Gaozhongpai / PaiConvPointSeg

Official repository for the paper "Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds" [Segmentation]

Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds [Segmentation]

This is the official implementation of PAI-Conv for point cloud semantic segmentation:

(1) Setup

This code has been tested with Python 3.7, Tensorflow 2, CUDA 10.2 on Ubuntu 18.04.

• Clone the repository

git clone https://github.com/Gaozhongpai/PaiConvPointSeg && cd PaiConvPointSeg

• Setup python environment

conda create -n paiconv python=3.7
source activate paiconv
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 /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 /data/S3DIS/results, calculate the final mean IoU results:

python utils/6_fold_cv.py

(3) Semantic3D

7zip is required to uncompress the raw data in this dataset, to install p7zip:

sudo apt-get install p7zip-full

• Download and extract the dataset. First, please specify the path of the dataset by changing the BASE_DIR in "download_semantic3d.sh"

sh utils/download_semantic3d.sh

• Preparing the dataset:

python utils/data_prepare_semantic3d.py

• Start training:

python main_Semantic3D.py --mode train --gpu 0

• Evaluation:

python main_Semantic3D.py --mode test --gpu 0

Note:

• Preferably with more than 64G RAM to process this dataset due to the large volume of point cloud

(4) SemanticKITTI

SemanticKITTI dataset can be found here. Download the files related to semantic segmentation and extract everything into the same folder. Uncompress the folder and move it to /data/semantic_kitti/dataset.

• Preparing the dataset:

python utils/data_prepare_semantickitti.py

• Start training:

python main_SemanticKITTI.py --mode train --gpu 0

• Evaluation:

sh jobs_test_semantickitti.sh

Acknowledgment

The structure of this codebase is borrowed from RandLA-Net.

License

Licensed under the CC BY-NC-SA 4.0 license, see LICENSE.