Genetic cellular neural networks for generating three-dimensional geometry
This is an incremental method for generating 3D geometry, potentially useful for designers or researchers in procedural content creation.
The paper tackles the problem of procedurally generating 3D shapes by combining a cellular neural network with a mesh growth algorithm, resulting in emergent shapes that mimic biological complexity through genetic mutation.
There are a number of ways to procedurally generate interesting three-dimensional shapes, and a method where a cellular neural network is combined with a mesh growth algorithm is presented here. The aim is to create a shape from a genetic code in such a way that a crude search can find interesting shapes. Identical neural networks are placed at each vertex of a mesh which can communicate with neural networks on neighboring vertices. The output of the neural networks determine how the mesh grows, allowing interesting shapes to be produced emergently, mimicking some of the complexity of biological organism development. Since the neural networks' parameters can be freely mutated, the approach is amenable for use in a genetic algorithm.