems. The implementation of this algorithm varies from the 2D Delaunay algorithm (i.e., vtkDelaunay2D) in an important way. When points are injected into the triangulation, the search for the enclosing tetrahedron is quite different. In the 3D case, the closest previously inserted point point is found, and then the connected tetrahedra are searched to find the containing one. (In 2D, a "walk" towards the enclosing triangle is performed.) If the triangulation is Delaunay, then an

See Also:

vtkDelaunay2D vtkGaussianSplatter vtkUnstructuredGrid

Methods:

Detailed Method Descriptions: