Geospatial-Prior Guidance for 3D Semantic Scene Completion

2026-08-04

AI Summary

This work addresses the challenge of reliably inferring complete 3D geometry and semantics from in-vehicle images, which is hindered by occlusions, limited field of view, and insufficient constraints in unobserved regions. To this end, the authors propose GeoScene, a novel framework that, for the first time, incorporates structured geospatial priors—such as road and building layouts derived from satellite imagery and OpenStreetMap—as learnable soft constraints. The method adaptively fuses local visual observations with global structural knowledge through voxel-wise reliability weighting and introduces a prior-guided feature refinement network. Evaluated on SemanticKITTI and SSCBench-KITTI-360, GeoScene significantly advances 3D semantic scene completion performance, particularly excelling on large-scale static objects and geographically structured categories.

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.

Problem

• 3D semantic scene completion
• occlusions
• geospatial prior
• limited field of view
• scene underconstrained

Innovation

• geospatial prior
• 3D semantic scene completion
• satellite imagery
• OpenStreetMap
• voxel-wise reliability weighting