GGPS — Datasets & Pretrained Models

Geometry and Gradient-based Partitioning for Panoramic Outdoor Reconstruction

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📦 Overview

This repository hosts the datasets and pretrained .ply Gaussian models for PanoLOG / GGPS — panoramic (equirectangular / ERP) outdoor 3D Gaussian Splatting reconstruction. For training and rendering code, see the GGPS code repository.

• datasets/ — per-scene capture data (ERP panoramas + openMVG reconstruction), one archive per scene.
• ply/ — pretrained / reconstructed Gaussian Splatting .ply models.

🔢 Dataset overview

Scene	Archive	Panoramas	Reconstruction
FTP	datasets/FTP.zip	354	openMVG (sfm_data.bin/.json, colorized.ply, cloud_and_poses.ply)
NSC	datasets/NSC.zip	1862	openMVG (same as above)
NSK	datasets/NSK.zip	576	openMVG (same as above)
Total		2792

All panoramas are equirectangular (ERP) .png.

🗂️ Layout

GGPS/
├── datasets/            # per-scene archives (FTP.zip, NSC.zip, NSK.zip, ...)
│   └── README.md
└── ply/                 # pretrained .ply Gaussian models
    └── README.md

Each dataset archive expands to the openMVG → COLMAP layout expected by the code repo:

<scene>/
├── images/                  # ERP panoramas
└── reconstruction/
    ├── sfm_data.bin         # openMVG binary (sfm_data.json can be re-exported from this)
    ├── sfm_data.json        # openMVG JSON
    ├── colorized.ply        # colorized sparse point cloud (3DGS init)
    └── cloud_and_poses.ply

⬇️ Download

With the Hugging Face CLI:

pip install -U "huggingface_hub[cli]"

# whole repo
hf download Insta360-Research/GGPS --repo-type model --local-dir GGPS

# a single scene archive
hf download Insta360-Research/GGPS datasets/FTP.zip --repo-type model --local-dir .

📄 License

Released under CC BY-NC 4.0 (non-commercial use only), consistent with the GGPS code repository.

📌 Citation

@article{panolog2026,
  title   = {Geometry and Gradient-based Partitioning for Panoramic Outdoor Reconstruction},
  author  = {Chen, Weijian and Yao, Weibo and Zhang, Yuhang and Tang, Xiaolin and
             Wang, Guo and Zhang, Weijun and Gao, Xitong and Chen, Yihao and
             Qin, Hongde and Qi, Lu},
  journal = {arXiv preprint arXiv:2607.08769},
  year    = {2026}
}