dataset
└── scannetv2
├── meta_data(unnecessary, we have moved into our source code)
│ ├── scannetv2_train.txt
│ ├── scannetv2_val.txt
│ ├── scannetv2_test.txt
│ └── scannetv2-labels.combined.tsv
├── scans
│ ├── ...
│ ├── [scene_id]
| | └── [scene_id]_vh_clean_2.ply & [scene_id]_vh_clean_2.labels.ply & [scene_id]_vh_clean_2.0.010000.segs.json & [scene_id].aggregation.json
| └── ...
└── scans_test
├── ...
├── [scene_id]
| └── [scene_id]_vh_clean_2.ply & [scene_id].txt
└── ...
- Refer to PointGroup, we've modify the code, and it can generate input files
[scene_id]_inst_nostuff.pth for instance segmentation directly, you don't need to split the origin data into train/val/test, the script refer to gorilla3d/preprocessing/scannetv2/segmentation.
- And we package these command. You just running:
- After running such command, the structure of directory is as following:
dataset
└── scannetv2
├── meta_data(unnecessary, we have moved into our source code)
│ └── ...
├── scans
| └── ...
├── scans_test
| └── ...
| (data preparation generation as following)
├── train
| ├── [scene_id]_inst_nostuff.pth
| └── ...
├── test
| ├── [scene_id]_inst_nostuff.pth
| └── ...
├── val
| ├── [scene_id]_inst_nostuff.pth
| └── ...
└── val_gt
├── [scene_id].txt
└── ...