Optimizing Local–Global Fused Feature Space With Category-Center-Aware Supervised Contrastive Learning for Urban 3-D Mesh Segmentation

Semantic urban 3D meshes have widely applied in the fields of photogrammetry and remote sensing. Acquiring discriminative feature representations to improve semantic segmentation accuracy in urban 3D meshes remains a significant challenge. To address this issue, a novel framework for urban 3D mesh semantic segmentation is proposed in this paper, which comprises three key innovations: (1) an adaptive local edge feature extractor designed to capture fine-grained geometric details across diverse local structures; (2) a local-global fusion module that synergistically combines these local features with global contextual representations extracted via transformer networks; and (3) a supervised category-center-aware contrastive learning strategy that addresses class imbalance and improves the discriminability of feature representations. Ablation studies validate the individual contributions of each component. Evaluated on three benchmark datasets, namely SUM, seMet, and Wuhan, our framework achieves state-of-the-art performance, consistently surpassing existing methods across all three datasets. The code is available at https://github.com/NJTechZhang/Category-Center-Aware-Contrastive-Learning-Mesh.git.