The first large-scale "ImageNet"-style dataset for 3D understanding.

• Dataset page: https://raw3d.xyz/dataset
• Purpose: A comprehensive 3D dataset for research and applications in computer vision, robotics, AR/VR, and scene understanding.

Features may include (see reader.py):

• loading 3D depth, normal, trajectory, mesh, and multi-view images
• reading object labels, bounding boxes, and semantic annotations
• supporting common dataset splits and evaluation protocols
• benchmark your SLAM, 3D reconstruction and other 3D related tasks.

Data Structure Example:

Rawmantic
└── LivingSpace
    └── StreetBlock
        └── LivingSpace_StreetBlock_00000000_ICPark
            └── 00000000_ICPark_02
                └── data
                    ├── depth_camera_left
                    ├── depth_camera_right
                    ├── depth_camera_third
                    ├── normal_camera_left
                    ├── normal_camera_right
                    ├── normal_camera_third
                    ├── pose_camera_left
                    ├── pose_camera_right
                    ├── pose_camera_third
                    ├── rgb_camera_left
                    ├── rgb_camera_right
                    └── rgb_camera_third

Still uploading, 4 sub scenes are done...

• Baidu Yunpan: https://pan.baidu.com/s/1DzJvQu6dEhi7svqE1S-18Q?pwd=rock
• Google Drive: slow transfer speed, try to find an alternative.

If you use this dataset in your research, please cite it as:

@article{rawmantic2026data,
  title={Rawmantic 3D Training Field: From The Real World, For The Real World},
  author={Rawmantic Team},
  journal={Rawmantic},
  year={2026}
}

Refer to our website: https://raw3d.xyz/dataset

If you want to contribute, report issues, or ask questions, please open an issue in this repository or reach out through the project's official channels.