A novel Building Section Skeleton for compact 3D reconstruction from point clouds: A study of high-density urban scenes

Compact building models are demanded by global smart city applications, while high -definition urban 3D data is increasingly accessible by dint of the advanced reality capture technologies. Yet, existing building reconstruction methods encounter crucial bottlenecks against high -definition data of large scales and highlevel complexity, particularly in high -density urban scenes. This paper proposes a Building Section Skeleton (BSS) to reflect architectural design principles about parallelism and symmetries. A BSS atom describes a pair of intrinsic parallel or symmetric points; a BSS segment clusters dense BSS atoms of a pair of symmetric surfaces; the polyhedra of all BSS segments further echo the architectural forms and reconstructability. To prove the concepts of BSS for automatic compact reconstruction, this paper presents a BSS method for building reconstruction that consists of one stage of BSS segments hypothesizing and another stage of BSS segments merging. Experiments and comparisons with four state-of-the-art methods have been conducted on 15 diverse scenes encompassing more than 60 buildings. Results confirmed that the BSS method achieves frontiers in compactness, robustness, geometric accuracy, and efficiency, simultaneously, especially for high -density urban scenes. On average, the BSS method reconstructed each scene into 623 triangles with a root -mean -square deviation (RMSD) of 0.82 m, completing the process in 110 s. First, the proposed BSS is an expressive 3D feature reflecting architectural designs in high -density cities, and can open new avenues to city modeling and other urban remote sensing and photogrammetry studies. Second, for practitioners in smart city development, the BSS method for building reconstruction offers an accurate and efficient approach to compact building and city modeling. The source code and tested scenes are available at https://github.com/eiiijiiiy/sobss.