GeoTexBuild: 3D Building Model Generation from Map Footprints
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911126230401024 |
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| author | Wang, Ruizhe Yang, Junyan Wang, Qiao |
| author_facet | Wang, Ruizhe Yang, Junyan Wang, Qiao |
| contents | We introduce GeoTexBuild, a modular generative framework for creating 3D building models from footprints derived from site planning or map designs. The system is designed for architects and city planners, offering a seamless solution that directly converts map features into 3D buildings. The proposed framework employs a three-stage process comprising height map generation, geometry reconstruction, and appearance stylization, culminating in building models with detailed geometry and appearance attributes. By integrating customized ControlNet, Neural style field (NSF), and Multi-view diffusion model, we explore effective methods for controlling both geometric and visual attributes during the generation process. Our approach eliminates the problem of structural variations in a single facade image in existing 3D generation techniques for buildings. Experimental results at each stage validate the capability of GeoTexBuild to generate detailed and accurate building models from footprints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08419 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GeoTexBuild: 3D Building Model Generation from Map Footprints Wang, Ruizhe Yang, Junyan Wang, Qiao Computer Vision and Pattern Recognition I.3; J.6 We introduce GeoTexBuild, a modular generative framework for creating 3D building models from footprints derived from site planning or map designs. The system is designed for architects and city planners, offering a seamless solution that directly converts map features into 3D buildings. The proposed framework employs a three-stage process comprising height map generation, geometry reconstruction, and appearance stylization, culminating in building models with detailed geometry and appearance attributes. By integrating customized ControlNet, Neural style field (NSF), and Multi-view diffusion model, we explore effective methods for controlling both geometric and visual attributes during the generation process. Our approach eliminates the problem of structural variations in a single facade image in existing 3D generation techniques for buildings. Experimental results at each stage validate the capability of GeoTexBuild to generate detailed and accurate building models from footprints. |
| title | GeoTexBuild: 3D Building Model Generation from Map Footprints |
| topic | Computer Vision and Pattern Recognition I.3; J.6 |
| url | https://arxiv.org/abs/2504.08419 |