Waterfall-Net: Waterfall Feature Aggregation for Point Cloud Semantic Segmentation

This is the official implementation of Waterfall-Net (PRCV 2022), a new point cloud semantic segmentation paradigm with waterfall feature aggregation. For technical details, please refer to:

Waterfall-Net: Waterfall Feature Aggregation for Point Cloud Semantic Segmentation
Hui Shuai, Xiang Xu, Qingshan Liu.
[paper]

(1) Setup

This code has been tested with Python 3.6, Tensorflow 1.13.1, CUDA 10.0 on Ubuntu 16.04.

• Clone the repository

git clone https://github.com/Xiangxu-0103/Waterfall-Net.git && cd Waterfall-Net

• Setup python environment

conda create -n waterfallnet python=3.6.8
conda activate waterfallnet
pip install tensorflow-gpu==1.13.1
pip install -r helper_requirements.txt
sh compile_op.sh

(2) S3DIS

S3DIS dataset can be found here. Download the files named "Stanford3dDataset_v1.2_Aligned_Version.zip". Uncompress the folder and move it to /home/data/S3DIS.

• Preparing the dataset:

python utils/data_prepare_s3dis.py

• Start 6-fold cross validation:

sh jobs_6_fold_cv_s3dis.sh

• Move all the generated results (*.ply) in /test folder to /home/data/S3DIS/results, calculate the final mean IoU results:

python utils/6_fold_cv.py

Citation

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

@inproceedings{shuai2022waterfall,
  title={Waterfall-Net: Waterfall Feature Aggregation for Point Cloud Semantic Segmentation},
  author={Shuai, Hui and Xu, Xiang and Liu, Qingshan},
  booktitle={Pattern Recognition and Computer Vision: 5th Chinese Conference, PRCV 2022, Shenzhen, China, November 4--7, 2022, Proceedings, Part III},
  pages={28--40},
  year={2022},
  organization={Springer}
}

Acknowledge

• Our code refers to RandLA-Net.