https://github.com/pqhieu/jsis3d

[CVPR'19] JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds
https://github.com/pqhieu/jsis3d

cvpr deep-learning instance-segmentation point-cloud pytorch

Last synced: 3 months ago
JSON representation

[CVPR'19] JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds

• Host: GitHub
• URL: https://github.com/pqhieu/jsis3d
• Owner: pqhieu
• License: mit
• Created: 2019-04-06T13:58:48.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2020-07-08T07:49:31.000Z (over 4 years ago)
• Last Synced: 2024-08-01T03:45:52.907Z (6 months ago)
• Topics: cvpr, deep-learning, instance-segmentation, point-cloud, pytorch
• Language: Python
• Homepage: https://pqhieu.com/research/cvpr19/
• Size: 1.49 MB
• Stars: 175
• Watchers: 7
• Forks: 36
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# JSIS3D

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](https://arxiv.org/abs/1904.00699) |

> [Homepage](https://pqhieu.github.io/research/cvpr19/)

### 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](https://drive.google.com/open?id=1s1cFfb8cInM-SNHQoTGxN9BIyNpNQK6x))

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 ([email protected])