Reconstruction of Complex Roof Semantic Structures from 3D Point Clouds Using Local Convexity and Consistency

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ID: 272643
2021
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Abstract
Three-dimensional (3D) building models are closely related to human activities in urban environments. Due to the variations in building styles and complexity in roof structures, automatically reconstructing 3D buildings with semantics and topology information still faces big challenges. In this paper, we present an automated modeling approach that can semantically decompose and reconstruct the complex building light detection and ranging (LiDAR) point clouds into simple parametric structures, and each generated structure is an unambiguous roof semantic unit without overlapping planar primitive. The proposed method starts by extracting roof planes using a multi-label energy minimization solution, followed by constructing a roof connection graph associated with proximity, similarity, and consistency attributes. Furthermore, a progressive decomposition and reconstruction algorithm is introduced to generate explicit semantic subparts and hierarchical representation of an isolated building. The proposed approach is performed on two various datasets and compared with the state-of-the-art reconstruction techniques. The experimental modeling results, including the assessment using the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark LiDAR datasets, demonstrate that the proposed modeling method can efficiently decompose complex building models into interpretable semantic structures.
Reference Key
hu2021remotereconstruction Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Pingbo Hu;Yiming Miao;Miaole Hou;Hu, Pingbo;Miao, Yiming;Hou, Miaole;
Journal remote sensing
Year 2021
DOI
10.3390/rs13101946
URL
https://www.mdpi.com/2072-4292/13/10/1946
Keywords
lidar compound building reconstruction point clouds semantic decomposition

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