Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud

Introduction

This repository propose python scripts for point cloud semantic segmentation. The library is coded with PyTorch.

The conference paper is here: https://arxiv.org/pdf/2103.10339.pdf?ref=https://githubhelp.com

Citation

If you use this code in your research, please consider citing: (citation will be updated as soon as 3DOR proceedings will be released)

@inproceedings{xu2021investigate,
  title={Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud},
  author={Xu, Mingye and Zhou, Zhipeng and Zhang, Junhao and Qiao, Yu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={4},
  pages={3047--3055},
  year={2021}
}

Data composition

The data is placed under the ./data/s3dis_data directory, as follows ./data/s3dis_data/Area_1/conferenceRoom_1/xyzrgb.npy

Platform

The code was tested on Ubuntu 16.04 with Anaconda.

Dependencies

• Pytorch
• Scikit-learn for confusion matrix computation, and efficient neighbors search
• TQDM for progress bars
• PlyFile
• H5py

All these dependencies can be install via conda in an Anaconda environment or via pip.

Nearest neighbor module

The nearest_neighbors directory contains a very small wrapper for NanoFLANN with OpenMP. To compile the module:

cd nearest_neighbors
python setup.py install --home="."

## Data preparation

Data is prepared using the ./excample/s3dis/prepare_s3dis_label.py.

Training

cd ./s3dis

For training on area 5:

python s3dis_seg.py --rootdir path_to_data_processed/ --area 5 --savedir path_to_save_directory

Testing

For testing on area 5:

python s3dis_seg.py --rootdir path_to_data_processed --area 5 --savedir path_to_save_directory --test

Evaluation

python s3dis_eval.py --datafolder path_to_data_processed --predfolder pathèto_model --area 5

Acknowledgement

We include the following PyTorch 3rd-party libraries: [1] [ConvPoint] (https://github.com/aboulch/ConvPoint) [2] [GSNet] (https://github.com/MingyeXu/GS-Net)