Su, Yang; Chen, Weiwei; Ling, Jiaxin; Yu, Diran; (2025) Zero‐shot point cloud segmentation for hydro power plant components. Computer-Aided Civil and Infrastructure Engineering 10.1111/mice.70150. (In press).

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Abstract

Accurate 3D segmentation of hydro power plant (HPP) components from point cloud data is essential for building high‐fidelity digital twin systems that enable automation in construction, monitoring, and maintenance. However, existing point cloud segmentation methods suffer from high annotation costs. To address these challenges, a novel fully automated segmentation framework is proposed that assigns 3D semantic labels directly from unannotated point cloud data using only a textual prompt, without prior training on HPP‐specific data. Experiments on six real‐world HPP scenarios demonstrate that it achieves superior performance compared to state‐of‐the‐art zero‐shot baselines, with an average positive ratio of 72.56% and negative ratio of 20.45%, while significantly reducing the human effort and time required for segmentation. This study advances automation in construction by providing a practical, annotation‐free solution for large‐scale, fine‐grained 3D segmentation of complex HPP environments, laying the foundation for efficient, intelligent digital twin creation and automated decision support in hydropower engineering.

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