Deep Dive into GPU-Based Gradient Noise: From 1D to 3D and Derivative Calculation

This article delves into the intricacies of implementing gradient noise on a GPU, starting with 1D noise and gradually progressing to higher dimensions. It covers hashing functions, pseudo-random value generation, gradient noise implementation in various dimensions, and derivative calculation. The article focuses on efficient GPU-based computation using WebGL2/GLSL, comparing numerical and analytical derivative approaches. Furthermore, it explores applications of Fractal Brownian Motion (fBm) and derivatives in texture generation, lighting calculations, and terrain simulation. The detailed content is ideal for readers interested in computer graphics and shader programming.