>>2007,WANG Qingguo, Automatic Generalization Methodology of 3D Urban building Group Models

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Abstract

With the rapid development of city information construction, research on 3D City Model (3DCM) has become a hot and widely attractive issue. Moreover, 3DCM is widely applying in many fields.

However, the geometric structure of 3DCM is complicated and the volume of texture data is huge, which is beyond the real time rendering abilities of current work stations. A common solution is to decrease the data volumes of geometric data and texture data to improve rendering speed. On the other hand, various applications have different requirements on the accuracy of geometric data and visual effects. For example, a lot of applications may emphasis on the details of geometric data. Some applications may need a holistic structure. Hence, the levels of detail (LoD) of 3DCM is of vital importance for a 3DCM, which has attracted wide research in recent years.

An ideal solution of generating Lod models is to derive a set of low resolution Lods from an original model based on generalizations. The generalization of 3DCM includes the simplification of single building model and the generalization of 3D urban building group models. However, existing research mainly focuses on the former; the latter one receives less attention. This thesis aims at the generalization of 3D building group models in local urban area, the main contents include:

1) Firstly, the theories and approaches in 2D map generalization and 3D model simplification are reviewed. Secondly, the trend of map generalization is summarized, namely, from 2D map generalization to 3D model generalization, from manual generalization to automated generalization, from visualization oriented to multi-applications oriented, from generalization of simple elements to generalization of complicate multi-dimension objects and scenes. Then, the main research topic on the thesis, automated generalization of adjacent 3D building geometric models in local urban area, is built.

2) The thesis proposes a hierarchical partition of 3D urban building group model in Chapter 2. Firstly, the characteristics of urban building groups are analyzed, correlative theories on city are analyzed and mined for the generalization of urban building group models. Then, the criterions and approaches of partition are established. Finally, a partition approach based on road is implemented for the partition of 3D urban building group models, which is quite different from the partition approach for 2D.

3) Chapter 3 presents a clustering approach based on adjacency and similarity. In the traditional clustering approaches, objects are treated as points without sizes and orientation, and only the adjacency between objects is considered. However, both the adjacency and similarity of objects should at least be taken into account for 3D clustering.

4) Chapter 4 presents generalization constraints, which aim at the generalization of 3D building group models. Three layers of constraints, namely, geometric constraints, relation constraints, and distribution structure constraints, are concerned.

5) Chapter 5 shows aggregation algorithm experiments. Based on the idea of scale-space, an aggregation algorithm is established and proved to be valid for the generalization of 3D building group models.

Key Words

Scale; Levels of Detail; 3D City Model; 3D Urban Building Group Model; Automatic Generalization; Characteristic