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An Optimized Modeling Method Based on
  Hierarchical Reconstruction for Three⁃
     dimensional Building Point Cloud

         Lin Sun          Guanghong Gong∗               Ying Li
 School of Automation   School of Automation    School of Automation

      Science and             Science and             Science and
Electrical Engineering  Electrical Engineering  Electrical Engineering

  Beihang University      Beihang University      Beihang University
     Beijing, China         Beijing, China          Beijing, China

sunlin_2019@ 163.com

ABSTRACT
In this paper, we propose an optimized modeling method based on hierarchical reconstruction
for 3D building point cloud. In contrast to existing approaches, this method can make full use
of the geometric information of the original building facade point cloud and roof point cloud,
which will provide a more complete description for the model of buildings. Firstly, we segment
and extract the facade and the roof point cloud of the building based on the difference in planar
slices. Further, we use the P⁃Linkage algorithm to segment the facade point cloud into planar
patches,and cluster the planar patches to layer the facade point cloud. Therefore, we reduce
the complexity of reconstruction from threedimensional to two⁃dimensional. Then we reconstruct
the building facade based on the hierarchical reconstruction method and the building roof based
on the block reconstruction method. Finally, we merge the facade model and roof model and
clip unnecessary polygons, leading to the final model. We experiment with different data and
the results compared with the commercial modeling software show that the method is feasible
and can improve the three⁃dimensional building modeling effect.
CCS CONCEPTS
·Computing methodologies ·Computer graphics ·Shape modeling ·Parametric curve and
surface models

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      CSAE 2019, October 22-24, 2019, Sanya, China
      © 2019 Association for Computing Machinery.
      ACM ISBN 978-1-4503-6294-8 / 19 / 10 $ 15.00
      https: / / doi.org / 10.1145 / 3331453.3361677

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