Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite
  Imagery
Authors: name: Jie-Ying Lee, name: Yi-Ruei Liu, name: Shr-Ruei Tsai, name: Wei-Cheng Chang, name: Chung-Ho Wu, name: Jiewen Chan, name: Zhenjun Zhao, name: Chieh Hubert Lin, name: Yu-Lun Liu
Organization: National Yang Ming Chiao Tung University

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 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/