From Extrinsic to Intrinsic: Geodesic-Guided Representation Learning for 3D Geometric Data 事件

From Extrinsic to Intrinsic: Geodesic-Guided Representation Learning for 3D Geometric Data arXiv:2606.02268v1 Announce Type: new Abstract: 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 introduce