Aerial Content-Aware Sample-Adaptive Classifier for Urban Photogrammetric Point Cloud Segmentation

Urban photogrammetric point clouds are commonly processed as local 3D patches, limiting the context available for distinguishing geometrically similar categories. Orthographic projection of the same data yields a co-registered digital orthophoto map (DOM)/RGB-D representation that supports efficient large-context modeling but compresses vertical geometry. Existing projection--point methods mainly fuse these two representations at the feature level while retaining a classifier shared across all samples. We present the Aerial Content-Aware Sample-Adaptive Classifier (ACASAC), which complements feature fusion with decision-level adaptation. A point-aligned aerial region bridges the full aerial crop and local 3D patch. ACASAC models scene-to-local variation within the projection and the representation discrepancy between aligned 2D and 3D supports, then transfers both relations to refine the local point class representation. The refined representation, scene-level aerial context, and fixed fused-head prior jointly form a classifier for the current sample. ACASAC improves the fixed dual-representation head by 2.1 and 2.7 mIoU points on SensatUrban and UrbanBIS, respectively, and by 2.1--2.3 points across four tested encoder combinations.