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Dynamically loading IFC models on a web browser based on spatial semantic partitioning

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摘要 Industry foundation classes(IFC)is an open and neutral data format specification for building information modeling(BIM)that plays a crucial role in facilitating interoperability.With increases in web-based BIM applications,there is an urgent need for fast loading large IFC models on a web browser.However,the task of fully loading large IFC models typically consumes a large amount of memory of a web browser or even crashes the browser,and this significantly limits further BIM applications.In order to address the issue,a method is proposed for dynamically loading IFC models based on spatial semantic partitioning(SSP).First,the spatial semantic structure of an input IFC model is partitioned via the extraction of story information and establishing a component space index table on the server.Subsequently,based on user interaction,only the model data that a user is interested in is transmitted,loaded,and displayed on the client.The presented method is implemented via Web Graphics Library,and this enables large IFC models to be fast loaded on the web browser without requiring any plug-ins.When compared with conventional methods that load all IFC model data for display purposes,the proposed method significantly reduces memory consumption in a web browser,thereby allowing the loading of large IFC models.When compared with the existing method of spatial partitioning for 3D data,the proposed SSP entirely uses semantic information in the IFC file itself,and thereby provides a better interactive experience for users.
出处 《Visual Computing for Industry,Biomedicine,and Art》 2019年第1期26-37,共12页 工医艺的可视计算(英文)
基金 The study was supported by the National Key R&D Program of China(No.2018YFB0505400) the National Natural Science Foundation of China(No.61472202) the Special Scientific Research Fund of China Railway Corporation(No.J2017X010) the Research on Key Technologies of Virtual Engineering of Railway Engineering Unit Based on BIM Technology(No.K2018G055).
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