{"slug": "wildcity-a-real-world-city-scale-testbed-for-rendering-simulation-and-spatial", "title": "WildCity: A Real-World City-Scale Testbed for Rendering, Simulation, and Spatial Intelligence", "summary": "Researchers introduced WildCity, a real-world multimodal dataset collected by autonomous fleets traversing complex urban environments, to advance city-scale spatial intelligence for AI. The dataset includes 18 trajectories averaging 83.7 kilometers each, with challenges like dynamic objects and lighting variations, and provides a reconstruction baseline and closed-loop simulator. WildCity aims to enable AI to perceive, remember, and reason across space at a scale comparable to human cognition.", "body_md": "arXiv:2607.06838v1 Announce Type: new\nAbstract: Humans can navigate an unfamiliar city and gradually form a coherent spatial mental map spanning tens of square kilometers. Can AI build spatial representations at a comparable scale? Although recent foundation models have advanced scene reconstruction and embodied intelligence, scaling to entire cities remains an open challenge, primarily due to the lack of city-scale data. To bridge the gap, we introduce WildCity, a real-world multimodal dataset collected by autonomous fleets traversing complex urban environments. Our dataset includes 18 trajectories, each averaging 83.7 kilometers in length, and preserves the core challenges of in-the-wild perception, e.g., dynamic objects, lighting variations, and imperfect camera poses. We further establish an urban-tailored reconstruction baseline and convert the reconstructed environments into a closed-loop simulator. Beyond the dataset and baseline, we systematically analyze the key challenges on the path to simulation-ready urban digital twins: scalability, extrapolation, and uncertainty. Ultimately, WildCity aims to catalyze progress not only in city-scale rendering, but more broadly in the pursuit of AI that can perceive, remember, and reason across space at a scale comparable to human cognition. Project page: https://han-xiangyu.github.io/Wild-City/", "url": "https://wpnews.pro/news/wildcity-a-real-world-city-scale-testbed-for-rendering-simulation-and-spatial", "canonical_source": "https://arxiv.org/abs/2607.06838", "published_at": "2026-07-09 04:00:00+00:00", "updated_at": "2026-07-09 04:28:54.173992+00:00", "lang": "en", "topics": ["artificial-intelligence", "computer-vision", "autonomous-vehicles", "ai-research", "ai-infrastructure"], "entities": ["WildCity"], "alternates": {"html": "https://wpnews.pro/news/wildcity-a-real-world-city-scale-testbed-for-rendering-simulation-and-spatial", "markdown": "https://wpnews.pro/news/wildcity-a-real-world-city-scale-testbed-for-rendering-simulation-and-spatial.md", "text": "https://wpnews.pro/news/wildcity-a-real-world-city-scale-testbed-for-rendering-simulation-and-spatial.txt", "jsonld": "https://wpnews.pro/news/wildcity-a-real-world-city-scale-testbed-for-rendering-simulation-and-spatial.jsonld"}}