Proc-GS: Procedural Building Generation for City Assembly with 3D Gaussians
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917865185083392 |
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| author | Li, Yixuan Ran, Xingjian Xu, Linning Lu, Tao Yu, Mulin Wang, Zhenzhi Xiangli, Yuanbo Lin, Dahua Dai, Bo |
| author_facet | Li, Yixuan Ran, Xingjian Xu, Linning Lu, Tao Yu, Mulin Wang, Zhenzhi Xiangli, Yuanbo Lin, Dahua Dai, Bo |
| contents | Buildings are primary components of cities, often featuring repeated elements such as windows and doors. Traditional 3D building asset creation is labor-intensive and requires specialized skills to develop design rules. Recent generative models for building creation often overlook these patterns, leading to low visual fidelity and limited scalability. Drawing inspiration from procedural modeling techniques used in the gaming and visual effects industry, our method, Proc-GS, integrates procedural code into the 3D Gaussian Splatting (3D-GS) framework, leveraging their advantages in high-fidelity rendering and efficient asset management from both worlds. By manipulating procedural code, we can streamline this process and generate an infinite variety of buildings. This integration significantly reduces model size by utilizing shared foundational assets, enabling scalable generation with precise control over building assembly. We showcase the potential for expansive cityscape generation while maintaining high rendering fidelity and precise control on both real and synthetic cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07660 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Proc-GS: Procedural Building Generation for City Assembly with 3D Gaussians Li, Yixuan Ran, Xingjian Xu, Linning Lu, Tao Yu, Mulin Wang, Zhenzhi Xiangli, Yuanbo Lin, Dahua Dai, Bo Computer Vision and Pattern Recognition Buildings are primary components of cities, often featuring repeated elements such as windows and doors. Traditional 3D building asset creation is labor-intensive and requires specialized skills to develop design rules. Recent generative models for building creation often overlook these patterns, leading to low visual fidelity and limited scalability. Drawing inspiration from procedural modeling techniques used in the gaming and visual effects industry, our method, Proc-GS, integrates procedural code into the 3D Gaussian Splatting (3D-GS) framework, leveraging their advantages in high-fidelity rendering and efficient asset management from both worlds. By manipulating procedural code, we can streamline this process and generate an infinite variety of buildings. This integration significantly reduces model size by utilizing shared foundational assets, enabling scalable generation with precise control over building assembly. We showcase the potential for expansive cityscape generation while maintaining high rendering fidelity and precise control on both real and synthetic cases. |
| title | Proc-GS: Procedural Building Generation for City Assembly with 3D Gaussians |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.07660 |