Visualization of Generalized Voronoi Diagrams

Abstract

Voronoi diagrams are an important data structure in computer science. However well studied mathematically, understanding such diagrams for different metrics, orders, and site shapes is a complex task. We propose a new method to visualize k-order diagrams and give an efficient adaptive implementation for this method. The algorithm is easy to customize for different metrics and site shapes. Its real-time performance makes it suitable for interactive planning and analysis of complex Voronoi configurations in 2D.We illustrate the method for different combinations of metrics and site shapes.