A Topology-Preserving Simplification Method for 3D Building Models

Simplification of 3D building models is an important way to improve rendering efficiency. When existing algorithms are directly applied to simplify multi-component models, generally composed of independent components with strong topological dependence, each component is simplified independently. The...

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Bibliographic Details
Published inISPRS international journal of geo-information Vol. 10; no. 6; p. 422
Main Authors Wang, Biao, Wu, Guoping, Zhao, Qiang, Li, Yaozhu, Gao, Yiyuan, She, Jiangfeng
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2021
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Summary:Simplification of 3D building models is an important way to improve rendering efficiency. When existing algorithms are directly applied to simplify multi-component models, generally composed of independent components with strong topological dependence, each component is simplified independently. The consequent destruction of topological dependence can cause unreasonable separation of components and even result in inconsistent conclusions of spatial analysis among different levels of details (LODs). To solve these problems, a novel simplification method, which considers the topological dependence among components as constraints, is proposed. The vertices of building models are divided into boundary vertices, hole vertices, and other ordinary vertices. For the boundary vertex, the angle between the edge and component (E–C angle), denoting the degree of component separation, is introduced to derive an error metric to limit the collapse of the edge located at adjacent areas of neighboring components. An improvement to the quadratic error metric (QEM) algorithm was developed for the hole vertex to address the unexpected error caused by the QEM’s defect. A series of experiments confirmed that the proposed method could effectively maintain the overall appearance features of building models. Compared with the traditional method, the consistency of visibility analysis among different LODs is much better.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi10060422