A bottom-up method for roof plane extraction from airborne LiDAR point clouds

Abstract

Accurate roof plane extraction is a crucial step in constructing a three-dimensional model for buildings. Due to the significant differences in size and shape of building roofs in airborne light detection and ranging point clouds, many existing plane extraction methods are struggling to achieve good performance. To solve the above problem, a bottom–up method for roof plane extraction is proposed in this paper. Starting with the division of the roof point cloud into voxels, the initial planes are obtained in the voxels. The initial planes are then expanded by a parameter-adaptive region growing algorithm. Then, the grown planes are merged according to predefined constraints. Finally, an energy minimization-based method is applied to optimize the results of roof plane extraction. The performance of our proposed method is evaluated on the Vaihingen dataset and the DALES dataset. Experiments demonstrate that our proposed method achieves a superior roof plane extraction result.