3RScan is a large scale, real-world dataset which features 1482 3D reconstructions / snapshots of 478 naturally changing indoor environments, designed for benchmarking emerging tasks such as long-term SLAM, scene change detection and object instance re-localization

Each sequence comes with aligned semantically annotated 3D data and corresponding 2D frames, containing in detail:

• calibrated RGB-D sequences.
• textured 3D meshes.
• 6DoF camera poses and camera calibration parameters K.
• global alignment among scans from the same scene as a global transformation T.
• dense instance-level semantic segmentation where each instance has a fixed ID that is kept consistent across different sequences of the same environment.
• object alignment, i.e. a ground truth transformation for each changed object together with its symmetry property.

If you find the data useful please consider citing our paper:

@inproceedings{Wald2019RIO,
    title={RIO: 3D Object Instance Re-Localization in Changing Indoor Environments},
    author={Johanna Wald, Armen Avetisyan, Nassir Navab, Federico Tombari, Matthias Niessner},
    booktitle={Proceedings IEEE International Conference on Computer Vision (ICCV)},
    year = {2019}
}

The data in 3RScan is organized by RGB-D sequence. Each sequence has a unique hash value to identify the scan. The RGB-sequences and 3D reconstructions are all stored together in a separate folder. The directory has the following structure:

<scanId>
|-- mesh.refined.v2.obj
    Reconstructed mesh
|-- mesh.refined.mtl
    Corresponding material file 
|-- mesh.refined_0.png
    Corresponding mesh texture
|-- sequence.zip
    Calibrated RGB-D sensor stream with color and depth frames, camera poses
|-- labels.instances.annotated.v2.ply
    Visualization of semantic segmentation
|-- mesh.refined.0.010000.segs.v2.json
    Over-segmentation of annotation mesh
|-- semseg.v2.json
    Instance segmentation of the mesh (contains the labels)

The following are overviews of the data formats used in 3RScan:

Reconstructed surface mesh file (*.obj): OBJ format mesh with +Z axis in upright orientation.

RGB-D sensor data (*.zip): ZIP-archive with per-frame color, depth, camera pose and camera intrinsics.

Instance segmentation of the mesh (semseg.v2.json):

{
  "scan_id": "8eabc405-5af7-2f32-86d2-d757fa7b019d",
  ...
  "segGroups": [
    {
      "id": 15,
      "objectId": 15,
      "label": "window",
      "segments": [ 21, 175, ... ],
      "obb": {
        "centroid": [ 2.15, 2.17, -1.18 ],
        "axesLengths": [ 4.57, 0.62, 4.14 ],
        ...
      }, 
      ...
    }, {
      "objectId": 29,
      "label": "plant",
      ...
    }, {
      "id": 14,
      "objectId": 14,
      "label": "windowsill",
      ...
    }

meta data file (3RScan.json):

[
  {
    "reference": "531cff08-0021-28f6-8e08-ba2eeb945e09", // id of the initial scan
    "type": "train"    
    "ambiguity": [
      [
        { "instance_source": 34, 
          "instance_target": 35,
          "transform": [ ... ]  // transformation of instance 34 to instance 35 (to resolve instance ambiguity)
        }, {...}
      ]
    ],
    "scans": [ // rescans
      {
        "reference": "531cff10-0021-28f6-8f94-80db8fdbbbee", // id of rescan
        "transform": [ ... ]  // transformation to align rescan with reference
        "nonrigid": [ ... ],  // list of instances with nonrigid changes
        "removed": [], // removed instances
        "rigid": [ // rigid changes
          {
            "instance_reference": 35, // instance ID in reference
            "instance_rescan": 35, // instance ID in rescan
            "symmetry": 0, // symmetry (0 = none)
            "transform": [ ... ] // transformation to align instance in the reference to the instance in the rescan
          }, {
            ...
          }         
        ],
        
      },
      // metadata of other rescans
      {
        "reference": "19eda6f4-55aa-29a0-8893-8eac3a4d8193", ... 
      }, { ... }
    ]
  }
]

For examples on how to read these files or how to use 3RScan in general see our code samples and documentation. You have a question? Check out our FAQ.

• Training Data: You can find the train, test and validation splits here: [train, val, test]
• Segmentation format (surface mesh segmentation and aggregated semantic annotation) is similar to ScanNet
• Please see our project page for more information.