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Large-Scale 3D Segmentation on ScanNet

Dataset

You can download our preprocessed ScanNet dataset as follows: 

cd data
gdown https://drive.google.com/uc?id=1uWlRPLXocqVbJxPvA2vcdQINaZzXf1z_
tar -xvf ScanNet.tar
Please cite the ScanNet paper [1] if you are going to conduct experiments on it.

Train

For example, train PointNext-XL using 8 GPUs by default. 

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python examples/segmentation/main.py --cfg cfgs/scannet/pointnext-xl.yaml
* change the cfg file to use any other model, e.g. cfgs/s3dis/pointnet++.yaml for training PointNet++
* run the command at the root directory

Val

CUDA_VISIBLE_DEVICES=0  python examples/segmentation/main.py --cfg cfgs/scannet/<YOUR_CONFIG> mode=test dataset.test.split=val --pretrained_path <YOUR_CHECKPOINT_PATH>

Test

You can generate Scannet benchmark submission file as follows 

CUDA_VISIBLE_DEVICES=0 python examples/segmentation/main.py --cfg cfgs/scannet/<YOUR_CONFIG> mode=test dataset.test.split=test no_label=True pretrained_path=<YOUR_CHECKPOINT_PATH>
Please make sure your checkpoint and your cfg matches with each.

Reference

@inproceedings{dai2017scannet,
    title={{ScanNet}: Richly-annotated {3D} Reconstructions of Indoor Scenes},
    author={Dai, Angela and Chang, Angel X. and Savva, Manolis and Halber, Maciej and Funkhouser, Thomas and Nie{\ss}ner, Matthias},
    booktitle = CVPR,
    year = {2017}
}