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Urbex introduces an agentic framework that reframes city-scale 3D object grounding as an active spatial search problem rather than relying on inefficient candidate ranking. By leveraging a Vision-Language Model agent to interact with multi-view environments and optimizing its tool-use policy via reinforcement learning, the method achieves superior accuracy and speed without requiring precomputed instance proposals. This approach demonstrates significant gains in Hit@1 and IoU metrics while improving cross-scene robustness for large outdoor datasets.
Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes
arXiv:2610.18356v1 Announce Type: cross Abstract: City-scale 3D grounding is commonly formulated as candidate ranking, which requires precomputed instance proposals and becomes inefficient in large outdoor scenes. We propose Urbex, an agentic vision-language framework that instead casts grounding as active spatial search. Urbex represents a city-scale 3D scene as an interactive multi-view environment, allowing a VLM agent to query landmarks, zoom into local regions, render oblique views, and commit a grounded bounding box. We optimize this tool-use policy with reinforcement learning on CityRefer, using smooth localization rewards and lightweight shaping rewards for efficient exploration. Experiments show that Urbex achieves the best Hit@1 and IoU on CityRefer, supports candidate-free grounding without ground-truth boxes, and is substantially faster than the strongest city-scale ranking baseline. Zero-shot results on STPLS3D-Refer further indicate improved cross-scene robustness.
Abstract:City-scale 3D grounding is commonly formulated as candidate ranking, which requires precomputed instance proposals and becomes inefficient in large outdoor scenes. We propose Urbex, an agentic vision-language framework that instead casts grounding as active spatial search. Urbex represents a city-scale 3D scene as an interactive multi-view environment, allowing a VLM agent to query landmarks, zoom into local regions, render oblique views, and commit a grounded bounding box. We optimize this tool-use policy with reinforcement learning on CityRefer, using smooth localization rewards and lightweight shaping rewards for efficient exploration. Experiments show that Urbex achieves the best Hit@1 and IoU on CityRefer, supports candidate-free grounding without ground-truth boxes, and is substantially faster than the strongest city-scale ranking baseline. Zero-shot results on STPLS3D-Refer further indicate improved cross-scene robustness.
NeurIPS 2026. 23 pages, 10 figures. Code: this https URL
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2610.18356 [cs.CV]
(or arXiv:2610.18356v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.18356 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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