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From point cloud to building and city information model (BIM/CIM): A study of architectonic grammar optimization

Goal: To process 3D point cloud via automated architectonic grammars and composition

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Fan Xue
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[Free PDF:🌐 https://authors.elsevier.com/a/1fIQhcUG5OuM6 🌐] Every windowed room has a view, which reflects the visibility of nature and landscape and has a strong influence on the health, living satisfaction, and housing value of inhabitants. Thus, automatic accurate window view assessment is vital in examining neighborhood landscape and optimizing the social and physical settings for sustainable urban development. However, existing methods are labor-intensive, inaccurate, and non-scalable to assess window views in high-rise, high-density cities. This study aims to assess Window View Indices (WVIs) quantitatively and automatically by using a photo-realistic City Information Model (CIM). First, we define four WVIs to represent the outside (i) greenery, (ii) water-body, (iii) sky, and (iv) construction views quantitatively. Then, we propose a deep transfer learning method to estimate the WVIs for the window views captured in the CIM. Preliminary experimental tests in Wan Chai District, Hong Kong confirmed that our method was highly satisfactory (R2 > 0.95) and fast (3.08 s per view), and the WVIs were accurate (RMSE < 0.042). The proposed approach can be used in computing city-scale window views for landscape management, sustainable urban planning and design, and real estate valuation.
Fan Xue
added a research item
Building information modeling (BIM) of cultural heritages, i.e., historic building information modeling (HBIM), advances the monitoring, maintenance, restoration, and virtual exhibitions of historical buildings. However, due to the elaborate styles and the unavoidable erosion and renovation, the reconstruction of HBIM from the prevalent raw data, such as point clouds and images, is very challenging, especially parametrical and semantic modeling. Recent studies have noticed the potential of architectonic grammar for facilitating parametric and semantic reconstruction. This paper investigates the manual modeling of cultural heritage with the architectonic grammar and proposes a roadmap consisting of four levels of automation, i.e., ‘calibration,’ ‘selection,’ ‘combination,’ and ‘generation,’ of the architectonic grammar reconstruction. Further quality improvement and cost analysis of these four levels show that ‘calibration’ and ‘selection’ are the most suitable options currently for real-world applications. This study inspires the future application of architectonic grammar to facilitate the parametric and semantic HBIM reconstruction and explores the prospect of a new HBIM reconstruction schema.
Fan Xue
added a research item
Coarse registration of 3D point clouds plays an indispensable role for parametric, semantically rich, and realistic digital twin buildings (DTBs) in the practice of GIScience, manufacturing, robotics, architecture, engineering, and construction. However, the existing methods have prominently been challenged by (i) the high cost of data collection for numerous existing buildings and (ii) the computational complexity from self-similar layout patterns. This paper studies the registration of two low-cost data sets, i.e., colorful 3D point clouds captured by smartphones and 2D CAD drawings, for resolving the first challenge. We propose a novel method named `Registration based on Architectural Reflection Detection’ (RegARD) for transforming the self-symmetries in the second challenge from a barrier of coarse registration to a facilitator. First, RegARD detects the innate architectural reflection symmetries to constrain the rotations and reduce degrees of freedom. Then, a nonlinear optimization formulation together with advanced optimization algorithms can overcome the second challenge. As a result, high-quality coarse registration and subsequent low-cost DTBs can be created with semantic components and realistic appearances. Experiments showed that the proposed method outperformed existing methods considerably in both effectiveness and efficiency, i.e., 49.88% less error and 73.13% less time, on average. The RegARD presented in this paper first contributes to coarse registration theories and exploitation of symmetries and textures in 3D point clouds and 2D CAD drawings. For practitioners in the industries, RegARD offers a new automatic solution to utilize ubiquitous smartphone sensors for massive low-cost DTBs.
Fan Xue
added a research item
Building Information Models (BIMs) and City Information Models (CIMs) have flourished in building and urban studies independently over the past decade. Semantic enrichment is an indispensable process that adds new semantics such as geometric, non-geometric, and topological information into existing BIMs or CIMs to enable multidisciplinary applications in fields such as construction management, geoinformatics, and urban planning. These two paths are now coming to a juncture for integration and juxtaposition. However, a critical review of the semantic enrichment of BIM and CIM is missing in the literature. This research aims to probe into semantic enrichment by comparing its similarities and differences between BIM and CIM over a ten-year time span. The research methods include establishing a uniform conceptual model, and sourcing and analyzing 44 pertinent cases in the literature. The findings plot the terminologies, methods, scopes, and trends for the semantic enrichment approaches in the two domains. With the increasing availability of data sources, algorithms, and computing power, they cross the border to enter each other's domain. Future research will likely gain new momentums from the demands of value-added applications, development of remote sensing devices, intelligent data processing algorithms, interoperability between BIM and CIM software platforms, and emerging technologies such as big data analytics.
Fan Xue
added a research item
Building information modeling (BIM) of cultural heritages, i.e., historic building information modeling (HBIM), advances the monitoring, maintenance, restoration, and virtual exhibitions of historical buildings. However, due to the elaborate styles and the unavoidable erosion and renovation, the reconstruction of HBIM from the prevalent raw data, such as point clouds and images, is very challenging, especially parametrical and semantic modeling. Recent studies have noticed the potential of architectonic grammar for facilitating parametric and semantic reconstruction. In this paper, we investigate the manual modeling of cultural heritage with the architectonic grammar and propose a roadmap consisting of four levels of automation, i.e., ‘calibration,’ ‘selection,’ ‘combination,’ and ‘generation,’ of the architectonic grammar reconstruction. Further quality improvement and cost analysis of these four levels show that ‘calibration’ and ‘selection’ are the most suitable options currently for real-world applications. This study inspires the future application of architectonic grammar to facilitate the parametric and semantic HBIM reconstruction and explores the prospect of a new HBIM reconstruction schema.
Fan Xue
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Fan Xue
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To process 3D point cloud via automated architectonic grammars and composition