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Project Details |
Funding Scheme : | Early Career Scheme | ||||||||||||||||||||||||||||||||||||||||||||||||
Project Number : | 27200520 | ||||||||||||||||||||||||||||||||||||||||||||||||
Project Title(English) : | From point cloud to building and city information model (BIM/CIM): A study of architectonic grammar optimization | ||||||||||||||||||||||||||||||||||||||||||||||||
Project Title(Chinese) : | 建築及城市信息模型的一類構造語法最優化方法 | ||||||||||||||||||||||||||||||||||||||||||||||||
Principal Investigator(English) : | Prof Xue, Fan | ||||||||||||||||||||||||||||||||||||||||||||||||
Principal Investigator(Chinese) : | |||||||||||||||||||||||||||||||||||||||||||||||||
Department : | Department of Real Estate and Construction | ||||||||||||||||||||||||||||||||||||||||||||||||
Institution : | The University of Hong Kong | ||||||||||||||||||||||||||||||||||||||||||||||||
E-mail Address : | xuef@hku.hk | ||||||||||||||||||||||||||||||||||||||||||||||||
Tel : | 3917 4174 | ||||||||||||||||||||||||||||||||||||||||||||||||
Co - Investigator(s) : |
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Panel : | Engineering | ||||||||||||||||||||||||||||||||||||||||||||||||
Subject Area : | Civil Engineering, Surveying, Building & Construction | ||||||||||||||||||||||||||||||||||||||||||||||||
Exercise Year : | 2020 / 21 | ||||||||||||||||||||||||||||||||||||||||||||||||
Fund Approved : | 635,569 | ||||||||||||||||||||||||||||||||||||||||||||||||
Project Status : | Completed | ||||||||||||||||||||||||||||||||||||||||||||||||
Completion Date : | 31-12-2023 | ||||||||||||||||||||||||||||||||||||||||||||||||
Project Objectives : |
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Abstract as per original application (English/Chinese): |
本項目旨在研究一類系統化化的方法,將三維點雲數據重建為建築及城市信息模型(BIM/CIM)。構造語法(architectonic grammar)不僅普遍存在于建築設計語言中,也是建築及城市信息模型的數據語言的基本標準。本研究將以構造語法最優化的視角來探索模型重建的問題。首先,定義構造語法來反應建築及城市信息模型,主要包括多種構件模型在參數化建模配置中的語義(例如類型、尺寸及顔色)及其文法規則(例如拓撲關係及多樣性)。然後,將構造語法設計映射至點雲數據匹配,建立一類非綫性優化數學模型,從而剋服點雲中的噪音、遮蔽及雜物等數據缺陷。第三步,研究運用高階無導數優化算法計算該非綫性優化問題,求解出最符合輸入數據和構造語法集的參數化構造方案。最終,最優化的參數化構造方案將剋服點雲數據缺陷,並自動重建出準確的建築及城市信息模型。研究成果將為處理非結構化點雲數據的相關研究和實踐提供一種新的自動化手段,並能為建築和建設的課堂教學提供前沿討論案例。 |
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Realisation of objectives: | 5.1 Scope of investigation -- Building/city information modeling (BIM/CIM) is vital to smart and sustainable development in the digital twin era. However, the BIM/CIM of existing buildings is hindered by the imperfections of 3D measurement data, such as noises, occlusions, and clutter in point clouds. The research team notices that buildings/cities' innate architectonic designs may overcome some data imperfections. -- This project studies architectonic grammar optimization methods to find the best design for BIM/CIM reconstruction. -- We define a nonlinear problem of architectonic grammar by integrating the global features (e.g., symmetry, enclosure, and urban patterns) and local features (e.g., local section backbones, deep learning-based semantics) in architectonic grammar and conventional 3D geometric error metrics. -- We apply heuristic optimization, DFO (derivative-free optimization), and deep learning algorithms to realize an automatic “best-fit” BIM/CIM reconstruction and apply the grammar/skeleton-guided model back to BIM/CIM to analyze and improve the overall performance. 5.2 Objectives achieved -- Objective 1 was achieved [100%]. The focused architectonic grammar includes global features and local features, sometimes the shape grammars can be inherited from established data sources according to [4]. We have identified and presented the foundation, example grammar, three global features, and two local features in the following publications: o Paper [9] [Note: #9 in Sect. 8]: A novel Building Section Skeleton theory as a foundation of architectonic grammar. o Paper [10]: A Symmetric Skeleton Grammar was defined and formulated. o Optional architectonic features for the grammar: -->> Paper [5]: Architectural symmetry for BIM/CIM, which won a Merit Award of Research in The Hong Kong OpenBIM / OpenGIS Awards 2022, -->> [Award #3 in Sect. 11]: enclosure of indoor space for BIM, -->> [3]: acoustic patterns of indoor settings for BIM, -->> Papers [2,8,11]: deep transfer learning-based semantics -->> Objective 2 was achieved sustainably [95%]. In addition to the general architectonic grammar optimization formulation in the proposal, Chapter [1] also defines a level of automation for architectonic optimization in the case of historical BIM. Papers [7, 10] extended the general formulation to building floor plan and individual building elements. Conference papers [3, 4, 2, 7] showcased the extended formulations for further BIM/CIM scenarios. -- Objective 3 was achieved sustainably [90%]. Papers [3,5,10] have demonstrated that DFO algorithms can successfully solve the architectonic optimization and its global/local features. Papers [2,8,11] also confirmed deep learning can be transferred for semantic segmentation for BIM/CIM, in terms of both 2D images and 3D point clouds for BIM/CIM reconstruction. -- Objective 4 was achieved [100%]. The approach was integrated and applied to several related problems. First, Paper [9] presented a state-of-the-art (SOTA) BIM/CIM compact reconstruction that bested all existing methods in terms of error and model compactness. The integrated approach has been piloted in a taught MSc course RECO7613 in 2021-2024, and in BSc course RECO2041 in 2022-2024. Grammar-guided parametric 3D reconstruction was taught in a popular gamified education environment, Minecraft Education Edition (Conference paper [6]). Our BIM reconstruction methods on architectonic regularities and enclosure won 6 awards from 2nd to 4th International Scan-to-BIM Challenge, CVPR 2022-2024[Awards 3, 10, 13 in Sect. 11]. Some papers have opened their source codes to the public via HKU Library and GitHub. 