CODEWITHSUNDEEP
c#performanceperlin-noisesimplex-noise
Mythics
Mythics

Reputation: 803

Desiring jagged results from simplex noise or another algorithm just as fast

I'm wanting to do some placement of objects like trees and the like based on noise for the terrain of a game/tech demo.

I've used value noise previously and I believe I understand perlin noise well enough. Simplex noise, however, escapes me quite well (just a tad over my head at present).

I have an implementation in C# of simplex noise, however, it's almost completely stolen from here. It works beautifully, but I just don't understand it well enough to modify it for my own purposes.

It is quite fast, but it also gives rather smooth results. I'm actually wanting something that is a little more jagged, like simple linear interpolation would give when I was doing value noise. My issue here is that due to the amount of calls I'd be doing for these object placements and using fractal Brownian motion, the speed of the algorithm becomes quite important.

Any suggestions on how to get more 'jagged' results like linear interpolation gives with value noise using a faster algorithm than value noise is?

Upvotes: 0

Views: 452

Answers (2)

Adam Brown
Adam Brown

Reputation: 1729

I realise this is a very old question, but I felt that the previous answer was entirely wrong, so I wanted to clarify how you should use a noise function to determine the placement of things like trees / rocks / bushes.

Basically, if you want to globally place items across a terrain, you're going to need some function which tells you where those are likely to occur. For instance, you might say "trees need to be on slopes of 45 degrees or less, and below 2000 meters". This gives you a map of possible places for trees. But now you need to choose random, but clustered locations for them.

The best way of doing this is to multiply your map of zeroes and ones by a fractal function (i.e. a Simplex noise function or one generated through subdivision and displacement - see https://fractal-landscapes.co.uk/maths).

This then gives you a probability density function, where the value at a point represents the relative probability of placing a tree at that location. Now you store the partial sum of that function for every location on the map. To place a new tree:

  1. Choose a random number between 0 and the maximum of the summed function.
  2. Do a binary search to find the location on the map in this range.
  3. Place the tree there.
  4. Rinse and repeat.

This allows you to place objects where they belong, according to their natural ranges and so on.

Upvotes: 0

bandybabboon
bandybabboon

Reputation: 2346

if you are using a complex noise function to do a simple task like the placement of trees, your using completely the wrong type of maths function. It is a very specific function which is great for making textures and 3d shapes and irregular curves. Placing treas on 2d certainly doesn't need irregular curves! Unless you want to place trees along in lines that are irregular and curved!

unless you mean you want to place trees in areas of the noise which are a certain level, for example where the noise is larger than 0.98, which will give you nicely randomised zones that you can use as a central point saying some trees will be there.

it will be a lot faster and a lot easier to vary, if you just use any normal noise function, just program your placement code around the noise function. I mean a predictable pseudo-random noise function which is the same every time you use it.

use integers 0 to 10 and 20 to 30, multiplied by your level number, to select 10 X and 10 Y points on the same pseudo-random noise curve. this will give you 10 random spots on your map from where to do stuff using almost no calculations.

Once you have the central point where trees will be, use another 10 random points from the function to say how many trees will be there, another 10 to say how far apart they will be, for the distribution around the tree seed quite exceptional.

The other option, if you want to change the curve http://webstaff.itn.liu.se/~stegu/simplexnoise/simplexnoise.pdf is to read this paper and look at the polynomial function /whatever gradient function could be used in your code, looking the comments for the gradient function, commented out and do X equals Y, which should give you a straight interpolation curve.

if you vote this answer up, I should have enough points in order to comment on this forum:]

Upvotes: 1

Related Questions