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- W4320036871 abstract "One of the societal problems for current building construction projects is the lack of timely progress monitoring and quality control, causing over-budget costs, inefficient productivity, and poor performance. This paper addresses the challenge of high-accuracy primitive instance segmentation from point clouds with the support of IFC model as a core stage to facilitate maintaining a geometric digital twin during the construction stage. Keeping the geometry of a building digital twin dynamic and up-to-date will help monitor and control the progress and quality timely and efficiently. We propose a novel automatic method named Priseg to detect and segment the entire points corresponding to the as-designed instance by developing an IFC-based instance descriptor and unsupervised clustering algorithm. The proposed solution is robust in real complex environments, such as point clouds are noisy with high occlusions and clutter, the as-built status deviates from the as-designed model in terms of position, orientation, and scale." @default.
- W4320036871 created "2023-02-12" @default.
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- W4320036871 date "2023-01-01" @default.
- W4320036871 modified "2023-09-26" @default.
- W4320036871 title "PriSeg: IFC-Supported Primitive Instance Geometry Segmentation with Unsupervised Clustering" @default.
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- W4320036871 doi "https://doi.org/10.1007/978-3-031-25082-8_13" @default.
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