Synthesizing 3D Abstractions by Inverting Procedural Buildings with Transformers
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909469480321024 |
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| author | Dax, Maximilian Berbel, Jordi Stria, Jan Guibas, Leonidas Bergmann, Urs |
| author_facet | Dax, Maximilian Berbel, Jordi Stria, Jan Guibas, Leonidas Bergmann, Urs |
| contents | We generate abstractions of buildings, reflecting the essential aspects of their geometry and structure, by learning to invert procedural models. We first build a dataset of abstract procedural building models paired with simulated point clouds and then learn the inverse mapping through a transformer. Given a point cloud, the trained transformer then infers the corresponding abstracted building in terms of a programmatic language description. This approach leverages expressive procedural models developed for gaming and animation, and thereby retains desirable properties such as efficient rendering of the inferred abstractions and strong priors for regularity and symmetry. Our approach achieves good reconstruction accuracy in terms of geometry and structure, as well as structurally consistent inpainting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17044 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Synthesizing 3D Abstractions by Inverting Procedural Buildings with Transformers Dax, Maximilian Berbel, Jordi Stria, Jan Guibas, Leonidas Bergmann, Urs Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning We generate abstractions of buildings, reflecting the essential aspects of their geometry and structure, by learning to invert procedural models. We first build a dataset of abstract procedural building models paired with simulated point clouds and then learn the inverse mapping through a transformer. Given a point cloud, the trained transformer then infers the corresponding abstracted building in terms of a programmatic language description. This approach leverages expressive procedural models developed for gaming and animation, and thereby retains desirable properties such as efficient rendering of the inferred abstractions and strong priors for regularity and symmetry. Our approach achieves good reconstruction accuracy in terms of geometry and structure, as well as structurally consistent inpainting. |
| title | Synthesizing 3D Abstractions by Inverting Procedural Buildings with Transformers |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2501.17044 |