To　improve　the　efficiency　and　accuracy　of　attention　mask　prediction　for　panoptic　segmentation　on　point　clouds,　an　end-to-end　point　cloud　panoptic　segmentation　network　model　guided　by　multi-modal　bird’s　eye　view　(BEV)　features　is　proposed.　First,　object　queries　are　generated　from　BEV　features　decoded　by　the　Transformer,　and　feature　enhancement　is　achieved　through　confidence　ranking　and　positional　encoding　embedding.　Second,　a　cross-attention　mechanism　module　is　constructed　to　fuse　object　queries　with　learnable　query　features　and　the　query　features　of　fused　object　instance　information　are　used　to　improve　the　accuracy　of　attention　mask　prediction.　Finally,　the　dimensionality　of　the　input　features　to　the　masked　attention　mechanism　network　is　reduced　to　enhance　detection　speed.　Experimental　results　based　on　the　nuScenes　dataset　indicate　that,　compared　with　the　baseline　method,　BEVGuide-PS　improves　panoptic　segmentation　metrics　PQ,　PQ†　,　RQ,　and　SQ　by　17.　7%,　17.　0%,　18.　3%,　and　20.　9%,　respectively,　reduces　inference　time　by　58.　4%,　and　significantly　enhances　training　efficiency.　©　2025　Universitat　zu　Koln.　All　rights　reserved.