SEMANTIC SEGMENTATION OF URBAN TEXTURED MESHES THROUGH POINT SAMPLING

7 citations as recorded by crossref.

1. Deep learning-based semantic segmentation of urban-scale 3D meshes in remote sensing: A survey J. Adam et al. 10.1016/j.jag.2023.103365
2. A Texture Integrated Deep Neural Network for Semantic Segmentation of Urban Meshes Y. Yang et al. 10.1109/JSTARS.2023.3276977
3. MeshSegNet: a local to global feature fusion-based semantic segmentation approach for urban meshes H. Yu et al. 10.1080/10095020.2025.2541883
4. SCRM-Net: Self-Supervised Deep Clustering Feature Representation for Urban 3D Mesh Semantic Segmentation J. Wang et al. 10.3390/rs17081424
5. Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds Z. Ballouch et al. 10.3390/rs16020329
6. METNet: A mesh exploring approach for segmenting 3D textured urban scenes Q. Schreiber et al. 10.1016/j.isprsjprs.2024.10.020
7. MeshNet-SP: A Semantic Urban 3D Mesh Segmentation Network with Sparse Prior G. Zhang & R. Zhang 10.3390/rs15225324