have been quite a few articles, web sites, and books published in the
past few years that talk about generating game worlds procedurally.
As game worlds have become larger and more complex, it has become less
practical for game designers to generate every detail by hand. Mathematical
procedures of all sorts have been around for quite a while to generate
different types of texture maps, terrain, and 3D objects. If you want
an excellent example of procedural modeling and texturing in action,
take a look at Terragen.
For a good example of procedural textures being generated real-time
in a game engine, check out the manual for the animated textures used
been interested in procedural texturing and modeling for even half the
amount of time I have, you've probably read many of the same articles
and web sites I've read. You may have even bought a book or two and
written some code. If you're like me, you've also probably been frustrated
by how much time it takes to sift through all the information out there
to figure out what really works. Although this article won't solve all
your problems or answer all your questions, hopefully it will get you
much further along the path than you are now, or at least give you plenty
is the first part in a series that will explain how to procedurally
generate an entire universe and render it real-time at any level of
detail in a game engine. Here is what's planned for the first few articles
in the series:
C++ demo with source will be provided that generates and renders an
Earth-sized planet with an Earth-sized moon revolving around it. The
rendering is done using OpenGL, but it can be easily ported to Direct3D
or another graphics library of your choice. I am publishing the source
under the GNU Lesser General Public License, and it is being used in
glElite, an open-source
OpenGL version of a popular 1980s game called Elite, which is being
written by Timo Suoranta.
Static vs. Dynamic Algorithms
most procedural routines I've found for generating terrain deal only
with flat terrain. Few game designers work with a world large enough
to need a sphere. If you see a flat algorithm you really like, you might
need to use some creativity to map it to a sphere without causing any
distortion. A good example of this can be found in Hugo Elias's article
Landscapes. The original algorithm generated random "fault
lines" on a flat map, randomly raising or lowering the land on
either side. The spherical version generates random planes that cut
through the sphere, then randomly raises or lowers the land on either
side. It's an interesting technique, and the pictures of it look pretty
line (or plane) algorithm is an example of what I call a "static"
algorithm. A static algorithm is one that generates a static mesh of
vertices or height values. The most common form generates a 2D bitmap
of height values, then uses the bitmap to build a triangle mesh for
a flat landscape. While some static algorithms are very good, they all
suffer from two crippling problems. The first is that you need a ridiculously
large mesh to get any detail for a full-size planetary body. The second
is that the speed of the algorithm depends on the size of the mesh.
you an idea of the size we're talking about with planetary bodies, an
order-10 sphere based on an icosahedron (20 * 410 triangles) has almost
21 million triangles in it. For a planet the size of Earth, the triangle
edges would be 300km long. The edges for an order-20 sphere (22 trillion
triangles) would be 0.3km long. It would be ridiculously slow, take
up an enormous amount of storage space, and still wouldn't be able to
show you details on individual hills or mountains. In that light, it
seems that the only feasible way to generate a planet is to come up
with something more dynamic.
of a "dynamic" algorithm is an algorithm that takes some sort
of vertex coordinates as parameters and returns the proper height value
at those coordinates. These coordinates could be longitude and latitude,
polar coordinates, 3D coordinates, or anything else you can think of
that consistently and uniquely identifies a vertex. Paired with an adapting
mesh algorithm such as the one that will be explained in Part 2 of this
series, you would not need to store anything in memory or on disk except
for the mesh you need to draw for that frame. The ideal algorithm would
be initialized with a random seed and a number of defining parameters
that would allow you to create a range of interesting planetary bodies
from one algorithm.
coming up with a dynamic algorithm like this which works well is difficult.
Coming up with one that's fast is even more difficult. You could, for
instance, be creative and make the fault plane algorithm into a dynamic
algorithm. Instead of pregenerating a mesh, you could store the planes
that cut through the sphere and use them to determine the proper height
for any requested vertex. You will run into two problems doing this.
The first problem will be that you need a lot of planes to get detail
on a full-size planet. What looks good from a distance would leave noticeable
lines close-up. That will lead you to the second problem, which is performance.
