Generalization of 3D building data based on a scale-space approach

Abstract

In image analysis, scale-space theory is used, e.g., for object recognition. A scale-space is obtained by deriving coarser representations at different scales from an image. With it, the behaviour of image features over scales can be analysed. One example of a scale-space is the reaction-diffusion-space, a combination of linear scale-space and mathematical morphology. As scale-spaces have an inherent abstraction capability, they are used here for the development of an automatic generalization procedure for three-dimensional (3D) building models. It can be used to generate level of detail (LOD) representations of 3D city models. Practically, it works by moving parallel facets towards each other until a 3D feature under a certain extent is eliminated or a gap is closed. As not all building structures consist of perpendicular facets, means for a squaring of non-orthogonal structures are given. Results for generalization and squaring are shown and remaining problems are discussed. The conference version of this paper is Forberg [Forberg, A., 2004. Generalization of 3D Building Data Based on a Scale-Space Approach. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 35 (Part B4) http://www.isprs.org/istanbul2004/comm4/papers/341.pdf (accessed January 17, 2007)].