Abstract

The complexity and variety of buildings and the defects of point cloud data are the main challenges faced by 3D urban reconstruction from point clouds, especially in metropolitan areas. In this paper, we developed a method that embeds multiple relations into a procedural modelling process for the automatic 3D reconstruction of buildings from photogrammetric point clouds. First, a hybrid tree of constructive solid geometry and boundary representation (CSG-BRep) was built to decompose the building bounding space into multiple polyhedral cells based on geometric-relation constraints. The cells that approximate the shapes of buildings were then selected based on topological-relation constraints and geometric building models were generated using a reconstructing CSG-BRep tree. Finally, different parts of buildings were retrieved from the CSG-BRep trees, and specific surface types were recognized to convert the building models into the City Geography Markup Language (CityGML) format. The point clouds of 105 buildings in a metropolitan area in Hong Kong were used to evaluate the performance of the proposed method. Compared with two existing methods, the proposed method performed the best in terms of robustness, regularity, and topological correctness. The CityGML building models enriched with semantic information were also compared with the manually digitized ground truth, and the high level of consistency between the results suggested that the produced models will be useful in smart city applications.

Highlights

  • Point clouds, either from laser scanning or photogrammetric processing, are the main data sources for three-dimensional (3D) urban reconstruction

  • The City Geography Markup Language (CityGML) building models enriched with semantic information were compared with the manually digitized ground truth, and the high level of consistency between the results suggested that the produced models will be useful in smart city applications

  • We briefly review previous studies related to the CityGML [19] building models, which are the final output of the method in this paper

Read more

Summary

Introduction

Either from laser scanning or photogrammetric processing, are the main data sources for three-dimensional (3D) urban reconstruction. Aerial laser scanning (ALS) point clouds can provide accurate 3D information about large-scale scenes and have been widely used for 3D building reconstruction in urban areas [1,2,3]. Vertical surfaces (such as building façades) are often completely missing from the ALS data, resulting in a huge loss of vertical information. Many previous studies focused only on the reconstruction of building rooftops [4,5] and produced 2.5D building models [6,7]. Because of occlusions between closely distributed high-rise buildings, missing partial data can remain a problem for the automatic generation of true 3D building models [10]

Objectives
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call