MapAnything: Evaluating Monocular Metric Depth Models for 3D Urban Asset Localization

ArXi:2509.14839v2 Announce Type: replace City administrations increasingly rely on comprehensive databases and urban digital twins of city assets, such as traffic signs and trees, as well as incidents like graffiti or road damage, to maintain an effective overview of urban conditions. Digitization has increased the demand for continuously updated spatial datasets, yet current data acquisition and maintenance processes still involve considerable manual effort, posing significant scalability challenges. This paper.