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Implementing Subdivision Surface Theory

April 25, 2000 Article Start Page 1 of 4 Next
 

In my previous article "Subdivision Surface Theory", I explained what subdivision surfaces were and why game developers should be interested in them. I also covered a couple different kinds of subdivision surfaces in their mathematical forms and briefly discussed their benefits and detriments. Most everything was in English, and the rest was expressed using equations. There were no code listings last month, not even a hint of C++, but I promised to discuss an implementation, and so that's the goal of this article. I'll cover a sample implementation of the modified butterfly scheme as discussed in last month's article, complete with a shiny, new demo.

Why the Butterfly?

In "Subdivision Surface Theory" , I wrote about a number of schemes and those were only the tip of the iceberg, so it's worth spending some time justifying the choice I've made for this implementation. Why use the modified butterfly? To explain my reasoning, it helps to look at more general characteristics of schemes and their advantages and disadvantages. The major differences tend to hinge on whether a scheme is approximating or interpolating.

Approximating schemes have a number of benefits. The surfaces they produce are generally very fair, and they are generally the favored schemes for use in high-end animation. For instance, Pixar uses Catmull-Clark surfaces for their character animation. The downside of approximating schemes are substantial, though. The major one is that because the scheme doesn't interpolate its control net, the shape of the limit surface can be difficult to envision from looking at the control net. The caveat is that as the net becomes denser, the surface will generally be closer to the net. But for games, the net itself won't be tens of thousands of polygons, so the surface can differ substantially from the net.

Interpolating schemes are a different story. They can exhibit problems with fairness, with ripples and undulations over the surface, especially near tight joint areas. Also, they aren't used in high-end rendering quite as much, which can mean that they're the focus of less research. But their major benefit is that the surface is substantially easier to envision by looking at the net. Since the surface passes through all the net vertices, it won't "pull away" from the net. The fairness issues are the price to pay for this, though. Approximating schemes are fair because the surface isn't constrained to pass through the net vertices, but interpolating schemes sacrifice the fairness for their interpolation.

Nonetheless, I feel that the fairness issues present less of a challenge to intuition than an approximating surface does. For example, in many cases, existing artwork can be used with interpolating schemes with some minor adjustments to smooth out rippling, whereas adapting existing polygonal art to be a control net for an approximating scheme is a much more difficult task.

Among interpolating schemes, the butterfly scheme has a number of things going for it. It's one of the better-researched schemes. It's also computationally fairly inexpensive. Finally, the results of subdivision tend to look good and conform fairly well to what intuition would expect. Therefore, it's my model of choice.

Butterfly in Review

Figure 1. The stencil used
for the regular case of the modified butterfly scheme.

If you haven't already, you probably should read my article from last month's issue for the deeper explanation of the modified butterfly scheme. But in case you haven't, I'll summarize it here. Given a triangular mesh, the control net, we want to subdivide it one step. We first add a vertex along each edge according to specific rules. If the endpoints of the edge are both of valence 6, then we use the stencil in Figure 1, with the weights:

If one endpoint is of valence 6 and the other is extraordinary (not of valence 6) then we use a special stencil that takes into account just the extraordinary vertex, shown in Figure 2. The weights are computed as follows:

Figure 2. The stencil used for the extraordinary case of the modified butterfly scheme.

If both endpoints are extraordinary, we average the results of using the above extraordinary stencil on each of them. Again, if this seems a bit too terse, refer to last month's article where I discuss the scheme in substantially more detail.

As far as the butterfly scheme's characteristics, it's interpolating because points in a control net also lie on the limit surface - the subdivision process doesn't move existing vertices. It's also triangular as it operates on triangular control nets. It's stationary as it uses the same set of rules every time it subdivides the net, and uniform because every section of the net is subdivided with the same set of rules.

Figure 3. The stencil used for the tangent mask of a regular vertex.

One aspect of the scheme that I mentioned last month but didn't define was the tangent mask of the butterfly scheme. This is the mask used to compute the tangent vectors explicitly at a vertex, which we use to find the vertex normals. The mask is large and therefore may look intimidating, but it's just a bunch of numbers, and a few multiplications and additions later, we've got the answer.

For regular vertices, the process involves the 1- and 2-neighborhood of the vertex (so it uses vertices that are one and two steps away.) Between both neighborhoods, there are 18 vertices, and so the scalars, corresponding to the indexing shown in Figure 3, are:

Multiplying the vertices by l0 and l1 gives us two different tangent vectors, the normalized cross product of which is our normal. For extraordinary vertices the normal is actually easier to find, as it depends only on the 1-neighborhood of the vertex. The two tangent vectors in this case can be found as:

Here, t0 and t1 are the tangents, N is the vertex valence, and ei is the ith neighbor point of the vertex in question, where e0 can be any of the points (it doesn't matter where you start) and the points wind counterclockwise. Crossing the two resulting vectors and normalizing the result produces the vertex normal.


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