Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite Imagery
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914403686809600 |
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| author | Lee, Jie-Ying Liu, Yi-Ruei Tsai, Shr-Ruei Chang, Wei-Cheng Wu, Chung-Ho Chan, Jiewen Zhao, Zhenjun Lin, Chieh Hubert Liu, Yu-Lun |
| author_facet | Lee, Jie-Ying Liu, Yi-Ruei Tsai, Shr-Ruei Chang, Wei-Cheng Wu, Chung-Ho Chan, Jiewen Zhao, Zhenjun Lin, Chieh Hubert Liu, Yu-Lun |
| contents | Synthesizing large-scale, explorable, and geometrically accurate 3D urban scenes is a challenging yet valuable task for immersive and embodied applications. The challenge lies in the lack of large-scale and high-quality real-world 3D scans for training generalizable generative models. In this paper, we take an alternative route to create large-scale 3D scenes by leveraging readily available satellite imagery for realistic coarse geometry and open-domain diffusion models for high-quality close-up appearance synthesis. We propose Skyfall-GS, a novel hybrid framework that synthesizes immersive city-block scale 3D urban scenes by combining satellite reconstruction with diffusion refinement, eliminating the need for costly 3D annotations, and also featuring real-time, immersive 3D exploration. We tailor a curriculum-driven iterative refinement strategy to progressively enhance geometric completeness and photorealistic texture. Extensive experiments demonstrate that Skyfall-GS provides improved cross-view consistent geometry and more realistic textures compared to state-of-the-art approaches. Project page: https://skyfall-gs.jayinnn.dev/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15869 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite Imagery Lee, Jie-Ying Liu, Yi-Ruei Tsai, Shr-Ruei Chang, Wei-Cheng Wu, Chung-Ho Chan, Jiewen Zhao, Zhenjun Lin, Chieh Hubert Liu, Yu-Lun Computer Vision and Pattern Recognition Synthesizing large-scale, explorable, and geometrically accurate 3D urban scenes is a challenging yet valuable task for immersive and embodied applications. The challenge lies in the lack of large-scale and high-quality real-world 3D scans for training generalizable generative models. In this paper, we take an alternative route to create large-scale 3D scenes by leveraging readily available satellite imagery for realistic coarse geometry and open-domain diffusion models for high-quality close-up appearance synthesis. We propose Skyfall-GS, a novel hybrid framework that synthesizes immersive city-block scale 3D urban scenes by combining satellite reconstruction with diffusion refinement, eliminating the need for costly 3D annotations, and also featuring real-time, immersive 3D exploration. We tailor a curriculum-driven iterative refinement strategy to progressively enhance geometric completeness and photorealistic texture. Extensive experiments demonstrate that Skyfall-GS provides improved cross-view consistent geometry and more realistic textures compared to state-of-the-art approaches. Project page: https://skyfall-gs.jayinnn.dev/ |
| title | Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite Imagery |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.15869 |