Aiming　at　the　segmentation　and　extraction　of　the　main　part　of　substation　equipment,　we　use　Fast　Point　Feature　Histograms　(FPFH)　and　Locally　Convex　Connected　Patches　(LCCP)　to　obtain　voxels’　integrated　geometric　features,　then　aggregate　these　features　and　their　K　nearest　neighbors’　on　voxels　to　build　multi-level　voxels’　features　by　bottom-up　hierarchy,　and　achieve　pre-segmentation　of　shapes　with　the　flow-constrained　super-voxel　clustering　algorithm;　After　the　pre-segmentation,　we　conduct　shape　analysis　to　extract　semantically　meaningful　instances　of　equipment　components,　achieving　part-level　point　cloud　data　instance　extraction　of　artificial　equipment　geometric　features.　Without　training　data　or　manual　annotations,　the　work　presented　is　simple　and　easy　to　implement.　It　can　merge　patches　across　surface-singularities.　It　needs　a　few　parameters,　can　achieve　automatic　3D　instance　extraction　from　point　clouds　for　different　scenes　with　the　same　or　similar　parameters.　©　2025　SPIE.