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RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learning

International Journal of Computer Vision, 2022

Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung.

Project Page | arXiv

If you found this paper useful in your research, please cite:


@article{zhang2022riconv2,
  title={RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learning},
  author={Zhang, Zhiyuan and Hua, Binh-Son and Yeung, Sai-Kit},
  journal={International Journal of Computer Vision},
  volume={130},
  number={5},
  pages={1228--1243},
  year={2022},
  publisher={Springer}
}

@inproceedings{zhang2020global,
  title={Global Context Aware Convolutions for 3D Point Cloud Understanding},
  author={Zhang, Zhiyuan and Hua, Binh-Son and Chen, Wei and Tian, Yibin and Yeung, Sai-Kit},
  booktitle={2020 International Conference on 3D Vision (3DV)},
  pages={210--219},
  year={2020}
}

@inproceedings{zhang2019rotation,
  title={Rotation Invariant Convolutions for 3D Point Clouds Deep Learning},
  author={Zhang, Zhiyuan and Hua, Binh-Son and Rosen, David W and Yeung, Sai-Kit},
  booktitle={2019 International Conference on 3D Vision (3DV)},
  pages={204--213},
  year={2019}
}

Installation

This repo provides the RIConv++ source codes, which had been tested with Python 3.9.7, PyTorch 1.9.0, CUDA 11.1 on Ubuntu 20.04. Our codes are implemented based on Xu Yan's Pytorch implementation of PointNet++(Pytorch) and ShaoShuai Shi's PointNet++ cuda operations.

Install the pointnet++ cuda operation library by running the following command:

cd models/pointnet2/
python setup.py install
cd ../../

Usage

Classification

We perform classification on ModelNet40 and ScanObjectNN respectively.

ModelNet40

Download alignment ModelNet here and save in ../data/modelnet40_normal_resampled/. Follow the instructions of PointNet++(Pytorch) to prepare the data. Specifically, please use --process_data to preprocess the data, and move the processed data to ../data/modelnet40_preprocessed/. Alternatively, you can also download the pre-processd data here and save it in ../data/modelnet40_preprocessed/. (Note: the data/ folder is outside the project folder)

To train a RIConv++ model to classify shapes in the ModelNet40 dataset:

python3 train_classification_modelnet40.py

For testing, you can use your trained model by specifying --log_dir or use our pretrained model 4.9MB directly:

python3 test_classification_modelnet40.py

ScanObjectNN

Download the ScanObjectNN here and save the main_split and main_split_nobg subfolders that inlcude the h5 files into the ../data/scanobjectnn/ (Note: the data/ folder is outside the project folder)

Training on the OBJ_ONLY variant:

python3 train_classification_scanobj.py --data_type 'OBJ_NOBG'

For testing, you can use your trained model by specifying --log_dir or use our pretrained model 4.9MB directly:

python3 test_classification_scanobj.py --data_type 'OBJ_NOBG'

Training on the OBJ_BG variant:

python3 train_classification_scanobj.py --data_type 'OBJ_BG'

For testing, you can use your trained model by specifying --log_dir or use our pretrained model 4.9MB directly:

python3 test_classification_scanobj.py --data_type 'OBJ_BG'

Training on the hardest variant PB_T50_RS:

python3 train_classification_scanobj.py --data_type 'hardest'

For testing, you can use your trained model by specifying --log_dir or use our pretrained model 4.9MB directly:

python3 test_classification_scanobj.py --data_type 'hardest'

Segmentation

We perform part segmentation and semantic segmentation on ShapeNet and S3DIS respectively.

ShapeNet

Download alignment ShapeNet here and save in ../data/shapenetcore_partanno_segmentation_benchmark_v0_normal/. (Note: the data/ folder is outside the project folder)

Training:

python3 train_partseg.py

For testing, you can use your trained model by specifying --log_dir or use our pretrained model 18.2MB directly:

python3 test_partseg.py

S3DIS

Please download the S3DIS dataset here, and run the following scripts to preprocess the data:

cd data_utils
python collect_indoor3d_data.py

Processed data will save in ../data/s3dis/stanford_indoor3d/. (Note: the data/ folder is outside the project folder)

Training:

python3 train_semseg.py

For testing, you can use your trained model by specifying --log_dir or use our pretrained model 18.2MB directly:

python3 test_semseg.py

License

This repository is released under MIT License (see LICENSE file for details).