Volume III-3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 379-386, 2016
https://doi.org/10.5194/isprs-annals-III-3-379-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 379-386, 2016
https://doi.org/10.5194/isprs-annals-III-3-379-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  06 Jun 2016

06 Jun 2016

A GLOBAL SOLUTION TO TOPOLOGICAL RECONSTRUCTION OF BUILDING ROOF MODELS FROM AIRBORNE LIDAR POINT CLOUDS

Jixing Yan1,2, Wanshou Jiang3, and Jie Shan4,2 Jixing Yan et al.
  • 1School of Highway, Chang’an University, 710064, Xi’an, China
  • 2School of Remote Sensing and Information Engineering, Wuhan University, 430079, Wuhan, China
  • 3State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 430079, Wuhan, China
  • 4Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA

Keywords: Global optimization, Building reconstruction, Roof topology, LiDAR, Point clouds

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.