AI RESEARCH
Learning 3D Representations for Spatial Intelligence from Unposed Multi-View Images
arXiv CS.CV
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ArXi:2604.10573v1 Announce Type: new Robust 3D representation learning forms the perceptual foundation of spatial intelligence, enabling downstream tasks in scene understanding and embodied AI. However, learning such representations directly from unposed multi-view images remains challenging. Recent self-supervised methods attempt to unify geometry, appearance, and semantics in a feed-forward manner, but they often suffer from weak geometry induction, limited appearance detail, and inconsistencies between geometry and semantics. We