Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite Imagery

• URL: http://arxiv.org/abs/2510.15869v1
• Date: Fri, 17 Oct 2025 17:59:51 GMT
• Title: Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite Imagery
• Authors: Jie-Ying Lee, Yi-Ruei Liu, Shr-Ruei Tsai, Wei-Cheng Chang, Chung-Ho Wu, Jiewen Chan, Zhenjun Zhao, Chieh Hubert Lin, Yu-Lun Liu,
• Abstract summary: We propose textbfSkyfall-GS, the first city-block scale 3D scene creation framework without costly 3D annotations.<n>We tailor a curriculum-driven iterative refinement strategy to progressively enhance geometric and photorealistic textures.
• Score: 13.938311471105303
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Synthesizing large-scale, explorable, and geometrically accurate 3D urban scenes is a challenging yet valuable task in providing immersive and embodied applications. The challenges lie 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 synergizing the readily available satellite imagery that supplies realistic coarse geometry and the open-domain diffusion model for creating high-quality close-up appearances. We propose \textbf{Skyfall-GS}, the first city-block scale 3D scene creation framework without costly 3D annotations, also featuring real-time, immersive 3D exploration. We tailor a curriculum-driven iterative refinement strategy to progressively enhance geometric completeness and photorealistic textures. 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/

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