I also have some 2D noise experiments, including 3D visualization of a 2D heightmap. how midpoint displacement, Simplex/Perlin noise, and fBm fit in.how you can use noise functions for procedural map generation.what red, pink, white, blue, and violet noise are.how to generate landscapes like these in under 15 lines of code.Try moving this slider to see how a single parameter can describe many different styles of noise: 0 The same concepts work for 2D (see demo) and 3D. I’m going to start with the basics of using random numbers and work my way up to explaining how 1D landscapes work. If you want to skip ahead to terrain generation using noise functions, see my other article. This page is about the concepts starting from the simplest ideas and working up. The math on this page is mostly sine waves. I only cover simple topics (frequency, amplitude, colors of noise, uses of noise) and not related topics (discrete vs continuous functions, FIR/IIR filters, FFT, complex numbers). I don’t think any of this is new but some of it is new to me, so I wanted to write it down and share. So here are some notes on how signal processing concepts relate to map generation. As I was studying audio signal processing, my brain started making connections back to procedural map generation.
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