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Biao He

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Proceedings Article | 23 January 2024 Paper

A high-efficiency data compression and visualization method for large complex BIM model

Aowei Tang, Xi Kuai, Biao He, Hai Xu, Xing Hao Zhou, Heng Zhang

Proceedings Volume 12978, 129781P (2024) https://doi.org/10.1117/12.3019423

KEYWORDS: Data modeling, Visual process modeling, Visualization, 3D modeling, Data conversion, Semantics, Cameras, Data storage, Visibility, Geographic information systems

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Building information modeling (BIM), with the accurate representation of building geometry structure and component composition, has played a significant role in digital twin development and city information modeling (CIM). Owing to the huge volume of data and complex reference relationships between BIM components, there are few applications that can efficiently transfer, store, and smoothly visualize BIM models. In this study, we propose an efficient BIM data compression and visualization method based on instancing storage and scene hierarchy instanced culling (SHIC) instancing rendering. We identify instanced BIM models based on the IFC semantic information and spatial geometry calculations and store them as instances. To avoid CPU-Bound, we partition the instanced BIM models space into clusters and calculate the visibility set with it as a culling unit. We implemented our method on Unreal Engine 5(UE5), used China Resources Building as experimental data, and a series of experiments were designed to illustrate the advantages of our method in terms of data compression and rendering efficiency. The results show that our method is effective in data compression and keeps a higher frame rate for visualization.

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