  A new method is presented, allowing for the generation of 3D terrain and
texture from coherent noise. The method is significantly faster than prevailing
fractal brownian motion approaches, while producing results of equivalent
quality. The algorithm is derived through a systematic approach that
generalizes to an arbitrary number of spatial dimensions and gradient
smoothness. The results are compared, in terms of performance and quality, to
fundamental and efficient gradient noise methods widely used in the domain of
fast terrain generation: Perlin noise and OpenSimplex noise. Finally, to
objectively quantify the degree of realism of the results, a fractal analysis
of the generated landscapes is performed and compared to real terrain data.
