Synthetic 2D point clouds generator

Every day, while developing applications and products to solve a huge number of today problems, data from real world is registered and consumed. The registration of this kind of data is costly, and on its quality depends on the correct behaviour of the solutions developed. Some of this data are 2-dimensional point clouds, for example spatial points registered by sensors. In this project, we present and investigate the use Generative Adversarial Networks and Neural Style Transfer over 2-dimensional point clouds in order to develop a tool to generate synthetic but realistic data based on real ones. We also study the possibility of combining these two technologies to improve each other's behaviour.