Abstract
This paper presents a global solution to building roof topological reconstruction from LiDAR point clouds. Starting with segmented roof planes from building LiDAR points, a BSP (binary space partitioning) algorithm is used to partition the bounding box of the building into volumetric cells, whose geometric features and their topology are simultaneously determined. To resolve the inside/outside labelling problem of cells, a global energy function considering surface visibility and spatial regularization between adjacent cells is constructed and minimized via graph cuts. As a result, the cells are labelled as either inside or outside, where the planar surfaces between the inside and outside form the reconstructed building model. Two LiDAR data sets of Yangjiang (China) and Wuhan University (China) are used in the study. Experimental results show that the completeness of reconstructed roof planes is 87.5%. Comparing with existing data-driven approaches, the proposed approach is global. Roof faces and edges as well as their topology can be determined at one time via minimization of an energy function. Besides, this approach is robust to partial absence of roof planes and tends to reconstruct roof models with visibility-consistent surfaces.
Highlights
Automated reconstruction of building roof models is of a current research interest in 3D city modelling. (Airborne) LiDAR (Light Detection and Ranging) technology can directly collect dense, accurate 3D point clouds over building roofs, from which 3D building models may be automatically reconstructed
This paper presents a global optimization solution to roof topology reconstruction from airborne LiDAR points
Starting from segmented roof planes, the building space is partitioned to possible roof edges with their topology
Summary
Automated reconstruction of building roof models is of a current research interest in 3D city modelling. (Airborne) LiDAR (Light Detection and Ranging) technology can directly collect dense, accurate 3D point clouds over building roofs, from which 3D building models may be automatically reconstructed. To extract roof primitives from LiDAR point clouds, techniques such as invariant moments (Maas and Vosselman, 1999), graph matching (Oude Elberink and Vosselman, 2009; Verma et al, 2006; Xiong et al, 2014), Support Vector Machine (SVM) (Henn et al, 2013; Satari et al, 2012), RANdom SAmple Consensus (RANSAC) (Henn et al, 2013) and Reversible Jump Markov Chain Monte Carlo (RJMCMC) (Huang et al, 2013) are used These approaches tend to fail when reconstructing complex roof shapes.
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