5.3 Deviations from the original plan -- There was a minor deviation in achieving Objective 3. We realized not only DFO algorithms but also heuristics optimization and deep learning algorithms for solving the architectonic grammar optimization formulations. The reason was that the optimization would be too sophisticated to be solved solely by DFO, if the scale of 3D point clouds went large. 5.4 Extensions and success beyond the original plan Apart from the successful execution of the project, we also looked into a few topics related to the project. -- Journal papers [2,11] proposed a 3D window view assessment based on CIM. -- Chapters [6, 1] extended the architectonic concept to digital heritage. -- Journal papers [10,8] applied the symmetric skeleton grammar and associated deep features to BIM design and details reconstruction. | ||||||||||||||||||||||||||||||||||||||||||||||||
Summary of objectives addressed: |
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Research Outcome | |||||||||||||||||||||||||||||||||||||||||||||||||
Major findings and research outcome: | 6.1.1 Contributions to the body of knowledge -- A new Building Section Skeleton (BSS) theory [9]. -- Two literature reviews on reconstruction and semantic enrichment to BIM/CIM [3,4], which attracted >100 citations -- New BIM/CIM reconstruction methods (RegARD, SSG--MMO) [5, 10] and deep learning dataset (HRHD--HK) [conf. 5], which attracted >70 citations -- Algorithmic experiments and sensitivity analysis for various (modern, rural, heritage, artefacts, and infrastructure in Hong Kong) real-world buildings and urban areas. -- Extended skeletons and grammars were applied to improve compact BIM/CIM reconstruction, city’s 3D window view, building energy performance, digital heritage, etc. 6.1.2 Research outcomes: With citations, impact factors, and honors 8 journal papers, which attracted >250 citations -- [2] Citation = 40 (as of Jan 2025) Journal impact factor = 7.9 (JCR 2024) -- [4] Citation = 104 Journal impact factor = 8.0 -- [5] Citation = 54 Journal impact factor = 4.2 -- [7] Citation = 2 Journal impact factor = 9.6 -- [8] Citation = 24 Journal impact factor = 9.6 -- [9] Citation = 5 Journal impact factor = 10.6 -- [10] Citation = 16 Journal impact factor = 6.6 -- [11] Citation = 14 Journal impact factor = 7.9 16 International/local research awards received: -- Please refer to Section 12. Note that each entry of #3, 10, and 13 contains two awards. 8 International conference papers; 6 invited lectures for HKPolyU, Tsinghua, Huazhong Univ of Science and Tech, South China Univ of Tech, Tongji, and Leica Geosciences between 2021 and 2024. | ||||||||||||||||||||||||||||||||||||||||||||||||
Potential for further development of the research and the proposed course of action: |
The findings of the project have successfully enabled a TRS project, a CRF project, one ITF project, a Shenzhen-Hong Kong-Macau TRP project, and a Guangdong Natural Science Funding project: -- TRS (T22-504/21-R, HK$34M) on generative urban design facilitated by optimization. -- CRF (C7080-22GF) on generative building design facilitated by optimization -- ITF ITSP platform project (2019B010151001) (PC, HK$ 5.2 million) -- Shenzhen-Hong Kong-Macau TRP project (PI, HK$ 1,26million), -- Guangdong Natural Science Funding (PI, HK$ 0.1 million) In addition, the concept, methodology, cases, and findings have been taught in a set of BSc and MSc courses (RECO2041, RECO3032, RECO4012, RECO7097, RECO7613) at the University of Hong Kong. | ||||||||||||||||||||||||||||||||||||||||||||||||
Layman's Summary of Completion Report: | The research project aimed to enhance building/city information modeling (BIM/CIM) reconstruction from 3D point clouds for smart and sustainable development. We focused on resolving the imperfections in existing buildings' 3D point cloud data using architectnoic grammar. The team studied a fundamental theory of 'building section skeleton.' We then investigated architectonic grammar optimization and studied global features like urban patterns and local features like deep learning-based semantics. We combined derivative-free optimization, heuristic optimization, and deep learning algorithms to achieve an automatic "best-fit" BIM/CIM reconstruction. Key objectives were met. The outcomes were validated through experiments in different fields, peer-reviewed as academic publications (8 journal papers), integrated into educational courses, and attracted 16 peer-reviewed awards. The project also extended its impact from BIM/CIM to digital heritage and 3D window view assessment. In summary, the funded project outcomes can contribute to the body of knowledge with new theories, methods, and datasets. | ||||||||||||||||||||||||||||||||||||||||||||||||
Research Output | |||||||||||||||||||||||||||||||||||||||||||||||||
Peer-reviewed journal publication(s) arising directly from this research project : (* denotes the corresponding author) |
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Recognized international conference(s) in which paper(s) related to this research project was/were delivered : |
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Other impact (e.g. award of patents or prizes, collaboration with other research institutions, technology transfer, etc.): |
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Realisation of the education plan: |
SCREEN ID: SCRRM00542 |