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Next: 6.4 Volume Tetrahedralization Up: 6. Architecture and Implementation Previous: 6.2 Initial Point Generation

6.3 Surface Triangulation

Given a set of constraining input facets $I$ (polygonal representation, Fig. 6.11) a surface triangulation is derived. All polygons are triangulated by a recursive splitting algorithm. The polygons may be non-convex, more than two polygons may share an edge, and they do not have to form a closed surface. Depending on the choice of mesh and discretization a refinement algorithm is further applied to either enforce the smallest sphere or the Delaunay criteria (Crit. 5.1, Crit. 5.2, Crit. 3.1, and Crit. 3.2). An important operation is defined to reduce the number of refinement points.

Definition 6.1 (flip-able triangle)   Two triangles $t_{1}, t_{2}$ which are in conjunction convex and which share one common edge $e_{c}$ are said to be flipped if they are replaced by two triangles $\tilde{t}_{1}, \tilde{t}_{2}$ forming the same outline but sharing a different edge $\tilde{e}_{c} \neq
e_{c}$. This operation is extended to non-planar triangles: Let $t_{1}, t_{2}$ be two triangles sharing a common edge $e_{c}$ which is not part of any other triangle $t_{i}, i \neq 1,2 $. If $t_{1}, t_{2}$ form a dihedral angle $\alpha =
180^{\circ} + \epsilon$ where $\vert\epsilon\vert \leq \epsilon_{acc}$ and $\epsilon_{acc}$ is a user controlled accuracy, and if the equatorial sphere of $t_{1}$ contains a vertex of $t_{2}$ which does not belong to $t_{1}$, the triangle $t_{1}$ may be flipped as well and will be called flip-able triangle.

The term flip saturation is introduced. A triangle may not satisfy the smallest sphere criterion and may not be flip-able. The triangle may become flip-able after a certain number of flip operations are applied on other triangles (Fig. 6.4). This situation is said to be unsaturated. It is desired to reach the state of flip saturation before unnecessary refinement occurs. This is achieved by employing the recursive triangle flip.

Figure 6.4: A triangle which is at first not flip-able and the state of flip saturation.
\includegraphics [height=5.5cm]{ppl/flipsat2.eps}

Recursive Triangle Flip
Two triangles $t_{1}, t_{2}$ are flipped. The resulting triangles $\tilde{t}_{1}$ and $\tilde{t}_{2}$ are each checked if they are flip-able. If any of the two triangles $\tilde{t}_{1}, \tilde{t}_{2}$ is flip-able with a triangle $t_{i}$ where $i \notin 1, 2$ it will be flipped as well. Repeat for the flipped triangle $\tilde{t}_{i}$ until no further flip operations are possible.
This operation is performed once on each triangle during a linear scan over all existing triangles. The procedure during which no refinement is allowed is only necessary at the start to ensure flip saturation. Afterwards, during refinement the flip saturation is maintained after insertion of a point by applying the recursive flip locally on the affected triangle only.

It follows a definition of structural and flip-able edges.

Definition 6.2 (flip-able and structural edges)   If two triangles $t_{1}, t_{2}$ sharing an edge $e_{c}$ form a dihedral angle $\alpha = 180^{\circ} +
\gamma$ where $\vert\gamma\vert > \epsilon_{acc}$ and $\epsilon_{acc}$ is a user controlled accuracy, the edge $e_{c}$ is called a structural edge or S-edge. If $\vert\gamma\vert \leq \epsilon_{acc}$ the edge $e_{c}$ is called flip-able edge. If a triangle $t$ possesses an edge $e_{open}$ with no existent adjacent triangle, the edge $e_{open}$ will be called a structural edge as well. If a triangle $t$ possesses an edge $e_{multi}$ with more than one existent adjacent triangles, the edge $e_{multi}$ will be called a structural edge as well.

An example of an edge shared by more than two triangles is shown in Fig. 6.5.

