International Journal of Computer Vision, 2022
Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung.
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}
}
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 ../../
We perform classification on ModelNet40 and ScanObjectNN respectively.
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
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'
We perform part segmentation and semantic segmentation on ShapeNet and S3DIS respectively.
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
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
This repository is released under MIT License (see LICENSE file for details).
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