Three-dimensional segmentation decomposes complex power environments into semantic and instance-level regions, enabling precise spatial awareness for power robots in automated and safer live-line operations. However, existing methods face high 3D data acquisition costs and limited real-time processing, hindering practical deployment. To address these challenges, we introduce the first low-cost, rapid 3D segmentation framework using only multi-view monocular images and text prompts, without requiring 3D sensors. Specifically, we leverage Grounded-SAM2’s robust generalization and a custom color-annotation scheme to produce RGB segmentation masks for each view. These masks are then fed into the Spann3r reconstruction model, fusing geometry and color to reconstruct a dense scene point cloud. Candidate point sets corresponding to power equipment are extracted via color filtering, and a density-constrained DBSCAN clustering step separates individual instances while suppressing noise. Experimental validation in real-world power scenarios shows that, using only monocular RGB imagery, our method achieves over 92.6% mean intersection over union (mIoU) and over 93.4% mean accuracy (mAcc) for 3D segmentation. It offers the low-cost, and fast solution for the intelligent operation and maintenance of power robots without 3D sensor support.