Data structures

Consider listing all of the triangles in a file or memory array. If the triangles form a mesh, then most or all vertices will be shared among multiple triangles. This is clearly a waste of space. Another issue is that we will frequently want to perform operations on the model. For example, after moving an object, can we determine whether it is in collision with another object (covered in Section 8.3)? A typical low-level task might be to determine which triangles share a common vertex or edge with a given triangle. This might require linearly searching through the triangle list to determine whether they share a vertex or two. If there are millions of triangles, which is not uncommon, then it would cost too much to perform this operation repeatedly.

Figure 3.3: Part of a doubly connected edge list is shown here for a face that has five edges on its boundary. Each half-edge structure $ e$ stores pointers to the next and previous edges along the face boundary. It also stores a pointer to its twin half-edge, which is part of the boundary of the adjacent face. (Figure from Wikipedia user Accountalive).
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For these reasons and more, geometric models are usually encoded in clever data structures. The choice of the data structure should depend on which operations will be performed on the model. One of the most useful and common is the doubly connected edge list, also known as half-edge data structure [54,224]. See Figure 3.3. In this and similar data structures, there are three kinds of data elements: faces, edges, and vertices. These represent two, one, and zero-dimensional parts, respectively, of the model. In our case, every face element represents a triangle. Each edge represents the border of one or two triangles, without duplication. Each vertex is shared between one or more triangles, again without duplication. The data structure contains pointers between adjacent faces, edges, and vertices so that algorithms can quickly traverse the model components in a way that corresponds to how they are connected together.

Steven M LaValle 2016-12-31