Shape-preserving mesh decimation for 3D building modeling
Jing Li, Dong Chen, Fan Hu, Yuliang Wang, Peng Li, Jiju Peethambaran
Abstract
We propose a shape-preserving building model reconstruction method that involves simplifying the original building mesh to accommodate various building shapes and complexities. To achieve this, we apply a structure-aware segmentation technique to parse the ubiquitous building points into building geometric primitives and building structural points, i.e., anchor points. After that, we generate dense building meshes from building semantic points in a topology-aware manner. As the geometric primitive semantics are assigned to the building points during the structure-aware segmentation process, these primitive semantics of the building points can be explicitly transferred into the created building meshes. To offer lightweight and accurate building models with enriched semantics, we leverage the building structural points as constraints for the subsequent edge collapse simplification algorithm. This algorithm effectively decimates irrelevant vertices and meshes, while preserving the essential building structural contours. The entire simplification process is performed in a shape-preserving manner, granting us flexible control over the imposition of different degrees of strength regarding various geometric primitives during the simplification. We conduct qualitative and quantitative analyses to evaluate the effectiveness of our method on both individual building models and a large-scale urban scene. Additionally, we extensively compare our proposed shape-preserving algorithm with other state-of-the-art mesh decimation methods to demonstrate our superiority.