Reconstruction of LoD-2 Building Models Guided by Façade Structures from Oblique Photogrammetric Point Cloud
Feng Wang, Guoqing Zhou, Han Hu, Yuefeng Wang, Bolin Fu, Shiming Li, Jiali Xie
Abstract
Due to the façade visibility, intuitive expression, and multi-view redundancy, oblique photogrammetry can provide optional data for large-scale urban LoD-2 reconstruction. However, the inherent noise in oblique photogrammetric point cloud resulting from the image-dense matching limits further model reconstruction applications. Thus, this paper proposes a novel method for the efficient reconstruction of LoD-2 building models guided by façade structures from an oblique photogrammetric point cloud. First, a building planar layout is constructed combined with footprint data and the vertical planes of the building based on spatial consistency constraints. The cells in the planar layout represent roof structures with a distinct altitude difference. Then, we introduce regularity constraints and a binary integer programming model to abstract the façade with the best-fitting monotonic regularized profiles. Combined with the planar layout and regularized profiles, a 2D building topology is constructed. Finally, the vertices of building roof facets can be derived from the 2D building topology, thus generating a LoD-2 building model. Experimental results using real datasets indicate that the proposed method can generate reliable reconstruction results compared with two state-of-the-art methods.