Prepare Matterport3D Data

1. Download Matterport3D data HERE. Only region_segmentations is needed. Dataset splits can be downloaded from HERE.

2. Unzip the dataset and move organize_as_scannet.py into the folder. The file directory should be like:

...
├── organize_as_scannet.py
└── v1
    └── scans
        ├── ...
        ├── ZMojNkEp431
        └── zsNo4HB9uLZ
            └── region_segmentations
                ├── ...
                ├── resionx.fsegs.json
                ├── resionx.ply
                ├── resionx.semseg.json
                └── resionx.vsegs.json

3. In region_segmentations, the index x must be continuous (start from 0). Some folders do not comply with this rule and we manually changed the index.

4. Run python organize_as_scannet.py. Move/link the generated for_scannet/scans folder such that under scans there should be folders with names such as scene0001_01.

5. Extract point clouds and annotations (semantic seg, instance seg etc.) by running python batch_load_matterport_data.py, which will create a folder named matterport_train_detection_data_md40 here.