In　recent　years,　large-scale　point　cloud　semantic　segmentation　has　been　widely　applied　in　various　fields,　such　as　remote　sensing　and　autonomous　driving.　Most　existing　point　cloud　networks　use　local　aggregation　to　abstract　unordered　point　clouds　layer　by　layer.　Among　these,　position　embedding　serves　as　a　crucial　step.　However,　current　methods　of　position　embedding　have　limitations　in　modeling　spatial　relationships,　especially　in　deeper　encoders　where　richer　spatial　positional　relationships　are　needed.　To　address　these　issues,　this　paper　summarizes　the　advantages　and　disadvantages　of　mainstream　position　embedding　methods　and　proposes　a　novel　Hybrid　Offset　Position　Encoding　(HOPE)　module.　This　module　comprises　two　branches　that　compute　relative　positional　encoding　(RPE)　and　offset　positional　encoding　(OPE).　RPE　combines　explicit　encoding　to　enhance　position　features　through　attention,　learning　position　bias　implicitly,　while　OPE　calculates　absolute　position　offset　encoding　by　considering　differences　with　grouping　embeddings.　These　two　encodings　are　adaptively　mixed　in　the　final　output.　The　experiment　conducted　on　multiple　datasets　demonstrates　that　our　module　helps　the　deep　encoders　of　the　network　capture　more　robust　features,　thereby　improving　model　performance　on　various　baseline　models.　For　instance,　PointNet++　and　PointMetaBase　enhanced　with　HOPE　achieved　mIoU　gains　of　2.1%　and　1.3%　on　the　large-scale　indoor　dataset　S3DIS　area-5,　2.5%　and　1.1%　on　S3DIS　6-fold,　and　1.5%　and　0.6%　on　ScanNet,　respectively.　RandLA-Net　with　HOPE　achieved　a　1.4%　improvement　on　the　large-scale　outdoor　dataset　Toronto3D,　all　with　minimal　additional　computational　cost.　PointNet++　and　PointMetaBase　had　approximately　only　a　0.1　M　parameter　increase.　This　module　can　serve　as　an　alternative　for　position　embedding,　and　is　suitable　for　point-based　networks　requiring　local　aggregation.　©　2025　by　the　authors.