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The reformulate city-scale 3D grounding as structured constraint reasoning, where description semantics are organized into computable cross-modal constraints over open-vocabulary 3D entities, attributes, and spatial relations, and introduces CitySTAR-3D, an enhanced benchmark that improves semantic coverage, instance completeness, bounding-box fidelity, and spatial-relation complexity in city-scale 3D grounding.
Achieving the best performance in 3D city asset generation, Sat2City v2 transforms satellite images into detailed, textured meshes, setting a new standard for urban simulation and geospatial intelligence.
GraspFoM reveals that leveraging 3D object priors can dramatically enhance both grasping accuracy and 3D reconstruction fidelity with minimal overhead.
Achieve significantly improved 3D reconstruction fidelity and layout plausibility in multi-object scenes by fusing multi-view observations with physics-aware constraints, all without requiring additional training.
