Combined Rule-Based and Hypothesis-Based Method for Building Model Reconstruction from Photogrammetric Point Clouds
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. Ai...
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Format: | Article |
Language: | English |
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MDPI AG
2021-03-01
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Series: | Remote Sensing |
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Online Access: | https://www.mdpi.com/2072-4292/13/6/1107 |
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doaj-8854071f2d254373b48519baaef135d4 |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Linfu Xie Han Hu Qing Zhu Xiaoming Li Shengjun Tang You Li Renzhong Guo Yeting Zhang Weixi Wang |
spellingShingle |
Linfu Xie Han Hu Qing Zhu Xiaoming Li Shengjun Tang You Li Renzhong Guo Yeting Zhang Weixi Wang Combined Rule-Based and Hypothesis-Based Method for Building Model Reconstruction from Photogrammetric Point Clouds Remote Sensing building models 3D reconstruction point clouds photogrammetry. |
author_facet |
Linfu Xie Han Hu Qing Zhu Xiaoming Li Shengjun Tang You Li Renzhong Guo Yeting Zhang Weixi Wang |
author_sort |
Linfu Xie |
title |
Combined Rule-Based and Hypothesis-Based Method for Building Model Reconstruction from Photogrammetric Point Clouds |
title_short |
Combined Rule-Based and Hypothesis-Based Method for Building Model Reconstruction from Photogrammetric Point Clouds |
title_full |
Combined Rule-Based and Hypothesis-Based Method for Building Model Reconstruction from Photogrammetric Point Clouds |
title_fullStr |
Combined Rule-Based and Hypothesis-Based Method for Building Model Reconstruction from Photogrammetric Point Clouds |
title_full_unstemmed |
Combined Rule-Based and Hypothesis-Based Method for Building Model Reconstruction from Photogrammetric Point Clouds |
title_sort |
combined rule-based and hypothesis-based method for building model reconstruction from photogrammetric point clouds |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-03-01 |
description |
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. |
topic |
building models 3D reconstruction point clouds photogrammetry. |
url |
https://www.mdpi.com/2072-4292/13/6/1107 |
work_keys_str_mv |
AT linfuxie combinedrulebasedandhypothesisbasedmethodforbuildingmodelreconstructionfromphotogrammetricpointclouds AT hanhu combinedrulebasedandhypothesisbasedmethodforbuildingmodelreconstructionfromphotogrammetricpointclouds AT qingzhu combinedrulebasedandhypothesisbasedmethodforbuildingmodelreconstructionfromphotogrammetricpointclouds AT xiaomingli combinedrulebasedandhypothesisbasedmethodforbuildingmodelreconstructionfromphotogrammetricpointclouds AT shengjuntang combinedrulebasedandhypothesisbasedmethodforbuildingmodelreconstructionfromphotogrammetricpointclouds AT youli combinedrulebasedandhypothesisbasedmethodforbuildingmodelreconstructionfromphotogrammetricpointclouds AT renzhongguo combinedrulebasedandhypothesisbasedmethodforbuildingmodelreconstructionfromphotogrammetricpointclouds AT yetingzhang combinedrulebasedandhypothesisbasedmethodforbuildingmodelreconstructionfromphotogrammetricpointclouds AT weixiwang combinedrulebasedandhypothesisbasedmethodforbuildingmodelreconstructionfromphotogrammetricpointclouds |
_version_ |
1724221206899458048 |
spelling |
doaj-8854071f2d254373b48519baaef135d42021-03-15T00:03:30ZengMDPI AGRemote Sensing2072-42922021-03-01131107110710.3390/rs13061107Combined Rule-Based and Hypothesis-Based Method for Building Model Reconstruction from Photogrammetric Point CloudsLinfu Xie0Han Hu1Qing Zhu2Xiaoming Li3Shengjun Tang4You Li5Renzhong Guo6Yeting Zhang7Weixi Wang8Research 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, ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaResearch 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, ChinaResearch 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, ChinaResearch 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, ChinaResearch 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, ChinaState Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaResearch 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, ChinaThree-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.https://www.mdpi.com/2072-4292/13/6/1107building models3D reconstructionpoint cloudsphotogrammetry. |