Towards Reconstructing Geographically Diverse Architecture with 3D Foundation Models

Internet-scale photo collections of real-world landmarks have driven progress in 3D computer vision yet remain highly challenging for modern 3D foundation models (3DFMs) that estimate scene structure in a single feed-forward pass. In this work, we introduce ArchWorld, a benchmark of 3D architectural landmarks with high geographic coverage and rich metadata and use it to conduct a thorough error analysis of current 3DFMs to understand where they fall short and how they can be improved. We examine socially relevant disparities in model error and find that model performance varies by geographic region, but this is heavily confounded by scene size. Leveraging insights from model failings, we introduce an intervention scheme that improves 3DFM performance while simultaneously reducing geographic disparities. Our code and data will be publicly available.