Semantic Decomposition and Reconstruction of Compound Buildings with Symmetric Roofs from LiDAR Data and Aerial Imagery

Remote Sensing

Hongtao Wang²  Wuming Zhang²  Yiming Chen²  Mei Chen²  Kai Yan²  Norman Kerle¹ 

[1] State Key Laboratory of Remote Sensing Science, Beijing Key Laboratory of Environmental Remote Sensing and Digital City, School of Geography, Beijing Normal University, Beijing 100875, China;;State Key Laboratory of Remote Sensing Science, Beijing Key Laboratory of Environmental Remote Sensing and Digital City, School of Geography, Beijing Normal University, Beijing 100875, China; E-Mails:

关键词: semantic decomposition;    compound building modeling;    LiDAR point cloud;    aerial imagery;    attributed graph;   

DOI  :  10.3390/rs71013945

来源: mdpi

【 摘 要 】

3D building models are important for many applications related to human activities in urban environments. However, due to the high complexity of the building structures, it is still difficult to automatically reconstruct building models with accurate geometric description and semantic information. To simplify this problem, this article proposes a novel approach to automatically decompose the compound buildings with symmetric roofs into semantic primitives by exploiting local symmetry contained in the building structure. In this approach, the proposed decomposition allows the overlapping of neighbor primitives and each decomposed primitive can be represented as a parametric form, which simplify the complexity of the building reconstruction and facilitate the integration of LiDAR data and aerial imagery into a parameters optimization process. The proposed method starts by extracting isolated building regions from the LiDAR point clouds. Next, point clouds belonging to each compound building are segmented into planar patches to construct an attributed graph, and then the local symmetries contained in the attributed graph are exploited to automatically decompose the compound buildings into different semantic primitives. In the final step, 2D image features are extracted depending on the initial 3D primitives generated from LiDAR data, and then the compound building is reconstructed using constraints from LiDAR data and aerial imagery by a nonlinear least squares optimization. The proposed method is applied to two datasets with different point densities to show that the complexity of building reconstruction can be reduced considerably by decomposing the compound buildings into semantic primitives. The experimental results also demonstrate that the traditional model driven methods can be further extended to the automated reconstruction of compound buildings by using the proposed semantic decomposition method.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.