Geospatial-Prior Guidance for 3D Semantic Scene Completion

• URL: http://arxiv.org/abs/2608.03618v1
• Date: Tue, 04 Aug 2026 13:08:14 GMT
• Title: Geospatial-Prior Guidance for 3D Semantic Scene Completion
• Abstract summary: GeoScene is a framework that jointly uses satellite imagery and structured OpenStreetMap cues as soft priors for 3D semantic scene completion.<n>GeoScene learns complementary voxel-wise reliability weights for onboard observations and geospatial guidance, and uses them to control feature refinement in observed and unobserved regions.
• Score: 38.43604650752214
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inferring complete 3D geometry and semantics from onboard images remains challenging because occlusions and restricted fields of view leave large scene regions underconstrained. Although satellite imagery provides wide-area context, appearance cues alone offer limited structural guidance and may be unreliable because of spatial or temporal discrepancies. We present GeoScene, a geospatially guided framework that jointly uses satellite imagery and structured OpenStreetMap cues as soft priors for 3D semantic scene completion. GeoScene learns complementary voxel-wise reliability weights for onboard observations and geospatial guidance, and uses them to control feature refinement in observed and unobserved regions. This design preserves local visual evidence while exploiting large-scale road and building structure beyond onboard visibility. Experiments on SemanticKITTI and SSCBench-KITTI-360 demonstrate that GeoScene consistently improves both geometric and semantic completion under the geospatial-prior-assisted setting, with the most pronounced benefits for large-scale static and geospatially structured classes.

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