This is the official Pytorch implementation of the following publication.

JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with
Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields
Quang-Hieu Pham, Duc Thanh Nguyen, Binh-Son Hua, Gemma Roig, Sai-Kit Yeung
Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Oral)
Paper, Homepage

If you find our work useful for your research, please consider citing:

@inproceedings{pham-jsis3d-cvpr19,
  title = {{JSIS3D}: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields},
  author = {Pham, Quang-Hieu and Nguyen, Duc Thanh and Hua, Binh-Son and Roig, Gemma and Yeung, Sai-Kit},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019}
}

This code is tested in Manjaro Linux with CUDA 10.0 and Pytorch 1.0.

• Python 3.5+
• Pytorch 0.4.0+

We have preprocessed the S3DIS dataset (2.5 GB) in HDF5 format. After downloading the files, put them into the corresponding data/s3dis/h5 folder.

To train a model on S3DIS dataset:

python train.py --config configs/s3dis.json --logdir logs/s3dis

Log files and network parameters will be saved to the logs/s3dis folder.

After training, we can use the model to predict semantic-instance segmentation labels as follows:

python pred.py --config configs/s3dis.json --logdir logs/s3dis

To evaluate the results:

python eval.py --config configs/s3dis.json --logdir logs/s3dis

Check out the scripts folder to see how we prepare the dataset for training.

Our code is released under MIT license (see LICENSE for more details).

Contact: Quang-Hieu Pham ([email protected])