Figure 6.5: Multiple connected edges.
\includegraphics [height=6cm]{ppl/}

Edges with only one adjacent facet are structural and a potential inconsistency. They may indicate an undesired gap in the surface representation. The current implementation allows to repair such faults by tracing these edges and linking them to construct a three-dimensional ring. If this ring consists of a large number of edges, it can be assumed that the boundary does not form a closed surface intentionally and no action will be taken. On the other hand if the ring is relatively small, it can be assumed that a hole in the surface has been detected. The hole can be patched by a non-planar triangulation of the ring. This is performed by the general polygon triangulation algorithm which was used for the facets of the input $I$.

Note that two triangles need not be flip-able if they share a flip-able edge (Fig. 6.6).

Figure 6.6: Non-convex coplanar triangles. The common edge is by definition flip-able, while the triangles are not.
\includegraphics [height=4cm]{ppl/}

All S-edges are detected and stored in a special data structure for further processing. The smallest sphere criterion will be enforced for S-edges by a novel refinement scheme presented in this section (Fig. 6.7). The idea is to avoid overrefinement due to a feedback situation where an inserted point causes further point insertions for other S-edges. This is especially important when incident S-edges span angles of less than $90^{\circ}$. The S-edges are drawn in bold, the dashed circles illustrate the minimal edge length up to which refinement is permitted, and the solid circles symbolize sphere tests. There are two possible types how a refinement point is derived from points which inflict the sphere criterion. Such points will be called disturbing points.

Type P Insertion
An orthogonal projection of the disturbing point onto the S-edge is used.
Type R Insertion
The disturbing point is rotated onto the S-edge. The rotation axis passes through the point at which the two S-edges are incident.
If more than one disturbing point exists, a best candidate is selected by comparing the relative distance and choosing the closest. The simple cases are P I and R I. In P II, R II, and R IV the minimal edge length is limited and the inserted point receives an offset. If the ratio between the length of the two S-edges is close to one, the most complex situation case R III evolves. An offset as in case R IV would not produce a good result and the sphere test depicted by the left solid circle in case R IV could fail due to the inserted point. An overall improved situation results from an intended first order feedback where actually two points will be inserted in two consecutive steps R III + R I. The circle for the sphere test which causes the insertion in the first place is omitted in all sketches. Once the location of the new point has been determined and prior to its insertion, sphere tests are performed for some of the new edges to avoid feedback (solid circles).

Figure 6.7: Refinement types for structural edges.
\includegraphics [width=0.9\textwidth]{ppl/refsedge2.eps}

Figure 6.8: Refining structural edges for the trivial case of a planar polygon.
\includegraphics [width=0.9\textwidth]{ppl/pedex2.eps}

Note that the algorithm is designed for three dimensions in spite of the two-dimensional sketches. Figure 6.8 shows the result for the trivial case of a planar polygon. The S-edges (outline) satisfy the smallest sphere criterion after refinement.

The triangles are processed after the S-edges. If desirable, the refinement can be reduced by omitting the smallest sphere criterion for triangles (Crit. 3.1). The weaker Delaunay criterion (Crit. 5.2) cannot be checked easily, because the number of spheres to test can theoretically be infinite as the criterion requires a sphere of any size to be empty. Hence, the insphere test would have to be repeated for different sizes until an empty sphere has been found. An equivalent criterion was devised which needs at most two insphere tests. It uses a metric $\lambda $ which will be described later on in Section 6.4.

Criterion 6.1 (double sphere)   Let $P$ be a finite set of points in three-dimensional space $R^{3}$ and let $t$ be a boundary triangle with a non-empty smallest sphere $S_{\min}$. The point $p_{k,\lambda_{\min}}$ is contained in $S_{\min}$ and minimizes a metric $\lambda = \vec{HM_{k}} \cdot
\vec{n}$. The triangle $t$ is a Delaunay triangle if and only if the sphere $S_{double}$ defined by $t$ and $p_{k,\lambda_{\min}}$ contains no other point in $P$.

A non-Delaunay triangle and a Delaunay triangle with two disturbing points located inside of the smallest sphere and the empty double sphere are depicted in Fig. 6.9.

