SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866908276625506304 |
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| author | Gao, Weixiao Nan, Liangliang Ledoux, Hugo |
| author_facet | Gao, Weixiao Nan, Liangliang Ledoux, Hugo |
| contents | Semantic segmentation in urban scene analysis has mainly focused on images or point clouds, while textured meshes - offering richer spatial representation - remain underexplored. This paper introduces SUM Parts, the first large-scale dataset for urban textured meshes with part-level semantic labels, covering about 2.5 km2 with 21 classes. The dataset was created using our own annotation tool, which supports both face- and texture-based annotations with efficient interactive selection. We also provide a comprehensive evaluation of 3D semantic segmentation and interactive annotation methods on this dataset. Our project page is available at https://tudelft3d.github.io/SUMParts/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15300 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes Gao, Weixiao Nan, Liangliang Ledoux, Hugo Computer Vision and Pattern Recognition Semantic segmentation in urban scene analysis has mainly focused on images or point clouds, while textured meshes - offering richer spatial representation - remain underexplored. This paper introduces SUM Parts, the first large-scale dataset for urban textured meshes with part-level semantic labels, covering about 2.5 km2 with 21 classes. The dataset was created using our own annotation tool, which supports both face- and texture-based annotations with efficient interactive selection. We also provide a comprehensive evaluation of 3D semantic segmentation and interactive annotation methods on this dataset. Our project page is available at https://tudelft3d.github.io/SUMParts/. |
| title | SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.15300 |