[ECCV 2026] Holo360D: A Large-Scale Real-World Dataset with Continuous Trajectories for Advancing Panoramic 3D Reconstruction and Beyond

       

🎉 NEWS

• [2026.07.04] 🔥 We have fixed incorrect RGB face masks within the Holo360D test set. If you downloaded the test set before July 2, 2026, please re-download the updated test dataset.
• [2026.07.01] 🔥 We have released the inference code for fine-tuned Pi3 on the Holo360D, which supports both single-view and multi-view panoramic 3D reconstruction.
• [2026.06.27] 🔥 We have released all data of the Holo360D dataset on Hugging Face, featuring 56 indoor scenes and 19 outdoor scene.
• [2026.06.18] 🎉 Holo360D has been accepted by ECCV 2026.
• [2026.06.03] 🔥 We have released test data of the Holo360D dataset on Hugging Face, featuring 13 indoor scenes and 4 outdoor scene.

✨ Overview

We present Holo360D, the first large-scale real-world panoramic 3D dataset, containing 109,495 panoramas paired with LiDAR-derived ground truth, including precise meshes, point clouds, depth maps, and camera poses. More importantly, Holo360D is the first panoramic dataset to offer accurately aligned high-completeness depth maps with continuous camera trajectories over long sequences.

Key characteristics (from the paper):

• Large-scale real-world 360 panorama 3D dataset.
• Continuous trajectory capture for multi-view settings.
• Accurately aligned high-completeness depth maps for training and testing.
• A benchmark setup for model fine-tuning and evaluation.

🔮 Inference

We have released the inference code and checkpoint for fine-tuned Pi3 on the Holo360D (click here)

📦 Dataset Structure

Holo360D/
├── train/
│   ├── Indoor_xxx/
│   │   ├── rgb/                # panoramic RGB images (.jpg)
│   │   ├── depth/              # depth maps (.exr)
│   │   │   ├── mesh_depth/             # depth maps (.exr)
│   │   │   ├── pointcloud_depth/       # depth maps (.exr)
│   │   │   ├── visual_mesh_depth/      # visualization (.jpg)
│   │   │   └── visual_pointcloud_depth/# visualization (.jpg)
│   │   ├── mask/               # masks (.jpg)
│   │   └── poses/              # camera poses (.txt)
│   ├── Indoor_xxx/
│   ├── Outdoor_xxx/
│   │   ├── rgb/                # panoramic RGB images (.jpg)
│   │   ├── depth/
│   │   │   ├── mesh_depth/             # depth maps (.exr)
│   │   │   ├── pointcloud_depth/       # depth maps (.exr)
│   │   │   ├── visual_mesh_depth/      # visualization (.jpg)
│   │   │   └── visual_pointcloud_depth/# visualization (.jpg)
│   │   ├── mask/               # masks (.jpg)
│   │   └── poses/              # camera poses (.txt)
│   ├── Outdoor_xxx/
│   └── ...
└── test/
    ├── Indoor_xxx/
    │   ├── rgb/
    │   ├── depth/
    │   │   ├── mesh_depth/             # depth maps (.exr)
    │   │   ├── pointcloud_depth/       # depth maps (.exr)
    │   │   ├── visual_mesh_depth/      # visualization (.jpg)
    │   │   └── visual_pointcloud_depth/# visualization (.jpg)
    │   ├── mask/
    │   └── poses/
    ├── Indoor_xxx/
    ├── Outdoor_xxx/
    │   ├── rgb/
    │   ├── depth/
    │   │   ├── mesh_depth/
    │   │   ├── pointcloud_depth/
    │   │   ├── visual_mesh_depth/
    │   │   └── visual_pointcloud_depth/
    │   ├── mask/
    │   └── poses/
    ├── Outdoor_xxx/
    └── ...

Notes:

• Timestamp-like file names are shared across modalities to support frame-level alignment.

💡 Dataset Download

Detailed download links and full-package release plan are to be released.

• Hugging Face

📬 Contact

If you have any other questions, you can open an issue on GitHub or contact us via email at jou719@connect.hkust-gz.edu.cn.

Citation

If you find this dataset useful, please cite our paper.

@article{ou2026holo360d,
  title={Holo360D: A Large-Scale Real-World Dataset with Continuous Trajectories for Advancing Panoramic 3D Reconstruction and Beyond},
  author={Ou, Jing and Cao, Zidong and Ren, Yinrui and Li, Zhuoxiao and Zhu, Jinjing and Hua, Tongyan and Zhang, Shuai and Xiong, Hui and Zhao, Wufan},
  journal={arXiv preprint arXiv:2604.22482},
  year={2026}
}