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Imprecision

LOD with Perlin Noise "zoom"

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Hi guys,

I'm using my Perlin Noise algorithm as a height map (I don't store as a texture, I use the values directly on my vertices).
Representing an earth scale planet I need a height map for at least 510 million km².

My problem is now, that I don't know how to make a "more detailed" noise on higher LOD.
There must be another way than making an array of 32000*32000 random numbers for perlin noise to get acceptable terrain on highest LOD.

Are there any techniques to get this done?

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Don't use a random function, rather use a hash function to generate the noise values. That way each point can be calculated independently of any other point, making LOD quite easy.

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Straight-up Perlin noise is a rather uninteresting sinusoidal curve. To generate a perlin-based terrain, one usually combines multiple octaves (layers) of perlin noise together, while varying the frequency/scale of each octave. Look around for "fractal brownian motion", or "ridged multifractal" for a few common such fractals.

I'm assuming you are already using such a multi-octave noise function, in which case, to increase detail at higher LOD, you just add successively more octaves of noise.

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Hi swiftcoder. Thanks for the keywords

"fractal brownian motion" and "ridged multifractal" thats exactly what I was looking for! Now I got tons of new material smile.png





[color=#282828][font=helvetica, arial, verdana, tahoma, sans-serif]NEW Question:[/font]


[color=#282828][font=helvetica, arial, verdana, tahoma, sans-serif]Is the only way to get a seamless cubemap for terrain, to generate 3D noise? Or is there an algorithm to map 2D data seamless on a cube? [/font]

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Is the only way to get a seamless cubemap for terrain, to generate 3D noise? Or is there an algorithm to map 2D data seamless on a cube?

One can come up with a number of kludgy solutions using tiling 2D noise and a polar projection, but nothing that works terribly reliably.

I always end up using 3D noise for my procedural planets - it just looks and works better.

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