Catlike Coding

We're making donuts! This time we adapt value noise so it can tile. This allows us to wrap it seamlessly around a torus. Tiling Noise - Procedural GPU Patterns (Catlikecoding)

The final part of the pseudorandom surfaces series adds particles that flow across the surfaces of planes and spheres, flowing either downhill or along elevation contour lines. Surface Flow (Catlikecoding)

We're not limited to applying noise to planes. Now we can also displace the surfaces of spheres. Spherical Elevation (Catlikecoding)

We also calculate the analytical derivatives for Voronoi noise. As this noise has sharp ridges and points we also introduce a smooth variants of F1 Worley noise. Voronoi Derivatives (Catlikecoding)

After taking care of Simplex noise we also calculate the analytical derivates of value and Perlin noise. Perlin Derivatives (Catlikecoding)

In this tutorial we introduce analytical derivatives and use those to construct normal and tangent vectors. Simplex noise is simplest, so we start with that. We also include a smoothed version of turbulence. Simplex Derivatives (Catlikecoding)

Now that we have both pseudorandom noise and procedural meshes, we combine the two and create pseudorandom surfaces! Surface Elevation (Catlikecoding)

This time we add support for the final big noise algorithm that was still missing: simplex noise! We also include a simplex value noise variant and start with that. The math of simplex noise is more complex than hypercube-based noise, but you can skip most of it if you're not interested in the speci

This time we add another type of noise Voronoi, with support for multiple distance functions and distance metrics. Voronoi Noise (Catlikecoding)

This time we spice up our noise with fractal, turbulence, and tiling variants. Noise Variants (Catlikecoding)

After Value noise comes Perlin noise! Perlin Noise (Catlikecoding)

We finally get to some real noise, beginning with the simplest lattice type: value noise! Hashing Space (Catlikecoding)

Part two of Pseudorandom Noise is here! It evolves our hash visualization so it works on a transformed 3D space and can sample with different shapes. It also introduces manual vectorization when working with nontrivial data. Everything is now set up so that we can move on to proper noise in the next

It is finally time to revisit the old Pseudorandom Noise series! We start with a fresh approach based on hashing, taking full advantage of jobs and Burst. I use this opportunity to introduce a small variant of xxHash specifically designed to be fed small data like 2D and 3D integer coordinates. Hash