Zero‐shot point cloud segmentation for hydro power plant components

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.