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Article

Combined Rule-Based and Hypothesis-Based Method for Building Model Reconstruction from Photogrammetric Point Clouds

1
Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University & Key Laboratory of Urban Land Resources Monitoring and Simulation, MNR & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen 518060, China
2
State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
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Department of Land Surveying & Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon 999077, Hong Kong
4
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
*
Author to whom correspondence should be addressed.
Academic Editor: Ben Gorte
Remote Sens. 2021, 13(6), 1107; https://doi.org/10.3390/rs13061107
Received: 19 January 2021 / Revised: 9 March 2021 / Accepted: 11 March 2021 / Published: 14 March 2021
(This article belongs to the Special Issue 3D City Modelling and Change Detection Using Remote Sensing Data)
Three-dimensional (3D) building models play an important role in digital cities and have numerous potential applications in environmental studies. In recent years, the photogrammetric point clouds obtained by aerial oblique images have become a major source of data for 3D building reconstruction. Aiming at reconstructing a 3D building model at Level of Detail (LoD) 2 and even LoD3 with preferred geometry accuracy and affordable computation expense, in this paper, we propose a novel method for the efficient reconstruction of building models from the photogrammetric point clouds which combines the rule-based and the hypothesis-based method using a two-stage topological recovery process. Given the point clouds of a single building, planar primitives and their corresponding boundaries are extracted and regularized to obtain abstracted building counters. In the first stage, we take advantage of the regularity and adjacency of the building counters to recover parts of the topological relationships between different primitives. Three constraints, namely pairwise constraint, triplet constraint, and nearby constraint, are utilized to form an initial reconstruction with candidate faces in ambiguous areas. In the second stage, the topologies in ambiguous areas are removed and reconstructed by solving an integer linear optimization problem based on the initial constraints while considering data fitting degree. Experiments using real datasets reveal that compared with state-of-the-art methods, the proposed method can efficiently reconstruct 3D building models in seconds with the geometry accuracy in decimeter level. View Full-Text
Keywords: building models; 3D reconstruction; point clouds; photogrammetry building models; 3D reconstruction; point clouds; photogrammetry
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MDPI and ACS Style

Xie, L.; Hu, H.; Zhu, Q.; Li, X.; Tang, S.; Li, Y.; Guo, R.; Zhang, Y.; Wang, W. Combined Rule-Based and Hypothesis-Based Method for Building Model Reconstruction from Photogrammetric Point Clouds. Remote Sens. 2021, 13, 1107. https://doi.org/10.3390/rs13061107

AMA Style

Xie L, Hu H, Zhu Q, Li X, Tang S, Li Y, Guo R, Zhang Y, Wang W. Combined Rule-Based and Hypothesis-Based Method for Building Model Reconstruction from Photogrammetric Point Clouds. Remote Sensing. 2021; 13(6):1107. https://doi.org/10.3390/rs13061107

Chicago/Turabian Style

Xie, Linfu, Han Hu, Qing Zhu, Xiaoming Li, Shengjun Tang, You Li, Renzhong Guo, Yeting Zhang, and Weixi Wang. 2021. "Combined Rule-Based and Hypothesis-Based Method for Building Model Reconstruction from Photogrammetric Point Clouds" Remote Sensing 13, no. 6: 1107. https://doi.org/10.3390/rs13061107

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