Binary Space Partitioning Trees: A Multiresolution Approach
Joaquín Huerta, Miguel Chover, Ricardo Quirós, Roberto Vivó and José Ribelles
Space partitioning techniques are a useful means of organizing geometric models into data structures. Such data structures provide easy and efficient access to a wide range of computer graphics and visualization applications like real-time rendering of large data bases, collision detection, point classification, etc. Binary Space Partitioning (BSP) trees are one of the most successful space partitioning techniques, since they allow both object modeling and classification in one single structure. However, with the advent of networked graphics applications there is an increasing need for multiresolution geometric representations. This paper presents a novel method that extends BSP trees to provide such a representation. The models we present have the advantages of both BSP trees and multiresolution representations. Nodes near the root of the BSP tree store coarser versions of the geometry, while leaf nodes provide finer details of the representation. We present in this paper different algorithms to construct multiresolution BSP trees in 2D. Then we propose extensions of our methods to 3D space.