[Submitted on 15 Oct 2026]
Title:Towards Reconstructing Geographically Diverse Architecture with 3D Foundation Models
View a PDF of the paper titled Towards Reconstructing Geographically Diverse Architecture with 3D Foundation Models, by Nicky Kriplani and Yiwen Zhang and Angelina Wang and Hadar Averbuch-Elor
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Abstract: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. We use this benchmark 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; however, unlike typical fairness regimes, the trends only partially match training data representation. Our investigation reveals that the observed geographic disparities reflect not only differences in data representation and scene size but also differences in region-specific architectural features and background distractors that make scenes from different regions inherently vary in difficulty. Leveraging insights from these model failings, we introduce an intervention scheme that improves 3DFM performance while simultaneously reducing geographic disparities.
Comments: NeurIPS 2026. Project Page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2610.22514 [cs.CV]
(or arXiv:2610.22514v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.22514
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Aniket Kriplani [view email] [v1] Thu, 15 Oct 2026 18:08:01 UTC (12,808 KB)
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