A Quantitative Texture Feature Preserving Method for Simplifying 3D Building Models
- https://doi.org/10.2991/cmsa-18.2018.17How to use a DOI?
- building models; LOD; quantitative simplification; structural feature; texture synthesis
Three-dimensional (3D) city models have become an important component of spatial information infrastructure, and widely used in studies of virtual geographical environments, urban planning, and navigation, as well as other fields. Level of detail (LOD) representation of 3D models provides a foundation for the large-scale visualization and application of building models, and simplification is one of the most popular approaches used to generate multi-resolution 3D building models. This paper proposes a texture feature preserving approach to the quantitative simplification of manually produced building models. First, the original models are converted to polygonal models with the B-Rep method. The structural features of the buildings are then extracted and used to perform quantitative simplification of the building models while preserving the structural features. Next, the textures of new triangles are synthesized from the original textures. Finally, a post-processing algorithm is used to detect overlapping and/or intersecting triangles to improve the simplification results. Experimental results show that the proposed method can be adapted to models of ordinary buildings and landmarks. It can be directly applied to large-scale building model network visualization.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Po Liu AU - Chengming Li AU - Zhanjie Zhao AU - Zhendong Liu PY - 2018/04 DA - 2018/04 TI - A Quantitative Texture Feature Preserving Method for Simplifying 3D Building Models BT - Proceedings of the 2018 International Conference on Computer Modeling, Simulation and Algorithm (CMSA 2018) PB - Atlantis Press SP - 72 EP - 77 SN - 1951-6851 UR - https://doi.org/10.2991/cmsa-18.2018.17 DO - https://doi.org/10.2991/cmsa-18.2018.17 ID - Liu2018/04 ER -