If you need 100,000 fault planes to get good detail, you will have to
run each requested vertex through a loop 100,000 times.
thing to note before I move on is that it's also possible combine static
and dynamic algorithms any way you want. A dynamic algorithm can be
seeded with height values created by hand or by a static algorithm.
Your requirements will help determine how much of one or the other to
use, but keep in mind the size limitations with predefined meshes and
static algorithms. If you need to be able to generate an entire planet
down to a very detailed level, a dynamic algorithm needs to take over
somewhere to fill in the gaps.
My First Attempt:
The Plasma Algorithm
ever read anything about procedural terrains, you've probably read about
the plasma algorithm. If you've done any coding, you probably started
with this algorithm. The basic concept is to start with a single square
and subdivide it recursively into smaller squares by adding new vertices
to the mesh. The height of each new vertex is offset up or down by a
random amount, and the maximum offset allowed is reduced with each level
of recursion. This algorithm produces some fairly interesting mountainous
terrain, but that's all it gives you. There is no flat land anywhere
the plasma algorithm is usually implemented as a static algorithm, my
first dynamic planet-generating algorithm was based on it. Instead of
passing any kind of spatial coordinates to a truly dynamic function,
I gave each triangle a random seed to identify it. Using the same random
seed every time I split a specific triangle made it give you the same
height value every time for the same vertex, so I used a triangle's
seed to generate the seeds for its children. As long as I maintained
the seed for each triangle properly, the algorithm produced consistent
results. Once I coded it and got it all working, my first dynamic Earth-sized
planet came to life on the screen.
the camera position close to the ground, and I got very excited as I
flew over shaded mountains and valleys. I moved the camera all over
the planet to see how it looked. Then I set up a simple 1D texture to
get a better feel for the height values I was seeing. Half of it was
ocean blue to indicate the water level, and the other half went from
yellow to green to white with increasing height; the texture coordinate
of each vertex was determined by its height.
burst as soon as I saw the planet in color. It's hard to tell whether
it looked more like thousands of little mountainous islands or thousands
of little lakes sandwiched between all the mountains. Needless to say,
it looked nothing at all like natural terrain. I tried a number of things
to salvage the plasma algorithm. It is a very fast algorithm, and I
feared that more complex algorithms would run too slow to be real-time.
However, no matter what I tried with the plasma algorithm, my planet
still looked terrible.
A True Dynamic
Algorithm: Perlin Noise
learned about Perlin noise. Perlin noise is a very popular noise function
written by Ken Perlin. To put it simply, it generates smoothed random
noise using a lattice of random numbers. The lattice is an n-dimensional
array of floats, and the noise function takes an n-dimensional vector
as input. Think of the lattice as an n-dimensional space and the input
as a point in that space. Because arrays must be accessed using integer
indexes and the point in space is defined using floats, the noise value
is determined by interpolating between the lattice values located at
the integer points around it. The number of integer points it has to
interpolate between increases exponentially with each dimension added,
so it's a good idea to minimize the number of dimensions you use. The
range of noise values returned is between -1.0 and 1.0.
on the range of the numbers you pass it in each dimension, 2D Perlin
noise output can look like anything ranging from white noise on a TV
screen to a very smooth random pattern. Because the same coordinates
always generate the same output, decreasing the range of numbers you
pass it into is like zooming in on part of the noisy image, while increasing
the range zooms out. If you look at the images in Figure 1, you can
see how increasing the range zooms out (look at the top-left corner
of each image). Note how the leftmost image looks like an elevation
Perlin noise is considered very fast for such a powerful noise function,
it is still too slow to use in a game engine for most purposes. As mentioned
above, each extra dimension added slows it down exponentially, but in
many cases using extra dimensions can be very useful. Adding another
spatial dimension to the image on the left in Figure 1 would give you
something that resembled volumetric fog. You can also add a time dimension,
achieving interesting animation effects by setting one of the coordinates
to a particular time value.
Perlin noise is occasionally used to generate bump maps, reflection
maps, and textures. However, because of its somewhat smooth and uniform
appearance, Perlin noise is more often used as a base for more complex
functions. More information on Perlin noise and its uses can be found
which has a set of online course notes written by Ken Perlin.