A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Building outline extraction using adaptive tracing alpha shapes and contextual topological optimization from airborne LiDAR
Abstract It is challenging to extract satisfactory building outlines from LiDAR data due to the unorganized point cloud and complex building shapes. To solve the issues, a method using adaptive tracing alpha shapes (ATAS) and contextual topological optimization is proposed. First, the ATAS method is used to extract sequential boundary points. After that, a method based on point cloud distribution analysis is developed to obtain building dominant directions and line segments of outlines. Finally, regularized outlines are obtained by adjusting all line segments simultaneously under the framework of global energy optimization that considers the geometric errors and contextual geometric relationships between adjacent line segments. Experimental results verify that the proposed ATAS method can efficiently extract sequential boundary points with a minimum 98.49% correctness. In addition, the extracted outlines are attractive and the minimum values of the RMSE, PoLiS, and RCC metrics of the extracted outlines are 0.48 m, 0.44 m, and 0.31 m, respectively, showing the effectiveness of the proposed method.
Highlights Adaptive tracing alpha shapes method (ATAS) is proposed to efficiently extract sequential boundary points directly from unorganized building point clouds with complex shapes, without pre-processing (e.g., triangulation, gridding). A method based on point cloud distribution analysis is proposed to detect accurate and reliable building dominant directions, which is beneficial to subsequent outline extraction. The proposed method can extract smooth and attractive outlines from complex buildings by formulating outline regularization as an optimal labeling problem under the framework of global energy optimization, which balances geometric errors of boundary points and contextual geometric relationships between adjacent line segments.
Building outline extraction using adaptive tracing alpha shapes and contextual topological optimization from airborne LiDAR
Abstract It is challenging to extract satisfactory building outlines from LiDAR data due to the unorganized point cloud and complex building shapes. To solve the issues, a method using adaptive tracing alpha shapes (ATAS) and contextual topological optimization is proposed. First, the ATAS method is used to extract sequential boundary points. After that, a method based on point cloud distribution analysis is developed to obtain building dominant directions and line segments of outlines. Finally, regularized outlines are obtained by adjusting all line segments simultaneously under the framework of global energy optimization that considers the geometric errors and contextual geometric relationships between adjacent line segments. Experimental results verify that the proposed ATAS method can efficiently extract sequential boundary points with a minimum 98.49% correctness. In addition, the extracted outlines are attractive and the minimum values of the RMSE, PoLiS, and RCC metrics of the extracted outlines are 0.48 m, 0.44 m, and 0.31 m, respectively, showing the effectiveness of the proposed method.
Highlights Adaptive tracing alpha shapes method (ATAS) is proposed to efficiently extract sequential boundary points directly from unorganized building point clouds with complex shapes, without pre-processing (e.g., triangulation, gridding). A method based on point cloud distribution analysis is proposed to detect accurate and reliable building dominant directions, which is beneficial to subsequent outline extraction. The proposed method can extract smooth and attractive outlines from complex buildings by formulating outline regularization as an optimal labeling problem under the framework of global energy optimization, which balances geometric errors of boundary points and contextual geometric relationships between adjacent line segments.
Building outline extraction using adaptive tracing alpha shapes and contextual topological optimization from airborne LiDAR
Liu, Ke (author) / Ma, Hongchao (author) / Zhang, Liang (author) / Gao, Lu (author) / Xiang, Shitao (author) / Chen, Dachang (author) / Miao, Qing (author)
2024-02-06
Article (Journal)
Electronic Resource
English
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