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arXiv · 2511.22686

Emergent Extreme-View Geometry in 3D Foundation Models

Yiwen Zhang·Joseph Tung·Ruojin Cai·David Fouhey·Hadar Averbuch-Elor
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Abstract

3D foundation models (3DFMs) have recently transformed 3D vision, enabling joint prediction of depths, poses, and point maps directly from images. Yet their ability to reason under extreme, non-overlapping views remains largely unexplored. In this work, we study their internal representations and find that 3DFMs exhibit an emergent understanding of extreme-view geometry, despite never being trained for such conditions. To further enhance these capabilities, we introduce a lightweight alignment scheme that refines their internal 3D representation by tuning only a small subset of backbone bias terms, leaving all decoder heads frozen. This targeted adaptation substantially improves relative pose estimation under extreme viewpoints without degrading per-image depth or point quality. Additionally, we contribute MegaUnScene, a new benchmark of Internet scenes unseen by existing 3DFMs, with dedicated test splits for both relative pose estimation and dense 3D reconstruction. All code and data will be released.

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Yiwen Zhang, Joseph Tung, Ruojin Cai, David Fouhey, Hadar Averbuch-Elor. 2025-12-01. Emergent Extreme-View Geometry in 3D Foundation Models. https://arxiv.org/abs/2511.22686

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