qian43/VIGOR_SAT3DGEN_add_skymask_DSM_satdepth

VIGOR SAT3DGEN Supplement

This repository contains the project-specific supplements for the paper Sat3DGen: Comprehensive Street-Level 3D Scene Generation from Single Satellite Image.

Project Page | Code

Dataset Summary

These files are intended to be used alongside the original VIGOR dataset. The supplement includes:

• sat_depth/: Satellite depth maps.
• pano_sky_mask/: Sky masks for panoramic images.
• Seattle_DSM/: High-resolution Digital Surface Model (DSM) data for Seattle.
• Training and test split .txt files.

Data Organization

Note the following organizational requirement from the authors:

• Seattle_DSM/ should be placed at the same level as the city folders (e.g., Seattle/), not inside them.

For the full expected folder organization, please refer to the dataset layout documentation in the GitHub repository.

Citation

@inproceedings{
    qian2026satdgen,
    title={Sat3{DG}en: Comprehensive Street-Level 3D Scene Generation from Single Satellite Image},
    author={Ming Qian and Zimin Xia and Changkun Liu and Shuailei Ma and Wen Wang and Zeran Ke and Bin Tan and Hang Zhang and Gui-Song Xia},
    booktitle={The Fourteenth International Conference on Learning Representations},
    year={2026},
    url={https://openreview.net/forum?id=E7JzkZCofa}
}

@ARTICLE{Qian_2026_Sat2Densitypp,
    author={Qian, Ming and Tan, Bin and Wang, Qiuyu and Zheng, Xianwei and Xiong, Hanjiang and Xia, Gui-Song and Shen, Yujun and Xue, Nan},
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
    title={Seeing Through Satellite Images at Street Views}, 
    year={2026},
    volume={48},
    number={5},
    pages={5692-5709},
    doi={10.1109/TPAMI.2026.3652860}}

@InProceedings{Qian_2023_Sat2Density,
    author    = {Qian, Ming and Xiong, Jincheng and Xia, Gui-Song and Xue, Nan},
    title     = {Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {3683-3692}
}