Awesome

JSIS3D

This is the official Pytorch implementation of the following publication.

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

Citation

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}
}

Usage

Prerequisites

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

• Python 3.5+
• Pytorch 0.4.0+

Installation

To use MV-CRF (optional), you first need to compile the code:

cd external/densecrf
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=Release ..
make
cd ../../.. # You should be at the root folder here
make

Dataset

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

Training & Evaluation

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 --logdir logs/s3dis --mvcrf

To evaluate the results, run the following command:

python eval.py --logdir logs/s3dis

For more details, you can use the --help option for every scripts.

Note: The results on S3DIS in our paper are tested on Area 6 instead of Area 5. To reproduce the results, please change the split in train.txt and test.txt accordingly. Here I chose to keep the test set on Area 5 to make it easier to compare with other methods.

Prepare your own dataset

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

License

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

Contact: Quang-Hieu Pham (pqhieu1192@gmail.com)