Feature Preserving Decimation of Urban Meshes
Vivek Kamra, Prachi Kudeshia, Somaye ArabiNaree, Dong Chen, Yasushi Akiyama, Jiju Peethambaran · 2023
3D models of urban buildings have paramount importance to most digital urban applications. However, requirement of large storage and high computational cost for processing the geometric details of urban objects have been observed as a major limitation to existing 3D modeling approaches. This draws the need of lightweight modeling techniques requiring less computational storage to capture the details of the urban entities. Additionally these models should facilitate accelerated visualizations along with consuming lesser bandwidth for online applications. In this paper, we propose a lightweight urban modeling method using gradient structure tensors based feature point extraction to produce highly detailed lightweight 3D building models from LiDAR scans. Further, a mean cost-based edge collapse operation is proposed to preserve the feature points. The qualitative and quantitative analysis and comparative study of different building façade models shows the efficacy of our method in generating simplified models with a trade-off between model simplification and accuracy.