地理空间先验引导的三维语义场景补全
Geospatial-Prior Guidance for 3D Semantic Scene Completion
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中文总结 AI 辅助
本文提出GeoScene框架，结合卫星图像与OpenStreetMap线索作为软先验，通过学习可靠性权重控制特征细化，在SemanticKITTI等数据集上提升了三维语义场景补全的几何与语义性能。
中文摘要 AI 辅助
从车载图像推断完整的三维几何结构与语义信息仍具挑战性，因为遮挡和受限视野会导致大量场景区域约束不足。尽管卫星图像能提供广域上下文，但仅外观线索的结构引导能力有限，且因空间或时间差异可能不可靠。本文提出GeoScene，一种地理空间引导框架，联合利用卫星图像与结构化OpenStreetMap线索作为三维语义场景补全的软先验。GeoScene学习车载观测与地理空间引导的逐体素互补可靠性权重，并用其控制观测与未观测区域的特征细化。该设计在保留局部视觉证据的同时，利用车载视野外的大规模道路与建筑结构。在SemanticKITTI和SSCBench-KITTI-360上的实验表明，在地理空间先验辅助设置下，GeoScene可持续提升几何与语义补全性能，对大规模静态及地理空间结构化类别的提升最为显著。
英文摘要
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.
