PanoCity Dataset

A Large-Scale Aerial Panoramic Dataset for 3D Scene Understanding

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📊 Dataset Statistics

Attribute	Value
Total Size	1.4 TB
Cities	Beijing (20 blocks), Jinan (76 blocks), Ningbo (41 blocks)
Panoramic RGB Images	119,537 (2048×4096)
Panoramic Depth Maps	119,537 (2048×4096)
Total Images	239,074
Image Format	PNG

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📂 Data Structure

PanoCity/
├── splits_config.json              # Official train/test splits
├── beijing/
│   ├── beijing_block0/
│   │   ├── pano_images/            # RGB panoramic images
│   │   ├── panodepth_images/       # Depth panoramic images
│   │   ├── beijing_block0_poses.json         # Pinhole camera poses
│   │   └── beijing_Pano_block0_poses.json    # Panoramic camera poses
│   └── ...
├── jinan/
└── ningbo/

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🚀 Quick Start

1. Installation

pip install datasets

2. Load Dataset

from datasets import load_dataset

# Load dataset (streaming recommended if storage is limited)
dataset = load_dataset("USERNAME/PanoCity", streaming=True)

# Iterate through samples
for sample in dataset["train"]:
    rgb_image = sample["rgb"]      # PIL Image
    depth_map = sample["depth"]    # PIL Image
    pose = sample["pose"]          # dict with transformation_matrix（opencv c2w）
    break

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📜 Citation

@article{guo2026panovggt,
  title={PanoVGGT: Feed-Forward 3D Reconstruction from Panoramic Imagery},
  author={Guo, Yijing and Chao, Mengjun and Wang, Luo and Zhao, Tianyang and Dai, Haizhao and Zhang, Yingliang and Yu, Jingyi and Shi, Yujiao},
  journal={arXiv preprint arXiv:2603.17571},
  year={2026}
}

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📧 Contact

For questions or issues, please open an issue in this repository.