[Background]　Traditional　methods,　due　to　their　static　receptive　field　design,　struggle　to　adapt　to　the　significant　scale　differences　among　cars,　pedestrians,　and　cyclists　in　urban　autonomous　driving　scenarios.　Moreover,　cross-scale　feature　fusion　often　leads　to　hierarchical　interference.　[Methodology]　To　address　the　key　challenge　of　cross-scale　representation　consistency　in　3D　object　detection　for　multi-class,　multi-scale　objects　in　autonomous　driving　scenarios,　this　study　proposes　a　novel　method　named　VoxTNT.　VoxTNT　leverages　an　equalized　receptive　field　and　a　local-global　collaborative　attention　mechanism　to　enhance　detection　performance.　At　the　local　level,　a　PointSetFormer　module　is　introduced,　incorporating　an　Induced　Set　Attention　Block　(ISAB)　to　aggregate　fine-grained　geometric　features　from　high-density　point　clouds　through　reduced　cross-attention.　This　design　overcomes　the　information　loss　typically　associated　with　traditional　voxel　mean　pooling.　At　the　global　level,　a　VoxelFormerFFN　module　is　designed,　which　abstracts　non-empty　voxels　into　a　super-point　set　and　applies　cross-voxel　ISAB　interactions　to　capture　long-range　contextual　dependencies.　This　approach　reduces　the　computational　complexity　of　global　feature　learning　from　O(N2)　to　O(M2)　(where　M　©　2025　Science　Press.　All　rights　reserved.