Constructing rules and scheduling technology for 3D building models

3D Models have become important form of geographic data beyond conventional 2D geospatial data. Buildings are important marks for human to identify their environments, because they are close with human life, particularly in the urban areas. Geographic information can be expressed in a more intuitive and effective manner with architectural models being modeled and visualized in a virtual 3D environment. Architectural model data features with huge data volume, high complexity, non-uniform rules and so on. Hence, the cost of constructing large-scale scenes is high. Meanwhile, computers are lack of processing capacity upon a large number of model data. Therefore, resolving the conflicts between limited processing capacity of computer and massive data of model is valuable. By investigating the characteristics of buildings and the regular changes of viewpoint in virtual 3D environment, this article introduces several constructing rules and scheduling techniques for 3D constructing of buildings, aiming at the reduction of data volume and complexity of model and thus improving computers´ efficiency at scheduling large amount of architectural models. In order to evaluate the efficiency of proposed constructing rules and scheduling technology listed in the above text, the authors carry out a case study by 3D constructing the campus of Peking University using the proposed method and the traditional method. The two results are then examined and compared from aspects of model data volume, model factuality, speed of model loading, average responding time during visualization, compatibility and reusability in 3D geo-visualization platforms: China Star, one China´s own platform for 3D global GIS manufactured by the authors of this paper. The result of comparison reveals that models built by the proposed methods are much better than those built using traditional methods. For the constructing of building objects in large-scale scenes, the proposed methods can not only reduce the co- - mplexity and amount of model data remarkably, but can also improving computers´ efficiency.