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https://github.com/yahuiliu99/PointConT
Official implementation of the paper "Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space"
https://github.com/yahuiliu99/PointConT
point-cloud pointnet2 pytorch
Last synced: about 2 months ago
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Official implementation of the paper "Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space"
- Host: GitHub
- URL: https://github.com/yahuiliu99/PointConT
- Owner: yahuiliu99
- Created: 2022-03-17T07:49:57.000Z (almost 3 years ago)
- Default Branch: master
- Last Pushed: 2023-06-01T12:23:27.000Z (over 1 year ago)
- Last Synced: 2024-08-01T03:40:41.798Z (5 months ago)
- Topics: point-cloud, pointnet2, pytorch
- Language: Python
- Homepage:
- Size: 250 KB
- Stars: 31
- Watchers: 2
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# PointConT
[](https://paperswithcode.com/sota/3d-point-cloud-classification-on-scanobjectnn?p=point-cloud-classification-using-content)
[](https://paperswithcode.com/sota/3d-point-cloud-classification-on-modelnet40?p=point-cloud-classification-using-content)
This repository is an official implementation of the following paper:
> [Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space](https://arxiv.org/pdf/2303.04599v1.pdf)
>
>Yahui Liu, Bin Tian, Yisheng Lv, Lingxi Li, Feiyue Wang
>
>**Accepted for publication in the *IEEE/CAA Journal of Automatica Sinica***
## Get Started
### Installation
```
# clone this repo
git clone https://github.com/yahuiliu99/PointConT.git
cd PointConT
# create a conda env
conda create -n pointcont -y python=3.7 numpy=1.20 numba
conda activate pointcont
# install PyTorch and libs (refer to requirements.txt)
# please install compatible PyTorch and CUDA versions
conda install -y pytorch=1.10.1 torchvision cudatoolkit=11.1 -c pytorch -c nvidia
pip install hydra-core==1.1 h5py scikit-learn einops tqdm warmup-scheduler deepspeed tensorboard
# install the pointnet++ library cuda extensions
pip install pointnet2_ops_lib/.
```
### Data Preparation
When you first run the command for training, the datasets will be automatically downloaded and saved in `data/`.
- **ModelNet40** -->`data/modelnet40_ply_hdf5_2048/`
- **ScanObjectNN** -->`data/h5_files/`
Alternatively, you can manually download the official data ([ModelNet40](https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip) | [ScanObjectNN](https://hkust-vgd.github.io/scanobjectnn/)) in any path, and create a symbolic link to your datasets folder.
```
mkdir data
ln -s /path/to/your/data/folder data/
```
## Training
### Step 1: Check config file
You can modify settings in `config/cls.yaml`.
Make sure the `eval` is set to False.
> We support [wandb](https://wandb.ai/site) for collecting results online. Just set `wandb.use_wandb=True` if use wandb.
> Please check the [official wandb doc](https://docs.wandb.ai/) for more details.
### Step 2: Train PointConT
- Classification on ModelNet40
```
python main_cls.py db=modelnet40
```
- Classification on ScanObjectNN
```
python main_cls.py db=scanobjectnn
```
`config/cls.yaml` will be automatically loaded when you run the command.
## Evaluation
To evaluate a trained-model, please set `eval=True` in `config/cls.yaml` and run `python main_cls.py db=${dataset}`
Or you can override values in the loaded config from the command line:
```
python main_cls.py db=${dataset} eval=True
```
## Visualization
### Dependency
- [Mitsuba](https://www.mitsuba-renderer.org/)
Please refer to the following github repository for point cloud rendering code: [PointFlowRenderer](https://github.com/zekunhao1995/PointFlowRenderer)
## Results (pretrained model)
| Dataset | mAcc | OA | Download |
| ------- | ---- | ---- | :----: |
| ModelNet40 | 90.5 | 93.5 | [ckpt](https://drive.google.com/file/d/1ZUrZMnqsnr2Tkqt51Aa9LPmsfQ0P3dmI/view?usp=sharing) \| [log](https://drive.google.com/file/d/14v4mmwN7qXnufIML-_1XmWU14U4eej2w/view?usp=share_link) |
| ScanObjectNN | 86.0 | 88.0 | [ckpt](https://drive.google.com/file/d/1hewKbJoERzaqY261_QQoenZ0Ni9-iYGF/view?usp=sharing) \| [log](https://drive.google.com/file/d/1s-z5quorbZUYHfTNWnevS-CA_jcy9S0F/view?usp=sharing) |
| ScanObjectNN * | 88.5 | 90.3 | [config](config/voting_cls.yaml) \| [log](https://drive.google.com/file/d/1XHQjuHzMBOciWhY7WlM6DsiaiZydrSVR/view?usp=sharing) |
\* denotes method evaluated with voting strategy
## Citation
If you find our work useful in your research, please consider citing:
```
@article{Liu2023PointConT,
author = {Liu, Yahui and Tian, Bin and Lv, Yisheng and Li, Lingxi and Wang, Fei-Yue},
title = {Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space},
journal = {IEEE/CAA Journal of Automatica Sinica},
year={2023},
volume={10},
number={8},
pages={1-9},
doi={10.1109/JAS.2023.123432}
}
```
## Acknowledgement
Our code is mainly based on the following open-source projects. Many thanks to the authors for their wonderful works.
[PointNet2](https://github.com/erikwijmans/Pointnet2_PyTorch), [Point-Transformers](https://github.com/qq456cvb/Point-Transformers), [DGCNN](https://github.com/AnTao97/dgcnn.pytorch),
[CurveNet](https://github.com/tiangexiang/CurveNet),
[PointMLP](https://github.com/ma-xu/pointMLP-pytorch), [PAConv](https://github.com/CVMI-Lab/PAConv), [PointNeXt](https://github.com/guochengqian/pointnext)