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1,509 files
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| Name | Size | Uploaded | Xet hash |
|---|---|---|---|
| data_eval_JAX | 28 items | ||
| data_eval_NYC | 504 items | ||
| README.md | 1.27 kB xet | 53f33a41 |
The evaluation videos for Skyfall-GS
This repository contains the evaluation videos for the paper Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite Imagery.
Project Page | GitHub | arXiv
Skyfall-GS is a 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. It features real-time, immersive 3D exploration and a curriculum-driven iterative refinement strategy to enhance geometric completeness and photorealistic textures.
Citation
If you find this work useful, please consider citing:
@article{lee2025SkyfallGS,
title = {{Skyfall-GS}: Synthesizing Immersive {3D} Urban Scenes from Satellite Imagery},
author = {Jie-Ying Lee and Yi-Ruei Liu and Shr-Ruei Tsai and Wei-Cheng Chang and Chung-Ho Wu and Jiewen Chan and Zhenjun Zhao and Chieh Hubert Lin and Yu-Lun Liu},
journal = {arXiv preprint},
year = {2025},
eprint = {2510.15869},
archivePrefix = {arXiv}
}
- Total size
- 5.65 GB
- Files
- 1,509
- Last updated
- Aug 6
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