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copied!<p>A friend just linked me to this question, and I thought I'd try and clear up a couple things that aren't addressed in the accepted answer.</p> <p>Elias' interesting and helpful article uses "value noise" not "Perlin noise". Value noise involves curve-fitting of randomized points. Gradient noise (of which Perlin noise is a primary example) creates a lattice of 0-value points and gives each one a random gradient. They are frequently confused with one another!</p> <p><a href="http://en.wikipedia.org/wiki/Gradient_noise" rel="nofollow noreferrer">http://en.wikipedia.org/wiki/Gradient_noise</a></p> <p>Secondly, using a 3rd value as a seed is expensive. If you want random terrain, consider translating your origin a random amount instead. 3D calls are going to be more expensive than 2D calls. And all you are doing is using the z value to select a particular slice of 2D noise.</p> <p>Thirdly, the straight function call is going to return values that are fairly smooth and rolling overall, not as craggy as real terrain, since it's randomness is limited to a single frequency. To get craggier terrain, a good technique is to sum together multiple calls that progress through the noise space at differing frequencies, usually set a "fractal" values. </p> <p>Thus, for example, sum together <code>noise(x, y) + (1/2)(noise(x*2, y*2) + (1/4)(noise(x*4, y*4)</code>...</p> <p>The resulting sum will probably often be outside the range -1 to 1, so you will have to normalize the result before the values are useful. I'd like to suggest setting up the factor series (1, 1/2, 1/4, etc.) so that you are guaranteed to remain within [-1, 1] which can be done by progressive weighting depending upon how many 'octaves' you use. (But I don't know if this is truly the most efficient way to do this.)</p> <p>Example with four octaves: <code>(1/15)(noise(x, y) + (2/15)(noise(2x, 2y) + (4/15)(noise(4x, 4y) + (8/15)(noise(8x, 8y)</code></p> <p>Then, use the "turbulent noise" normalization of taking the sum and making it <code>= |sum|</code> (i.e., using the abs function). This will give the terrain definite angular valley ridges as opposed to being smoothly rolling.</p> <p>I'm working on a SimplexNoise visualizer, hope to open source it on GitHub eventually, as a Java project. A first draft of the visualizer can be found and run via this post at java-gaming.org: <a href="http://www.java-gaming.org/topics/simplex-noise-experiments-towards-procedural-generation/27163/view.html" rel="nofollow noreferrer">http://www.java-gaming.org/topics/simplex-noise-experiments-towards-procedural-generation/27163/view.html</a> The emphasis on the first draft is more tutorial, with generated code examples (but they are in Java).</p> <p>Great article on how SimplexNoise works (and Perlin vs Gradient background): <a href="http://staffwww.itn.liu.se/~stegu/simplexnoise/simplexnoise.pdf" rel="nofollow noreferrer">http://staffwww.itn.liu.se/~stegu/simplexnoise/simplexnoise.pdf</a></p> <p>Stefan Gustavson did a really nice job of this!</p>

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