chenfengxu714 / YOGO

Official implementation of YOGO for point-cloud processing

You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module (IROS 2021)

By Chenfeng Xu, Bohan Zhai, Bichen Wu, Tian Li, Wei Zhan, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka.

This repository contains a Pytorch implementation of YOGO, a new, simple, and elegant model for point-cloud processing. The framework of our YOGO is shown below:

Selected quantitative results of different approaches on the ShapeNet and S3DIS dataset.

ShapeNet part segmentation:

Method	mIoU	Latency (ms)	GPU Memory (GB)
PointNet	83.7	21.4	1.5
RSNet	84.9	73.8	0.8
PointNet++	85.1	77.7	2.0
DGCNN	85.1	86.7	2.4
PointCNN	86.1	134.2	2.5
YOGO(KNN)	85.2	25.6	0.9
YOGO(Ball query)	85.1	21.3	1.0

S3DIS scene parsing:

Method	mIoU	Latency (ms)	GPU Memory (GB)
PointNet	42.9	24.8	1.0
RSNet	51.9	111.5	1.1
PointNet++*	50.7	501.5	1.6
DGCNN	47.9	174.3	2.4
PointCNN	57.2	282.4	4.6
YOGO(KNN)	54.0	27.7	2.0
YOGO(Ball query)	53.8	24.0	2.0

For more detail, please refer to our paper: YOGO. The work is a follow-up work to SqueezeSegV3 and Visual Transformers. If you find this work useful for your research, please consider citing:

@article{xu2021you,
  title={You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module},
  author={Xu, Chenfeng and Zhai, Bohan and Wu, Bichen and Li, Tian and Zhan, Wei and Vajda, Peter and Keutzer, Kurt and Tomizuka, Masayoshi},
  journal={arXiv preprint arXiv:2103.09975},
  year={2021}
}

Related works:

@inproceedings{xu2020squeezesegv3,
  title={Squeezesegv3: Spatially-adaptive convolution for efficient point-cloud segmentation},
  author={Xu, Chenfeng and Wu, Bichen and Wang, Zining and Zhan, Wei and Vajda, Peter and Keutzer, Kurt and Tomizuka, Masayoshi},
  booktitle={European Conference on Computer Vision},
  pages={1--19},
  year={2020},
  organization={Springer}
}

@misc{wu2020visual,
      title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision}, 
      author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
      year={2020},
      eprint={2006.03677},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

License

YOGO is released under the BSD license (See LICENSE for details).

Installation

The instructions are tested on Ubuntu 16.04 with python 3.6 and Pytorch 1.5 with GPU support.

• Clone the YOGO repository:

git clone https://github.com/chenfengxu714/YOGO.git

• Use pip to install required Python packages:

pip install -r requirements.txt

• Install KNN library:

cd convpoint/knn/
python setup.py install --home='.'

• Click to download ShapeNet and S3DIS dataset.

Prepare the S3DIS dataset

cd data/s3dis
python prepare_data.py

Pre-trained Models

The pre-trained YOGO is avalible at Google Drive, you can directly download them.

Inference

To infer the predictions for the entire dataset:

python train.py [config-file] --devices [gpu-ids] --evaluate --configs.evaluate.best_checkpoint_path [path to the model checkpoint]

for example, you can run the below command for ShapeNet inference:

python train.py configs/shapenet/yogo/yogo.py --devices 0 --evaluate --configs.evaluate.best_checkpoint_path ./runs/shapenet/best.pth

Training:

To train the model:

python train.py [config-file] --devices [gpu-ids] --evaluate --configs.evaluate.best_checkpoint_path [path to the model checkpoint]

for example, you can run the below command for ShapeNet training:

python train.py configs/shapenet/yogo/yogo.py --devices 0

You can run the below command for multi-gpu training:

python train.py configs/shapenet/yogo/yogo.py --devices 0,1,2,3

Note that we conduct training on Titan RTX gpu, you can modify the batch size according your GPU memory, the performance is slightly different.

Acknowledgement:

The code is modified from PVCNN and the code for KNN is from Pointconv.