Figure 6.9: Double sphere criterion.
\includegraphics [width=0.36\textwidth]{ppl/doublesph.eps}

A key idea for Steiner point refinement with provable bounds was discussed in Section 5.6. The circumcenter of a triangle is a very well suited location for a new point to be inserted. The triangle which will actually be split can be different from the triangle which causes the refinement. Hence, a triangle search by e.g. a jump and walk algorithm is required. An algorithm was implemented which extends this concept for non-planar surfaces (Fig. 6.10).

Figure 6.10: Locating the triangle which contains the projection of the circumcenter and flipping of the non-structural edge.
\includegraphics [width=0.36\textwidth]{ppl/steiner25d.eps}

The location of the new point $S$ is derived from the circumcenter $M$ by an orthogonal projection. $S$ is the point $M$ projected onto the surface ( $S = M_{proj}$ ). It is checked if the refinement point $S$ really violates the sphere criterion of the bad triangle. The point $S$ might fall outside of the sphere in which case a Steiner point refinement would not be justified, because the bad triangle would survive the Delaunay update step. A point location is necessary to find the triangle containing $S$ for a given $M$. The direction of projection is orthogonal to the plane spanned by the bad triangle. The triangle search can be performed by a walk algorithm.
Locate Triangle
Starting with a triangle $t = t_{bad}$ and a given point $P$ a triangle is searched for which contains $P_{proj}$. (Usually $P$ is the circumcenter $M$.) The direction of projection is defined by the normal vector $\vec{n}_{bad}$ of $t_{bad}$. An edge $e$ of $t$ and $\vec{n}_{bad}$ define a plane which separates two half spaces. The edge $e$ is said to face the point $P$, if $P$ and $t$ do not lie in the same half space. Determine one edge $e$ of $t$ which faces $P$ and which is a flip-able edge (Def. 6.2). Follow edge $e$ by finding the adjacent triangle $t_{adj}$. Repeat for $t = t_{adj}$ until an S-edge has been encountered or until the triangle $t$ possesses no edge $e$ which faces $P$. In either case the search is terminates. In the latter case $t$ contains $P_{proj}$. The Steiner point $S = P_{proj}$ on $t$ is calculated by intersecting the found triangle $t$ with the line defined by $P$ and the direction of projection.
The point $P$ was used in the description to show that the algorithm can search for arbitrary points where $P \neq M$. This is important when Steiner points are derived from disturbing points instead of circumcenters.

If the triangle is not extremely obtuse, $M$ will be contained in the triangle itself or in an adjacent triangle. Then, no iterations or only a few will be required. If an S-edge is encountered the triangle will not be refined. Instead the S-edge will be refined as described above. This is important for several reasons. Insertion of $S$ is usually followed by triangle flip operations which eliminate the bad triangle. Hence, the edge to follow must be flip-able. Also, it is of limited sense to project $M$ onto the surface for regions with sharp dihedral angles.

Originally, this type of refinement should only be applied to Delaunay triangles with an empty circumsphere to improve quality angle criteria. If the circumsphere is not empty, points could be inserted too close to each other. In the present case this is not necessary. In fact the Steiner point refinement of non-Delaunay triangles proves successful for enforcing the Delaunay criterion as long as the state of flip saturation is guaranteed and maintained after each point insertion.

At the same time it is possible to define minimum angle and maximum area criteria upon which Delaunay surface triangles are adapted by the insertion of Steiner points. These can be applied inhomogeneously according to a control function. For the example in Fig. 6.11 the structural edges are shown in Fig. 6.12, the Delaunay surface triangulation in Fig. 6.13, and the adapted surface mesh in Fig. 6.14.

Figure 6.11: Polygonal boundary description of a MOS Transistor with a spacer.
\includegraphics [width=0.4\textwidth]{ppl/inputtrans.eps}

Figure 6.12: Structural edges of the MOS transistor example.
\includegraphics [width=0.4\textwidth]{ppl/}

Figure 6.13: Delaunay surface triangulation.
\includegraphics [width=0.4\textwidth]{ppl/delsurf.eps}

Figure 6.14: Adapted surface mesh after Steiner point insertions.
\includegraphics [width=0.4\textwidth]{ppl/finalgrid.eps}

next up previous contents
Next: 6.4 Volume Tetrahedralization Up: 6. Architecture and Implementation Previous: 6.2 Initial Point Generation
Peter Fleischmann