AI RESEARCH
From Extrinsic to Intrinsic: Geodesic-Guided Representation Learning for 3D Geometric Data
arXiv CS.CV
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ArXi:2606.02268v1 Announce Type: new Geometric analysis fundamentally distinguishes between \textit{extrinsic} and \textit{intrinsic} perspectives. The dominant paradigm in current 3D representation learning relies on either extrinsic spatial structures or high-level semantics, struggling to capture the essence of shape identity and underlying manifold topology. To bridge this gap, we