A large‐scale urban 3D model organisation method considering spatial distribution of buildings

Abstract

Abstract The rendering of urban 3D scenes involves a large number of models. In order to render scenes more efficiently, the main solution is to build a level of detail model (LOD). This may have the problem of building fragmentation, while relying on building a level of detail model (LOD) alone cannot meet the accuracy and fluency of large‐scale scene visualisation. Effective and reasonable data organisation has important research significance for the authors to achieve accurate and fast rendering of scenes. Therefore, the authors propose a large‐scale city model data organisation method considering building distribution to solve the above problems. This method first classifies the buildings in the scene at macro‐, meso‐ and microscales and records the classification using R‐trees. Then an adaptive quadtree is used to construct the data index of the city model. Finally, the data organisation of the large‐scale 3D city model is achieved by using the information of each node of the R‐tree as a constraint and combining with the adaptive quadtree. The results show that the method not only ensures the integrity of the user's area of interest but also can improve the efficiency of 3D scene construction.

Keywords

• city brain and smart cities metrics
• smart cities
• smart cities standards