Skip to content

Latest commit

 

History

History
39 lines (20 loc) · 1.38 KB

sem_segmentation.md

File metadata and controls

39 lines (20 loc) · 1.38 KB

S3DIS indoor segmentation

Data

The S3DIS dataset can be downloaded here (4.8 GB). Download the file named Stanford3dDataset_v1.2.zip, and move it to data/Stanford3dDataset_v1.2. You may also specific your own data directory by changing the path argument in train.py.

Compile the C++ extension modules for python located in cpp_wrappers. Open a terminal in this folder, and run:

sh compile_wrappers.sh
  • The code has been tested on one configuration:
    • PyTorch 1.8.1, CUDA 10.1

Training

We train the network on a Tesla V100 gpu (to maintain the batch size). It will take a few more time in the first training. The pretrained model can be found here. Simply run:

python train.py

You may reduce the batch_num in train.py for some smaller 12GB gpus (train_tiny.py).

The models are saved in results/train/checkpoints/ every 10 epochs.

Testing

To test the model current_chkp in the previous run:

python test.py --log ./results/train

And to test a model in epoch n:

python test.py --log ./results/train --model epoch_0099.tar

Acknowledgement

The S3DIS data processing was borrowed from KPConv