SEMANTIC URBAN MESH ENHANCEMENT UTILIZING A HYBRID MODEL

12 citations as recorded by crossref.

1. Mesh-Based DGCNN: Semantic Segmentation of Textured 3-D Urban Scenes R. Zhang et al. https://doi.org/10.1109/TGRS.2023.3266273
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3. Deep learning-based semantic segmentation of urban-scale 3D meshes in remote sensing: A survey J. Adam et al. https://doi.org/10.1016/j.jag.2023.103365
4. A Texture Integrated Deep Neural Network for Semantic Segmentation of Urban Meshes Y. Yang et al. https://doi.org/10.1109/JSTARS.2023.3276977
5. METNet: A mesh exploring approach for segmenting 3D textured urban scenes Q. Schreiber et al. https://doi.org/10.1016/j.isprsjprs.2024.10.020
6. MeshSegNet: a local to global feature fusion-based semantic segmentation approach for urban meshes H. Yu et al. https://doi.org/10.1080/10095020.2025.2541883
7. Juggling with representations: On the information transfer between imagery, point clouds, and meshes for multi-modal semantics D. Laupheimer & N. Haala https://doi.org/10.1016/j.isprsjprs.2021.03.007
8. MeshNet-SP: A Semantic Urban 3D Mesh Segmentation Network with Sparse Prior G. Zhang & R. Zhang https://doi.org/10.3390/rs15225324
9. Improving Three-Dimensional Building Segmentation on Three-Dimensional City Models through Simulated Data and Contextual Analysis for Building Extraction F. Leroux et al. https://doi.org/10.3390/ijgi13010020
10. LMSeg: an end-to-end geometric message-passing network on barycentric dualgraphs for large-scale landscape mesh segmentation Z. Huang et al. https://doi.org/10.1080/15481603.2025.2584806
11. Hierarchical 2-D/3-D Object-Based Classification of Photogrammetric Textured Mesh Models Z. Hu et al. https://doi.org/10.1109/JSTARS.2024.3500012
12. Optimizing Local–Global Fused Feature Space With Category-Center-Aware Supervised Contrastive Learning for Urban 3-D Mesh Segmentation G. Zhou et al. https://doi.org/10.1109/TGRS.2026.3665519