SGMatch: Semantic-Guided Non-Rigid Shape Matching with Flow Regularization

Establishing accurate point-to-point correspondences betweennon-rigid 3D shapes remains a critical challenge, particularly under non-isometric deformations and topological noise. Existing functional mappipelines suffer from ambiguities that geometric descriptors alone can-not resolve, and spatial inconsistencies inherent in the projection of trun-cated spectral bases to dense pointwise correspondences. In this paper,we introduce SGMatch, a learning-based framework that couples 3D-lifted semantic cues with trajectory-level feature transport regulariza-tion. Specifically, we design a Semantic-Guided Local Cross-Attentionmodule that integrates semantic features from vision foundation modelsinto geometric descriptors while preserving local structural continuity.Furthermore, we adapt conditional flow matching as a time-conditionedfeature transport regularizer that promotes spatially coherent point-wiserecovery. Experimental results on multiple benchmarks demonstrate thatSGMatch achieves competitive performance across near-isometric set-tings and consistent improvements under non-isometric deformations andtopological noise.